Combining the principle of antibody concentration with the idea of biological evolution, this paper proposes an adaptive target detection algorithm for cloud service security based on Bio-Inspired Performance Evaluati...Combining the principle of antibody concentration with the idea of biological evolution, this paper proposes an adaptive target detection algorithm for cloud service security based on Bio-Inspired Performance Evaluation Process Algebra(Bio-PEPA). The formal modelling of cloud services is formally modded by Bio-PEPA and the modules are transformed between cloud service internal structures and various components. Then, the security adaptive target detection algorithm of cloud service is divided into two processes, the short-term optimal action selection process which selects the current optimal detective action through the iterative operation of the expected function and the adaptive function, and the long-term detective strategy realized through the updates and eliminations of action planning table. The combination of the two processes reflects the self-adaptability of cloud service system to target detection. The simulating test detects three different kinds of security risks and then analyzes the relationship between the numbers of components with time in the service process. The performance of this method is compared with random detection method and three anomaly detection methods by the cloud service detection experiment. The detection time of this method is 50.1% of three kinds of detection methods and 86.3% of the random detection method. The service success rate is about 15% higher than that of random detection methods. The experimental results show that the algorithm has good time performance and high detection hit rate.展开更多
Quality of Service (QoS) generally refers to measurable like latency and throughput, things that directly affect the user experience. Queuing (the most popular QoS tool) involves choosing the packets to be sent based ...Quality of Service (QoS) generally refers to measurable like latency and throughput, things that directly affect the user experience. Queuing (the most popular QoS tool) involves choosing the packets to be sent based on something other than arrival time. The Active queue management is important subject to manage this queue to increase the effectiveness of Transmission Control Protocol networks. Active queue management (AQM) is an effective means to enhance congestion control, and to achieve trade-off between link utilization and delay. The de facto standard, Random Early Detection (RED), and many of its variants employ queue length as a congestion indicator to trigger packet dropping. One of these enhancements of RED is FRED or Fair Random Early Detection attempts to deal with a fundamental aspect of RED in that it imposes the same loss rate on all flows, regardless of their bandwidths. FRED also uses per-flow active accounting, and tracks the state of active flows. FRED protects fragile flows by deterministically accepting flows from low bandwidth connections and fixes several shortcomings of RED by computing queue length during both arrival and departure of the packet. Unlike FRED, we propose a new scheme that used hazard rate estimated packet dropping function in FRED. We call this new scheme Enhancement Fair Random Early Detection. The key idea is that, with EFRED Scheme change packet dropping function, to get packet dropping less than RED and other AQM algorithms like ARED, REM, RED, etc. Simulations demonstrate that EFRED achieves a more stable throughput and performs better than current active queue management algorithms due to decrease the packets loss percentage and lowest in queuing delay, end to end delay and delay variation (JITTER).展开更多
Impact craters are important for understanding the evolution of lunar geologic and surface erosion rates,among other functions.However,the morphological characteristics of these micro impact craters are not obvious an...Impact craters are important for understanding the evolution of lunar geologic and surface erosion rates,among other functions.However,the morphological characteristics of these micro impact craters are not obvious and they are numerous,resulting in low detection accuracy by deep learning models.Therefore,we proposed a new multi-scale fusion crater detection algorithm(MSF-CDA)based on the YOLO11 to improve the accuracy of lunar impact crater detection,especially for small craters with a diameter of<1 km.Using the images taken by the LROC(Lunar Reconnaissance Orbiter Camera)at the Chang’e-4(CE-4)landing area,we constructed three separate datasets for craters with diameters of 0-70 m,70-140 m,and>140 m.We then trained three submodels separately with these three datasets.Additionally,we designed a slicing-amplifying-slicing strategy to enhance the ability to extract features from small craters.To handle redundant predictions,we proposed a new Non-Maximum Suppression with Area Filtering method to fuse the results in overlapping targets within the multi-scale submodels.Finally,our new MSF-CDA method achieved high detection performance,with the Precision,Recall,and F1 score having values of 0.991,0.987,and 0.989,respectively,perfectly addressing the problems induced by the lesser features and sample imbalance of small craters.Our MSF-CDA can provide strong data support for more in-depth study of the geological evolution of the lunar surface and finer geological age estimations.This strategy can also be used to detect other small objects with lesser features and sample imbalance problems.We detected approximately 500,000 impact craters in an area of approximately 214 km2 around the CE-4 landing area.By statistically analyzing the new data,we updated the distribution function of the number and diameter of impact craters.Finally,we identified the most suitable lighting conditions for detecting impact crater targets by analyzing the effect of different lighting conditions on the detection accuracy.展开更多
Real-time anomaly detection of massive data streams is an important research topic nowadays due to the fact that a lot of data is generated in continuous temporal processes. There is a broad research area, covering ma...Real-time anomaly detection of massive data streams is an important research topic nowadays due to the fact that a lot of data is generated in continuous temporal processes. There is a broad research area, covering mathematical, statistical, information theory methodologies for anomaly detection. It addresses various problems in a lot of domains such as health, education, finance, government, etc. In this paper, we analyze the state-of-the-art of data streams anomaly detection techniques and algorithms for anomaly detection in data streams (time series data). Critically surveying the techniques’ performances under the challenge of real-time anomaly detection of massive high-velocity streams, we conclude that the modeling of the normal behavior of the stream is a suitable approach. We evaluate Holt-Winters (HW), Taylor’s Double Holt-Winters (TDHW), Hierarchical temporal memory (HTM), Moving Average (MA), Autoregressive integrated moving average (ARIMA) forecasting models, etc. Holt-Winters (HW) and Taylor’s Double Holt-Winters (TDHW) forecasting models are used to predict the normal behavior of the periodic streams, and to detect anomalies when the deviations of observed and predicted values exceeded some predefined measures. In this work, we propose an enhancement of this approach and give a short description about the algorithms and then they are categorized by type of pre-diction as: predictive and non-predictive algorithms. We implement the Genetic Algorithm (GA) to periodically optimize HW and TDHW smoothing parameters in addition to the two sliding windows parameters that improve Hyndman’s MASE measure of deviation, and value of the threshold parameter that defines no anomaly confidence interval [1]. We also propose a new optimization function based on the input training datasets with the annotated anomaly intervals, in order to detect the right anomalies and minimize the number of false ones. The proposed method is evaluated on the known anomaly detection benchmarks NUMENTA and Yahoo datasets with annotated anomalies and real log data generated by the National education information system (NEIS)1 in Macedonia.展开更多
Traffic sign detection is an important part of autonomous driving,and its recognition accuracy and speed are directly related to road traffic safety.Although convolutional neural networks(CNNs)have made certain breakt...Traffic sign detection is an important part of autonomous driving,and its recognition accuracy and speed are directly related to road traffic safety.Although convolutional neural networks(CNNs)have made certain breakthroughs in this field,in the face of complex scenes,such as image blur and target occlusion,the traffic sign detection continues to exhibit limited accuracy,accompanied by false positives and missed detections.To address the above problems,a traffic sign detection algorithm,You Only Look Once-based Skip Dynamic Way(YOLO-SDW)based on You Only Look Once version 8 small(YOLOv8s),is proposed.Firstly,a Skip Connection Reconstruction(SCR)module is introduced to efficiently integrate fine-grained feature information and enhance the detection accuracy of the algorithm in complex scenes.Secondly,a C2f module based on Dynamic Snake Convolution(C2f-DySnake)is proposed to dynamically adjust the receptive field information,improve the algorithm’s feature extraction ability for blurred or occluded targets,and reduce the occurrence of false detections and missed detections.Finally,the Wise Powerful IoU v2(WPIoUv2)loss function is proposed to further improve the detection accuracy of the algorithm.Experimental results show that the average precision mAP@0.5 of YOLO-SDW on the TT100K dataset is 89.2%,and mAP@0.5:0.95 is 68.5%,which is 4%and 3.3%higher than the YOLOv8s baseline,respectively.YOLO-SDW ensures real-time performance while having higher accuracy.展开更多
This paper proposes a lightweight traffic sign detection system based on you only look once(YOLO).Firstly,the classification to fusion(C2f)structure is integrated into the backbone network,employing deformable convolu...This paper proposes a lightweight traffic sign detection system based on you only look once(YOLO).Firstly,the classification to fusion(C2f)structure is integrated into the backbone network,employing deformable convolution and bi-directional feature pyramid network(BiFPN)_Concat to improve the adaptability of the network.Secondly,the simple attention module(SimAm)is embedded to prioritize key features and reduce the complexity of the model after the C2f layer at the end of the backbone network.Next,the focal efficient intersection over union(EloU)is introduced to adjust the weights of challenging samples.Finally,we accomplish the design and deployment for the mobile app.The results demonstrate improvements,with the F1 score of 0.8987,mean average precision(mAP)@0.5 of 98.8%,mAP@0.5:0.95 of 75.6%,and the detection speed of 50 frames per second(FPS).展开更多
Aiming at the problems of low detection accuracy and large model size of existing object detection algorithms applied to complex road scenes,an improved you only look once version 8(YOLOv8)object detection algorithm f...Aiming at the problems of low detection accuracy and large model size of existing object detection algorithms applied to complex road scenes,an improved you only look once version 8(YOLOv8)object detection algorithm for infrared images,F-YOLOv8,is proposed.First,a spatial-to-depth network replaces the traditional backbone network's strided convolution or pooling layer.At the same time,it combines with the channel attention mechanism so that the neural network focuses on the channels with large weight values to better extract low-resolution image feature information;then an improved feature pyramid network of lightweight bidirectional feature pyramid network(L-BiFPN)is proposed,which can efficiently fuse features of different scales.In addition,a loss function of insertion of union based on the minimum point distance(MPDIoU)is introduced for bounding box regression,which obtains faster convergence speed and more accurate regression results.Experimental results on the FLIR dataset show that the improved algorithm can accurately detect infrared road targets in real time with 3%and 2.2%enhancement in mean average precision at 50%IoU(mAP50)and mean average precision at 50%—95%IoU(mAP50-95),respectively,and 38.1%,37.3%and 16.9%reduction in the number of model parameters,the model weight,and floating-point operations per second(FLOPs),respectively.To further demonstrate the detection capability of the improved algorithm,it is tested on the public dataset PASCAL VOC,and the results show that F-YOLO has excellent generalized detection performance.展开更多
The snapshot Fault Detection(FD)algorithm of Advanced Receiver Autonomous Integrity Monitoring(ARAIM)necessitates the allocation of continuity and integrity risk requirements from the operational exposure time level t...The snapshot Fault Detection(FD)algorithm of Advanced Receiver Autonomous Integrity Monitoring(ARAIM)necessitates the allocation of continuity and integrity risk requirements from the operational exposure time level to the single epoch level.Current studies primarily focus on finding a conservative Number of Effective Samples(NES)as a risk mapping factor.However,considering that the NES varies with the observation environment and the type of the fault mode,applying a fixed NES can constrain the performance of the algorithm.To address this issue,the continuity and integrity risks over the operational exposure time are analyzed and bounded based on all epochs within the exposure time.A more adaptable method for continuity and integrity budget allocation over the operational exposure time is presented,capable of monitoring the continuity and integrity risks over the recent operational exposure time in real time,and dynamically adjusting the allocation values based on the current observation environment.Simulation results demonstrate that,compared with the allocation method based on a fixed NES,ARAIM based on the proposed allocation method exhibits superior performance in terms of the availability.At an FD execution frequency equal to the required Time-To-Alert(TTA),the dual-constellation H-ARAIM provides 100%of the global coverage with 99.5%availability of the RNP 0.1 service,and the dual-constellation V-ARAIM provides 86.38%of the global coverage with 99.5%availability of the LPV-200 service.展开更多
UAV-based object detection is rapidly expanding in both civilian and military applications,including security surveillance,disaster assessment,and border patrol.However,challenges such as small objects,occlusions,comp...UAV-based object detection is rapidly expanding in both civilian and military applications,including security surveillance,disaster assessment,and border patrol.However,challenges such as small objects,occlusions,complex backgrounds,and variable lighting persist due to the unique perspective of UAV imagery.To address these issues,this paper introduces DAFPN-YOLO,an innovative model based on YOLOv8s(You Only Look Once version 8s).Themodel strikes a balance between detection accuracy and speed while reducing parameters,making itwell-suited for multi-object detection tasks from drone perspectives.A key feature of DAFPN-YOLO is the enhanced Drone-AFPN(Adaptive Feature Pyramid Network),which adaptively fuses multi-scale features to optimize feature extraction and enhance spatial and small-object information.To leverage Drone-AFPN’smulti-scale capabilities fully,a dedicated 160×160 small-object detection head was added,significantly boosting detection accuracy for small targets.In the backbone,the C2f_Dual(Cross Stage Partial with Cross-Stage Feature Fusion Dual)module and SPPELAN(Spatial Pyramid Pooling with Enhanced LocalAttentionNetwork)modulewere integrated.These components improve feature extraction and information aggregationwhile reducing parameters and computational complexity,enhancing inference efficiency.Additionally,Shape-IoU(Shape Intersection over Union)is used as the loss function for bounding box regression,enabling more precise shape-based object matching.Experimental results on the VisDrone 2019 dataset demonstrate the effectiveness ofDAFPN-YOLO.Compared to YOLOv8s,the proposedmodel achieves a 5.4 percentage point increase inmAP@0.5,a 3.8 percentage point improvement in mAP@0.5:0.95,and a 17.2%reduction in parameter count.These results highlight DAFPN-YOLO’s advantages in UAV-based object detection,offering valuable insights for applying deep learning to UAV-specific multi-object detection tasks.展开更多
With the rapid urbanization and exponential population growth in China,two-wheeled vehicles have become a popular mode of transportation,particularly for short-distance travel.However,due to a lack of safety awareness...With the rapid urbanization and exponential population growth in China,two-wheeled vehicles have become a popular mode of transportation,particularly for short-distance travel.However,due to a lack of safety awareness,traffic violations by two-wheeled vehicle riders have become a widespread concern,contributing to urban traffic risks.Currently,significant human and material resources are being allocated to monitor and intercept non-compliant riders to ensure safe driving behavior.To enhance the safety,efficiency,and cost-effectiveness of traffic monitoring,automated detection systems based on image processing algorithms can be employed to identify traffic violations from eye-level video footage.In this study,we propose a robust detection algorithm specifically designed for two-wheeled vehicles,which serves as a fundamental step toward intelligent traffic monitoring.Our approach integrates a novel convolutional and attention mechanism to improve detection accuracy and efficiency.Additionally,we introduce a semi-supervised training strategy that leverages a large number of unlabeled images to enhance the model’s learning capability by extracting valuable background information.This method enables the model to generalize effectively to diverse urban environments and varying lighting conditions.We evaluate our proposed algorithm on a custom-built dataset,and experimental results demonstrate its superior performance,achieving an average precision(AP)of 95%and a recall(R)of 90.6%.Furthermore,the model maintains a computational efficiency of only 25.7 GFLOPs while achieving a high processing speed of 249 FPS,making it highly suitable for deployment on edge devices.Compared to existing detection methods,our approach significantly enhances the accuracy and robustness of two-wheeled vehicle identification while ensuring real-time performance.展开更多
Accurate and real-time fire detection is crucial for industrial production and daily life.However,the variable form of fire and the significant differences in visual characteristics across its different stages pose gr...Accurate and real-time fire detection is crucial for industrial production and daily life.However,the variable form of fire and the significant differences in visual characteristics across its different stages pose great challenges to precise fire prevention and control.To address this issue,a multi-scale fire target detection algorithm using YOLO-fire was proposed by improving the YOLOv8 model.This model introduced new layer structures and attention mechanism,replaced new feature fusion modules and loss functions.By introducing a small-target detection P2 layer,the model’s ability to detect early-stage fires is improved.The coordinate attention mechanism is integrated into the layer structures of multi-scale target detection,enhancing the capture of target location information and channel relationships,thereby focusing more on the target regions.The Neck network structure was optimized by adopting a BiFPN_F strategy for different feature layers,which strengthened the cross-scale representation of fire features and controlled the parameter count of the designed model.The WIoU loss function was employed to optimize the regression process,improving fire source localization accuracy in complex scenarios,enhancing model robustness,and increasing detection precision.Experimental results on fire datasets demonstrated that YOLO-fire could effectively detect multi-scale fire targets in various scenarios.Compared to the baseline model(YOLOv8n),YOLO-fire achieves improvements of 1.37%in accuracy,1.25%in mAP50-95,and 0.35%in F1-score,while reducing parameters by 3.79%.Furthermore,compared to current mainstream target detection algorithms,YOLO-Fire achieved optimal detection performance while reducing network parameters and computational complexity.This research provided effective technical support for fire safety prevention and control in related fields.展开更多
Brain tumors are neoplastic diseases caused by the proliferation of abnormal cells in brain tissues,and their appearance may lead to a series of complex symptoms.However,current methods struggle to capture deeper brai...Brain tumors are neoplastic diseases caused by the proliferation of abnormal cells in brain tissues,and their appearance may lead to a series of complex symptoms.However,current methods struggle to capture deeper brain tumor image feature information due to the variations in brain tumor morphology,size,and complex background,resulting in low detection accuracy,high rate of misdiagnosis and underdiagnosis,and challenges in meeting clinical needs.Therefore,this paper proposes the CMS-YOLO network model for multi-category brain tumor detection,which is based on the You Only Look Once version 10(YOLOv10s)algorithm.This model innovatively integrates the Convolutional Medical UNet extended block(CMUNeXt Block)to design a brand-new CSP Bottleneck with 2 convolutions(C2f)structure,which significantly enhances the ability to extract features of the lesion area.Meanwhile,to address the challenge of complex backgrounds in brain tumor detection,a Multi-Scale Attention Aggregation(MSAA)module is introduced.The module integrates features of lesions at different scales,enabling the model to effectively capture multi-scale contextual information and enhance detection accuracy in complex scenarios.Finally,during the model training process,the Shape-IoU loss function is employed to replace the Complete-IoU(CIoU)loss function for optimizing bounding box regression.This ensures that the predicted bounding boxes generated by the model closely match the actual tumor contours,thereby further enhancing the detection precision.The experimental results show that the improved method achieves 94.80%precision,93.60%recall,96.20%score,and 79.60%on the MRI for Brain Tumor with Bounding Boxes dataset.Compared to the YOLOv10s model,this represents improvements of 1.0%,1.1%,1.0%,and 1.1%,respectively.The method can achieve automatic detection and localization of three distinct categories of brain tumors—glioma,meningioma,and pituitary tumor,which can accurately detect and identify brain tumors,assist doctors in early diagnosis,and promote the development of early treatment.展开更多
Collision detection mechanisms in Wireless Sensor Networks (WSNs) have largely been revolving around direct demodulation and decoding of received packets and deciding on a collision based on some form of a frame error...Collision detection mechanisms in Wireless Sensor Networks (WSNs) have largely been revolving around direct demodulation and decoding of received packets and deciding on a collision based on some form of a frame error detection mechanism, such as a CRC check. The obvious drawback of full detection of a received packet is the need to expend a significant amount of energy and processing complexity in order to fully decode a packet, only to discover the packet is illegible due to a collision. In this paper, we propose a suite of novel, yet simple and power-efficient algorithms to detect a collision without the need for full-decoding of the received packet. Our novel algorithms aim at detecting collision through fast examination of the signal statistics of a short snippet of the received packet via a relatively small number of computations over a small number of received IQ samples. Hence, the proposed algorithms operate directly at the output of the receiver's analog-to-digital converter and eliminate the need to pass the signal through the entire. In addition, we present a complexity and power-saving comparison between our novel algorithms and conventional full-decoding (for select coding schemes) to demonstrate the significant power and complexity saving advantage of our algorithms.展开更多
With the great development of unmanned aircraft system(UAS)over the last decade,sense and avoid(SAA)system has been a crucial technology for integrating unmanned aircraft vehicle(UAV)into national airspace with reliab...With the great development of unmanned aircraft system(UAS)over the last decade,sense and avoid(SAA)system has been a crucial technology for integrating unmanned aircraft vehicle(UAV)into national airspace with reliable and safe operations.This paper mainly focuses on intruder detection for SAA system.A robust algorithm based on the combination of edge-boxes and spatial pyramid matching using sparse coding(sc-SPM)is presented.The algorithm is composed of three stages.First,edge-boxes method is adopted to obtain a large number of proposals;Second,the optimization program is presented to obtain intruder area-of-interest(ROI)regions;Third,sc-SPM is employed for feature representation of ROI regions and support vector machines(SVM)is adopted to detect the intruder.The algorithm is evaluated under different weather conditions.The recall reaches to 0.95 in dawn and sunny weather and 0.9 in cloudy weather.The experimental results indicate that the intruder detection algorithm is effective and robust with various weather under complex background.展开更多
In order to improve the accuracy and consistency of data in health monitoring system,an anomaly detection algorithm for stay cables based on data fusion is proposed.The monitoring data of Nanjing No.3 Yangtze River Br...In order to improve the accuracy and consistency of data in health monitoring system,an anomaly detection algorithm for stay cables based on data fusion is proposed.The monitoring data of Nanjing No.3 Yangtze River Bridge is used as the basis of study.Firstly,an adaptive processing framework with feedback control is established based on the concept of data fusion.The data processing contains four steps:data specification,data cleaning,data conversion and data fusion.Data processing information offers feedback to the original data system,which further gives guidance for the sensor maintenance or replacement.Subsequently,the algorithm steps based on the continuous data distortion is investigated,which integrates the inspection data and the distribution test method.Finally,a group of cable force data is utilized as an example to verify the established framework and algorithm.Experimental results show that the proposed algorithm can achieve high detection accuracy,providing a valuable reference for other monitoring data processing.展开更多
Pulmonary nodules represent an early manifestation of lung cancer.However,pulmonary nodules only constitute a small portion of the overall image,posing challenges for physicians in image interpretation and potentially...Pulmonary nodules represent an early manifestation of lung cancer.However,pulmonary nodules only constitute a small portion of the overall image,posing challenges for physicians in image interpretation and potentially leading to false positives or missed detections.To solve these problems,the YOLOv8 network is enhanced by adding deformable convolution and atrous spatial pyramid pooling(ASPP),along with the integration of a coordinate attention(CA)mechanism.This allows the network to focus on small targets while expanding the receptive field without losing resolution.At the same time,context information on the target is gathered and feature expression is enhanced by attention modules in different directions.It effectively improves the positioning accuracy and achieves good results on the LUNA16 dataset.Compared with other detection algorithms,it improves the accuracy of pulmonary nodule detection to a certain extent.展开更多
Load time series analysis is critical for resource management and optimization decisions,especially automated analysis techniques.Existing research has insufficiently interpreted the overall characteristics of samples...Load time series analysis is critical for resource management and optimization decisions,especially automated analysis techniques.Existing research has insufficiently interpreted the overall characteristics of samples,leading to significant differences in load level detection conclusions for samples with different characteristics(trend,seasonality,cyclicality).Achieving automated,feature-adaptive,and quantifiable analysis methods remains a challenge.This paper proposes a Threshold Recognition-based Load Level Detection Algorithm(TRLLD),which effectively identifies different load level regions in samples of arbitrary size and distribution type based on sample characteristics.By utilizing distribution density uniformity,the algorithm classifies data points and ultimately obtains normalized load values.In the feature recognition step,the algorithm employs the Density Uniformity Index Based on Differences(DUID),High Load Level Concentration(HLLC),and Low Load Level Concentration(LLLC)to assess sample characteristics,which are independent of specific load values,providing a standardized perspective on features,ensuring high efficiency and strong interpretability.Compared to traditional methods,the proposed approach demonstrates better adaptive and real-time analysis capabilities.Experimental results indicate that it can effectively identify high load and low load regions in 16 groups of time series samples with different load characteristics,yielding highly interpretable results.The correlation between the DUID and sample density distribution uniformity reaches 98.08%.When introducing 10% MAD intensity noise,the maximum relative error is 4.72%,showcasing high robustness.Notably,it exhibits significant advantages in general and low sample scenarios.展开更多
The paper discusses an application for rail track thermal image fault detection. In order to get better results from the Canny edge detection algorithm, the image needs to be processed in advance. The histogram equali...The paper discusses an application for rail track thermal image fault detection. In order to get better results from the Canny edge detection algorithm, the image needs to be processed in advance. The histogram equalization method is proposed to enhance the contrast of the image. Since a thermal image contains multiple parallel rail tracks, an algorithm has been developed to locate and separate the tracks that we are interested in. This is accomplished by applying the least squares linear fitting technique to represent the surface of a track. The performance of the application is evaluated by using a number of images provided by a specialised company and the results are essentially favourable.展开更多
Spectrum sensing is one of the most important steps in cognitive radio. In this paper, a new fully-distributed collaborative energy detection algorithm based on diffusion cooperation scheme and consensus filtering the...Spectrum sensing is one of the most important steps in cognitive radio. In this paper, a new fully-distributed collaborative energy detection algorithm based on diffusion cooperation scheme and consensus filtering theory is proposed, which doesn’t need the center node to fuse the detection results of all users. The secondary users only exchange information with their neighbors to obtain the detection data, and then make the corresponding decisions independently according to the pre-defined threshold. Simulations show that the proposed algorithm is more superior to the existing centralized collaborative energy detection algorithm in terms of the detecting performance and robustness in the insecurity situation.展开更多
To maintain the reliability of power systems,routine inspections using drones equipped with advanced object detection algorithms are essential for preempting power-related issues.The increasing resolution of drone-cap...To maintain the reliability of power systems,routine inspections using drones equipped with advanced object detection algorithms are essential for preempting power-related issues.The increasing resolution of drone-captured images has posed a challenge for traditional target detection methods,especially in identifying small objects in high-resolution images.This study presents an enhanced object detection algorithm based on the Faster Regionbased Convolutional Neural Network(Faster R-CNN)framework,specifically tailored for detecting small-scale electrical components like insulators,shock hammers,and screws in transmission line.The algorithm features an improved backbone network for Faster R-CNN,which significantly boosts the feature extraction network’s ability to detect fine details.The Region Proposal Network is optimized using a method of guided feature refinement(GFR),which achieves a balance between accuracy and speed.The incorporation of Generalized Intersection over Union(GIOU)and Region of Interest(ROI)Align further refines themodel’s accuracy.Experimental results demonstrate a notable improvement in mean Average Precision,reaching 89.3%,an 11.1%increase compared to the standard Faster R-CNN.This highlights the effectiveness of the proposed algorithm in identifying electrical components in high-resolution aerial images.展开更多
基金Supported by the National Natural Science Foundation of China(61202458,61403109)the Natural Science Foundation of Heilongjiang Province of China(F2017021)and the Harbin Science and Technology Innovation Research Funds(2016RAQXJ036)
文摘Combining the principle of antibody concentration with the idea of biological evolution, this paper proposes an adaptive target detection algorithm for cloud service security based on Bio-Inspired Performance Evaluation Process Algebra(Bio-PEPA). The formal modelling of cloud services is formally modded by Bio-PEPA and the modules are transformed between cloud service internal structures and various components. Then, the security adaptive target detection algorithm of cloud service is divided into two processes, the short-term optimal action selection process which selects the current optimal detective action through the iterative operation of the expected function and the adaptive function, and the long-term detective strategy realized through the updates and eliminations of action planning table. The combination of the two processes reflects the self-adaptability of cloud service system to target detection. The simulating test detects three different kinds of security risks and then analyzes the relationship between the numbers of components with time in the service process. The performance of this method is compared with random detection method and three anomaly detection methods by the cloud service detection experiment. The detection time of this method is 50.1% of three kinds of detection methods and 86.3% of the random detection method. The service success rate is about 15% higher than that of random detection methods. The experimental results show that the algorithm has good time performance and high detection hit rate.
文摘Quality of Service (QoS) generally refers to measurable like latency and throughput, things that directly affect the user experience. Queuing (the most popular QoS tool) involves choosing the packets to be sent based on something other than arrival time. The Active queue management is important subject to manage this queue to increase the effectiveness of Transmission Control Protocol networks. Active queue management (AQM) is an effective means to enhance congestion control, and to achieve trade-off between link utilization and delay. The de facto standard, Random Early Detection (RED), and many of its variants employ queue length as a congestion indicator to trigger packet dropping. One of these enhancements of RED is FRED or Fair Random Early Detection attempts to deal with a fundamental aspect of RED in that it imposes the same loss rate on all flows, regardless of their bandwidths. FRED also uses per-flow active accounting, and tracks the state of active flows. FRED protects fragile flows by deterministically accepting flows from low bandwidth connections and fixes several shortcomings of RED by computing queue length during both arrival and departure of the packet. Unlike FRED, we propose a new scheme that used hazard rate estimated packet dropping function in FRED. We call this new scheme Enhancement Fair Random Early Detection. The key idea is that, with EFRED Scheme change packet dropping function, to get packet dropping less than RED and other AQM algorithms like ARED, REM, RED, etc. Simulations demonstrate that EFRED achieves a more stable throughput and performs better than current active queue management algorithms due to decrease the packets loss percentage and lowest in queuing delay, end to end delay and delay variation (JITTER).
基金the National Key Research and Development Program of China(Grant No.2022YFF0711400)which provided valuable financial support and resources for my research and made it possible for me to deeply explore the unknown mysteries in the field of lunar geologythe National Space Science Data Center Youth Open Project(Grant No.NSSDC2302001),which has not only facilitated the smooth progress of my research,but has also built a platform for me to communicate and cooperate with experts in the field.
文摘Impact craters are important for understanding the evolution of lunar geologic and surface erosion rates,among other functions.However,the morphological characteristics of these micro impact craters are not obvious and they are numerous,resulting in low detection accuracy by deep learning models.Therefore,we proposed a new multi-scale fusion crater detection algorithm(MSF-CDA)based on the YOLO11 to improve the accuracy of lunar impact crater detection,especially for small craters with a diameter of<1 km.Using the images taken by the LROC(Lunar Reconnaissance Orbiter Camera)at the Chang’e-4(CE-4)landing area,we constructed three separate datasets for craters with diameters of 0-70 m,70-140 m,and>140 m.We then trained three submodels separately with these three datasets.Additionally,we designed a slicing-amplifying-slicing strategy to enhance the ability to extract features from small craters.To handle redundant predictions,we proposed a new Non-Maximum Suppression with Area Filtering method to fuse the results in overlapping targets within the multi-scale submodels.Finally,our new MSF-CDA method achieved high detection performance,with the Precision,Recall,and F1 score having values of 0.991,0.987,and 0.989,respectively,perfectly addressing the problems induced by the lesser features and sample imbalance of small craters.Our MSF-CDA can provide strong data support for more in-depth study of the geological evolution of the lunar surface and finer geological age estimations.This strategy can also be used to detect other small objects with lesser features and sample imbalance problems.We detected approximately 500,000 impact craters in an area of approximately 214 km2 around the CE-4 landing area.By statistically analyzing the new data,we updated the distribution function of the number and diameter of impact craters.Finally,we identified the most suitable lighting conditions for detecting impact crater targets by analyzing the effect of different lighting conditions on the detection accuracy.
文摘Real-time anomaly detection of massive data streams is an important research topic nowadays due to the fact that a lot of data is generated in continuous temporal processes. There is a broad research area, covering mathematical, statistical, information theory methodologies for anomaly detection. It addresses various problems in a lot of domains such as health, education, finance, government, etc. In this paper, we analyze the state-of-the-art of data streams anomaly detection techniques and algorithms for anomaly detection in data streams (time series data). Critically surveying the techniques’ performances under the challenge of real-time anomaly detection of massive high-velocity streams, we conclude that the modeling of the normal behavior of the stream is a suitable approach. We evaluate Holt-Winters (HW), Taylor’s Double Holt-Winters (TDHW), Hierarchical temporal memory (HTM), Moving Average (MA), Autoregressive integrated moving average (ARIMA) forecasting models, etc. Holt-Winters (HW) and Taylor’s Double Holt-Winters (TDHW) forecasting models are used to predict the normal behavior of the periodic streams, and to detect anomalies when the deviations of observed and predicted values exceeded some predefined measures. In this work, we propose an enhancement of this approach and give a short description about the algorithms and then they are categorized by type of pre-diction as: predictive and non-predictive algorithms. We implement the Genetic Algorithm (GA) to periodically optimize HW and TDHW smoothing parameters in addition to the two sliding windows parameters that improve Hyndman’s MASE measure of deviation, and value of the threshold parameter that defines no anomaly confidence interval [1]. We also propose a new optimization function based on the input training datasets with the annotated anomaly intervals, in order to detect the right anomalies and minimize the number of false ones. The proposed method is evaluated on the known anomaly detection benchmarks NUMENTA and Yahoo datasets with annotated anomalies and real log data generated by the National education information system (NEIS)1 in Macedonia.
基金funded by Key research and development Program of Henan Province(No.251111211200)National Natural Science Foundation of China(Grant No.U2004163).
文摘Traffic sign detection is an important part of autonomous driving,and its recognition accuracy and speed are directly related to road traffic safety.Although convolutional neural networks(CNNs)have made certain breakthroughs in this field,in the face of complex scenes,such as image blur and target occlusion,the traffic sign detection continues to exhibit limited accuracy,accompanied by false positives and missed detections.To address the above problems,a traffic sign detection algorithm,You Only Look Once-based Skip Dynamic Way(YOLO-SDW)based on You Only Look Once version 8 small(YOLOv8s),is proposed.Firstly,a Skip Connection Reconstruction(SCR)module is introduced to efficiently integrate fine-grained feature information and enhance the detection accuracy of the algorithm in complex scenes.Secondly,a C2f module based on Dynamic Snake Convolution(C2f-DySnake)is proposed to dynamically adjust the receptive field information,improve the algorithm’s feature extraction ability for blurred or occluded targets,and reduce the occurrence of false detections and missed detections.Finally,the Wise Powerful IoU v2(WPIoUv2)loss function is proposed to further improve the detection accuracy of the algorithm.Experimental results show that the average precision mAP@0.5 of YOLO-SDW on the TT100K dataset is 89.2%,and mAP@0.5:0.95 is 68.5%,which is 4%and 3.3%higher than the YOLOv8s baseline,respectively.YOLO-SDW ensures real-time performance while having higher accuracy.
基金supported by the Shanxi Agricultural University Science and Technology Innovation Enhancement Project。
文摘This paper proposes a lightweight traffic sign detection system based on you only look once(YOLO).Firstly,the classification to fusion(C2f)structure is integrated into the backbone network,employing deformable convolution and bi-directional feature pyramid network(BiFPN)_Concat to improve the adaptability of the network.Secondly,the simple attention module(SimAm)is embedded to prioritize key features and reduce the complexity of the model after the C2f layer at the end of the backbone network.Next,the focal efficient intersection over union(EloU)is introduced to adjust the weights of challenging samples.Finally,we accomplish the design and deployment for the mobile app.The results demonstrate improvements,with the F1 score of 0.8987,mean average precision(mAP)@0.5 of 98.8%,mAP@0.5:0.95 of 75.6%,and the detection speed of 50 frames per second(FPS).
基金supported by the National Natural Science Foundation of China(No.62103298)。
文摘Aiming at the problems of low detection accuracy and large model size of existing object detection algorithms applied to complex road scenes,an improved you only look once version 8(YOLOv8)object detection algorithm for infrared images,F-YOLOv8,is proposed.First,a spatial-to-depth network replaces the traditional backbone network's strided convolution or pooling layer.At the same time,it combines with the channel attention mechanism so that the neural network focuses on the channels with large weight values to better extract low-resolution image feature information;then an improved feature pyramid network of lightweight bidirectional feature pyramid network(L-BiFPN)is proposed,which can efficiently fuse features of different scales.In addition,a loss function of insertion of union based on the minimum point distance(MPDIoU)is introduced for bounding box regression,which obtains faster convergence speed and more accurate regression results.Experimental results on the FLIR dataset show that the improved algorithm can accurately detect infrared road targets in real time with 3%and 2.2%enhancement in mean average precision at 50%IoU(mAP50)and mean average precision at 50%—95%IoU(mAP50-95),respectively,and 38.1%,37.3%and 16.9%reduction in the number of model parameters,the model weight,and floating-point operations per second(FLOPs),respectively.To further demonstrate the detection capability of the improved algorithm,it is tested on the public dataset PASCAL VOC,and the results show that F-YOLO has excellent generalized detection performance.
基金supported by the National Key Research and Development Program of China(No.2023YFB4302804)the National Natural Science Foundation of China(Nos.U2233217,62371029,62471023,62301016,and 62101015)。
文摘The snapshot Fault Detection(FD)algorithm of Advanced Receiver Autonomous Integrity Monitoring(ARAIM)necessitates the allocation of continuity and integrity risk requirements from the operational exposure time level to the single epoch level.Current studies primarily focus on finding a conservative Number of Effective Samples(NES)as a risk mapping factor.However,considering that the NES varies with the observation environment and the type of the fault mode,applying a fixed NES can constrain the performance of the algorithm.To address this issue,the continuity and integrity risks over the operational exposure time are analyzed and bounded based on all epochs within the exposure time.A more adaptable method for continuity and integrity budget allocation over the operational exposure time is presented,capable of monitoring the continuity and integrity risks over the recent operational exposure time in real time,and dynamically adjusting the allocation values based on the current observation environment.Simulation results demonstrate that,compared with the allocation method based on a fixed NES,ARAIM based on the proposed allocation method exhibits superior performance in terms of the availability.At an FD execution frequency equal to the required Time-To-Alert(TTA),the dual-constellation H-ARAIM provides 100%of the global coverage with 99.5%availability of the RNP 0.1 service,and the dual-constellation V-ARAIM provides 86.38%of the global coverage with 99.5%availability of the LPV-200 service.
基金supported by the National Natural Science Foundation of China(Grant Nos.62101275 and 62101274).
文摘UAV-based object detection is rapidly expanding in both civilian and military applications,including security surveillance,disaster assessment,and border patrol.However,challenges such as small objects,occlusions,complex backgrounds,and variable lighting persist due to the unique perspective of UAV imagery.To address these issues,this paper introduces DAFPN-YOLO,an innovative model based on YOLOv8s(You Only Look Once version 8s).Themodel strikes a balance between detection accuracy and speed while reducing parameters,making itwell-suited for multi-object detection tasks from drone perspectives.A key feature of DAFPN-YOLO is the enhanced Drone-AFPN(Adaptive Feature Pyramid Network),which adaptively fuses multi-scale features to optimize feature extraction and enhance spatial and small-object information.To leverage Drone-AFPN’smulti-scale capabilities fully,a dedicated 160×160 small-object detection head was added,significantly boosting detection accuracy for small targets.In the backbone,the C2f_Dual(Cross Stage Partial with Cross-Stage Feature Fusion Dual)module and SPPELAN(Spatial Pyramid Pooling with Enhanced LocalAttentionNetwork)modulewere integrated.These components improve feature extraction and information aggregationwhile reducing parameters and computational complexity,enhancing inference efficiency.Additionally,Shape-IoU(Shape Intersection over Union)is used as the loss function for bounding box regression,enabling more precise shape-based object matching.Experimental results on the VisDrone 2019 dataset demonstrate the effectiveness ofDAFPN-YOLO.Compared to YOLOv8s,the proposedmodel achieves a 5.4 percentage point increase inmAP@0.5,a 3.8 percentage point improvement in mAP@0.5:0.95,and a 17.2%reduction in parameter count.These results highlight DAFPN-YOLO’s advantages in UAV-based object detection,offering valuable insights for applying deep learning to UAV-specific multi-object detection tasks.
基金supported by the Natural Science Foundation Project of Fujian Province,China(Grant No.2023J011439 and No.2019J01859).
文摘With the rapid urbanization and exponential population growth in China,two-wheeled vehicles have become a popular mode of transportation,particularly for short-distance travel.However,due to a lack of safety awareness,traffic violations by two-wheeled vehicle riders have become a widespread concern,contributing to urban traffic risks.Currently,significant human and material resources are being allocated to monitor and intercept non-compliant riders to ensure safe driving behavior.To enhance the safety,efficiency,and cost-effectiveness of traffic monitoring,automated detection systems based on image processing algorithms can be employed to identify traffic violations from eye-level video footage.In this study,we propose a robust detection algorithm specifically designed for two-wheeled vehicles,which serves as a fundamental step toward intelligent traffic monitoring.Our approach integrates a novel convolutional and attention mechanism to improve detection accuracy and efficiency.Additionally,we introduce a semi-supervised training strategy that leverages a large number of unlabeled images to enhance the model’s learning capability by extracting valuable background information.This method enables the model to generalize effectively to diverse urban environments and varying lighting conditions.We evaluate our proposed algorithm on a custom-built dataset,and experimental results demonstrate its superior performance,achieving an average precision(AP)of 95%and a recall(R)of 90.6%.Furthermore,the model maintains a computational efficiency of only 25.7 GFLOPs while achieving a high processing speed of 249 FPS,making it highly suitable for deployment on edge devices.Compared to existing detection methods,our approach significantly enhances the accuracy and robustness of two-wheeled vehicle identification while ensuring real-time performance.
基金supported by Natural Science Foundation of Inner Mongolia Autonomous Region,China(No.2023QN05023)the Key R&D and Achievement Transformation Programs of Inner Mongolia Autonomous Region,China(Nos.2025KYPT0051,2025KYPT0050).
文摘Accurate and real-time fire detection is crucial for industrial production and daily life.However,the variable form of fire and the significant differences in visual characteristics across its different stages pose great challenges to precise fire prevention and control.To address this issue,a multi-scale fire target detection algorithm using YOLO-fire was proposed by improving the YOLOv8 model.This model introduced new layer structures and attention mechanism,replaced new feature fusion modules and loss functions.By introducing a small-target detection P2 layer,the model’s ability to detect early-stage fires is improved.The coordinate attention mechanism is integrated into the layer structures of multi-scale target detection,enhancing the capture of target location information and channel relationships,thereby focusing more on the target regions.The Neck network structure was optimized by adopting a BiFPN_F strategy for different feature layers,which strengthened the cross-scale representation of fire features and controlled the parameter count of the designed model.The WIoU loss function was employed to optimize the regression process,improving fire source localization accuracy in complex scenarios,enhancing model robustness,and increasing detection precision.Experimental results on fire datasets demonstrated that YOLO-fire could effectively detect multi-scale fire targets in various scenarios.Compared to the baseline model(YOLOv8n),YOLO-fire achieves improvements of 1.37%in accuracy,1.25%in mAP50-95,and 0.35%in F1-score,while reducing parameters by 3.79%.Furthermore,compared to current mainstream target detection algorithms,YOLO-Fire achieved optimal detection performance while reducing network parameters and computational complexity.This research provided effective technical support for fire safety prevention and control in related fields.
基金supported in part by the National Natural Science Foundation of China under Grants 61861007in part by the Guizhou Province Science and Technology Planning Project ZK[2021]303in part by the Guizhou Province Science Technology Support Plan under Grants[2022]264,[2023]096,[2023]412 and[2023]409.
文摘Brain tumors are neoplastic diseases caused by the proliferation of abnormal cells in brain tissues,and their appearance may lead to a series of complex symptoms.However,current methods struggle to capture deeper brain tumor image feature information due to the variations in brain tumor morphology,size,and complex background,resulting in low detection accuracy,high rate of misdiagnosis and underdiagnosis,and challenges in meeting clinical needs.Therefore,this paper proposes the CMS-YOLO network model for multi-category brain tumor detection,which is based on the You Only Look Once version 10(YOLOv10s)algorithm.This model innovatively integrates the Convolutional Medical UNet extended block(CMUNeXt Block)to design a brand-new CSP Bottleneck with 2 convolutions(C2f)structure,which significantly enhances the ability to extract features of the lesion area.Meanwhile,to address the challenge of complex backgrounds in brain tumor detection,a Multi-Scale Attention Aggregation(MSAA)module is introduced.The module integrates features of lesions at different scales,enabling the model to effectively capture multi-scale contextual information and enhance detection accuracy in complex scenarios.Finally,during the model training process,the Shape-IoU loss function is employed to replace the Complete-IoU(CIoU)loss function for optimizing bounding box regression.This ensures that the predicted bounding boxes generated by the model closely match the actual tumor contours,thereby further enhancing the detection precision.The experimental results show that the improved method achieves 94.80%precision,93.60%recall,96.20%score,and 79.60%on the MRI for Brain Tumor with Bounding Boxes dataset.Compared to the YOLOv10s model,this represents improvements of 1.0%,1.1%,1.0%,and 1.1%,respectively.The method can achieve automatic detection and localization of three distinct categories of brain tumors—glioma,meningioma,and pituitary tumor,which can accurately detect and identify brain tumors,assist doctors in early diagnosis,and promote the development of early treatment.
文摘Collision detection mechanisms in Wireless Sensor Networks (WSNs) have largely been revolving around direct demodulation and decoding of received packets and deciding on a collision based on some form of a frame error detection mechanism, such as a CRC check. The obvious drawback of full detection of a received packet is the need to expend a significant amount of energy and processing complexity in order to fully decode a packet, only to discover the packet is illegible due to a collision. In this paper, we propose a suite of novel, yet simple and power-efficient algorithms to detect a collision without the need for full-decoding of the received packet. Our novel algorithms aim at detecting collision through fast examination of the signal statistics of a short snippet of the received packet via a relatively small number of computations over a small number of received IQ samples. Hence, the proposed algorithms operate directly at the output of the receiver's analog-to-digital converter and eliminate the need to pass the signal through the entire. In addition, we present a complexity and power-saving comparison between our novel algorithms and conventional full-decoding (for select coding schemes) to demonstrate the significant power and complexity saving advantage of our algorithms.
基金supported by the National Natural Science Foundation of China (Nos. 61673211, U1633105)the Fundamental Research Funds for the Central Universities of China (No. NP2019105)the Funding of Jiangsu Innovation Program for Graduation Education, Funding for Outstanding Doctoral Dissertation in NUAA (No.BCXJ18-11)
文摘With the great development of unmanned aircraft system(UAS)over the last decade,sense and avoid(SAA)system has been a crucial technology for integrating unmanned aircraft vehicle(UAV)into national airspace with reliable and safe operations.This paper mainly focuses on intruder detection for SAA system.A robust algorithm based on the combination of edge-boxes and spatial pyramid matching using sparse coding(sc-SPM)is presented.The algorithm is composed of three stages.First,edge-boxes method is adopted to obtain a large number of proposals;Second,the optimization program is presented to obtain intruder area-of-interest(ROI)regions;Third,sc-SPM is employed for feature representation of ROI regions and support vector machines(SVM)is adopted to detect the intruder.The algorithm is evaluated under different weather conditions.The recall reaches to 0.95 in dawn and sunny weather and 0.9 in cloudy weather.The experimental results indicate that the intruder detection algorithm is effective and robust with various weather under complex background.
基金Sponsored by the National Natural Science Foundation of China(Grant No.51208096)Major Scientific and Technological Special Project of Jiangsu Provincial Communications Department(Grant No.2014Y02)the Project of Jiangsu Provincial Communications Department(Grant No.2012Y25)
文摘In order to improve the accuracy and consistency of data in health monitoring system,an anomaly detection algorithm for stay cables based on data fusion is proposed.The monitoring data of Nanjing No.3 Yangtze River Bridge is used as the basis of study.Firstly,an adaptive processing framework with feedback control is established based on the concept of data fusion.The data processing contains four steps:data specification,data cleaning,data conversion and data fusion.Data processing information offers feedback to the original data system,which further gives guidance for the sensor maintenance or replacement.Subsequently,the algorithm steps based on the continuous data distortion is investigated,which integrates the inspection data and the distribution test method.Finally,a group of cable force data is utilized as an example to verify the established framework and algorithm.Experimental results show that the proposed algorithm can achieve high detection accuracy,providing a valuable reference for other monitoring data processing.
文摘Pulmonary nodules represent an early manifestation of lung cancer.However,pulmonary nodules only constitute a small portion of the overall image,posing challenges for physicians in image interpretation and potentially leading to false positives or missed detections.To solve these problems,the YOLOv8 network is enhanced by adding deformable convolution and atrous spatial pyramid pooling(ASPP),along with the integration of a coordinate attention(CA)mechanism.This allows the network to focus on small targets while expanding the receptive field without losing resolution.At the same time,context information on the target is gathered and feature expression is enhanced by attention modules in different directions.It effectively improves the positioning accuracy and achieves good results on the LUNA16 dataset.Compared with other detection algorithms,it improves the accuracy of pulmonary nodule detection to a certain extent.
文摘Load time series analysis is critical for resource management and optimization decisions,especially automated analysis techniques.Existing research has insufficiently interpreted the overall characteristics of samples,leading to significant differences in load level detection conclusions for samples with different characteristics(trend,seasonality,cyclicality).Achieving automated,feature-adaptive,and quantifiable analysis methods remains a challenge.This paper proposes a Threshold Recognition-based Load Level Detection Algorithm(TRLLD),which effectively identifies different load level regions in samples of arbitrary size and distribution type based on sample characteristics.By utilizing distribution density uniformity,the algorithm classifies data points and ultimately obtains normalized load values.In the feature recognition step,the algorithm employs the Density Uniformity Index Based on Differences(DUID),High Load Level Concentration(HLLC),and Low Load Level Concentration(LLLC)to assess sample characteristics,which are independent of specific load values,providing a standardized perspective on features,ensuring high efficiency and strong interpretability.Compared to traditional methods,the proposed approach demonstrates better adaptive and real-time analysis capabilities.Experimental results indicate that it can effectively identify high load and low load regions in 16 groups of time series samples with different load characteristics,yielding highly interpretable results.The correlation between the DUID and sample density distribution uniformity reaches 98.08%.When introducing 10% MAD intensity noise,the maximum relative error is 4.72%,showcasing high robustness.Notably,it exhibits significant advantages in general and low sample scenarios.
文摘The paper discusses an application for rail track thermal image fault detection. In order to get better results from the Canny edge detection algorithm, the image needs to be processed in advance. The histogram equalization method is proposed to enhance the contrast of the image. Since a thermal image contains multiple parallel rail tracks, an algorithm has been developed to locate and separate the tracks that we are interested in. This is accomplished by applying the least squares linear fitting technique to represent the surface of a track. The performance of the application is evaluated by using a number of images provided by a specialised company and the results are essentially favourable.
文摘Spectrum sensing is one of the most important steps in cognitive radio. In this paper, a new fully-distributed collaborative energy detection algorithm based on diffusion cooperation scheme and consensus filtering theory is proposed, which doesn’t need the center node to fuse the detection results of all users. The secondary users only exchange information with their neighbors to obtain the detection data, and then make the corresponding decisions independently according to the pre-defined threshold. Simulations show that the proposed algorithm is more superior to the existing centralized collaborative energy detection algorithm in terms of the detecting performance and robustness in the insecurity situation.
基金supported by the Shanghai Science and Technology Innovation Action Plan High-Tech Field Project(Grant No.22511100601)for the year 2022 and Technology Development Fund for People’s Livelihood Research(Research on Transmission Line Deep Foundation Pit Environmental Situation Awareness System Based on Multi-Source Data).
文摘To maintain the reliability of power systems,routine inspections using drones equipped with advanced object detection algorithms are essential for preempting power-related issues.The increasing resolution of drone-captured images has posed a challenge for traditional target detection methods,especially in identifying small objects in high-resolution images.This study presents an enhanced object detection algorithm based on the Faster Regionbased Convolutional Neural Network(Faster R-CNN)framework,specifically tailored for detecting small-scale electrical components like insulators,shock hammers,and screws in transmission line.The algorithm features an improved backbone network for Faster R-CNN,which significantly boosts the feature extraction network’s ability to detect fine details.The Region Proposal Network is optimized using a method of guided feature refinement(GFR),which achieves a balance between accuracy and speed.The incorporation of Generalized Intersection over Union(GIOU)and Region of Interest(ROI)Align further refines themodel’s accuracy.Experimental results demonstrate a notable improvement in mean Average Precision,reaching 89.3%,an 11.1%increase compared to the standard Faster R-CNN.This highlights the effectiveness of the proposed algorithm in identifying electrical components in high-resolution aerial images.
