In the present research,we describe a computer-aided detection(CAD)method aimed at automatic fetal head circumference(HC)measurement in 2D ultrasonography pictures during all trimesters of pregnancy.The HC might be ut...In the present research,we describe a computer-aided detection(CAD)method aimed at automatic fetal head circumference(HC)measurement in 2D ultrasonography pictures during all trimesters of pregnancy.The HC might be utilized toward determining gestational age and tracking fetal development.This automated approach is particularly valuable in low-resource settings where access to trained sonographers is limited.The CAD system is divided into two steps:to begin,Haar-like characteristics were extracted from ultrasound pictures in order to train a classifier using random forests to find the fetal skull.We identified the HC using dynamic programming,an elliptical fit,and a Hough transform.The computer-aided detection(CAD)program was well-trained on 999 pictures(HC18 challenge data source),and then verified on 335 photos from all trimesters in an independent test set.A skilled sonographer and an expert in medicine personally marked the test set.We used the crown-rump length(CRL)measurement to calculate the reference gestational age(GA).In the first,second,and third trimesters,the median difference between the standard GA and the GA calculated by the skilled sonographer stayed at 0.7±2.7,0.0±4.5,and 2.0±12.0 days,respectively.The regular duration variance between the baseline GA and the health investigator’s GA remained 1.5±3.0,1.9±5.0,and 4.0±14 a couple of days.The mean variance between the standard GA and the CAD system’s GA remained between 0.5 and 5.0,with an additional variation of 2.9 to 12.5 days.The outcomes reveal that the computer-aided detection(CAD)program outperforms an expert sonographer.When paired with the classifications reported in the literature,the provided system achieves results that are comparable or even better.We have assessed and scheduled this computerized approach for HC evaluation,which includes information from all trimesters of gestation.展开更多
CT colonography （CTC） is a non-invasive screening technique for the detection of eolorectal polyps, as an alternative to optical colonoscopy in clinical practice. Computer-aided detection （CAD） for CTC refers to a...CT colonography （CTC） is a non-invasive screening technique for the detection of eolorectal polyps, as an alternative to optical colonoscopy in clinical practice. Computer-aided detection （CAD） for CTC refers to a scheme which automatically detects colorectal polyps and masses in CT images of the colon. It has the potential to increase radiologists＇ detection performance and greatly shorten the detection time. Over the years, technical developments have advanced CAD for CTC substantially. In this paper, key techniques used in CAD for polyp detection are reviewed. Illustrations about the performance of existing CAD schemes show their relatively high sensitivity and low false positive rate. However, these CAD schemes are still suffering from technical or clinical problems. Some existing challenges faced by CAD are also pointed out at the end of this paper.展开更多
Computer aided detection(CADe)of pulmonary nodules plays an important role in assisting radiologists’diagnosis and alleviating interpretation burden for lung cancer.Current CADe systems,aiming at simulating radiologi...Computer aided detection(CADe)of pulmonary nodules plays an important role in assisting radiologists’diagnosis and alleviating interpretation burden for lung cancer.Current CADe systems,aiming at simulating radiologists’examination procedure,are built upon computer tomography(CT)images with feature extraction for detection and diagnosis.Human visual perception in CT image is reconstructed from sinogram,which is the original raw data acquired from CT scanner.In this work,different from the conventional image based CADe system,we propose a novel sinogram based CADe system in which the full projection information is used to explore additional effective features of nodules in the sinogram domain.Facing the challenges of limited research in this concept and unknown effective features in the sinogram domain,we design a new CADe system that utilizes the self-learning power of the convolutional neural network to learn and extract effective features from sinogram.The proposed system was validated on 208 patient cases from the publicly available online Lung Image Database Consortium database,with each case having at least one juxtapleural nodule annotation.Experimental results demonstrated that our proposed method obtained a value of 0.91 of the area under the curve(AUC)of receiver operating characteristic based on sinogram alone,comparing to 0.89 based on CT image alone.Moreover,a combination of sinogram and CT image could further improve the value of AUC to 0.92.This study indicates that pulmonary nodule detection in the sinogram domain is feasible with deep learning.展开更多
Background:The main cause of breast cancer is the deterioration of malignant tumor cells in breast tissue.Early diagnosis of tumors has become the most effective way to prevent breast cancer.Method:For distinguishing ...Background:The main cause of breast cancer is the deterioration of malignant tumor cells in breast tissue.Early diagnosis of tumors has become the most effective way to prevent breast cancer.Method:For distinguishing between tumor and non-tumor in MRI,a new type of computer-aided detection CAD system for breast tumors is designed in this paper.The CAD system was constructed using three networks,namely,the VGG16,Inception V3,and ResNet50.Then,the influence of the convolutional neural network second migration on the experimental results was further explored in the VGG16 system.Result:CAD system built based on VGG16,Inception V3,and ResNet50 has higher performance than mainstream CAD systems.Among them,the system built based on VGG16 and ResNet50 has outstanding performance.We further explore the impact of the secondary migration on the experimental results in the VGG16 system,and these results show that the migration can improve system performance of the proposed framework.Conclusion:The accuracy of CNN represented by VGG16 is as high as 91.25%,which is more accurate than traditional machine learningmodels.The F1 score of the three basic networks that join the secondary migration is close to 1.0,and the performance of the VGG16-based breast tumor CAD system is higher than Inception V3,and ResNet50.展开更多
Breast cancer is one of the most common and deadliest types of cancer among women and early detection is of major importance to decrease mortality rates. Microcalcification clusters and masses are two major indicators...Breast cancer is one of the most common and deadliest types of cancer among women and early detection is of major importance to decrease mortality rates. Microcalcification clusters and masses are two major indicators of malignancy in the early stages of this disease, when mammography is typically used as the screening technology. Computer-Aided Diagnosis (CAD) systems can support the radiologists’ work, by performing a double-reading process, which provides a second opinion that the physician can take into account in the detection process. This paper presents a CAD model based on computer vision procedures for locating suspicious regions that are later analyzed by artificial neural networks, support vector machines and linear discriminant analysis, to classify them into benign or malignant, based on a set of features that are extracted from lesions to characterize their visual content. A genetic algorithm is used to find the subset of features that provide the greatest discriminant power. Our results show that the SVM presented the highest overall accuracy and specificity for classifying microcalcification clusters, while the NN outperformed the rest for mass-classification in the same parameters. Overall accuracy, sensitivity and specificity were measured.展开更多
The mechanical-touched detector was used commonly in textile production limes. It has some defect with high false alarm rate, response delay and high maintenance cost. In order to overcome such defects, a new kind dev...The mechanical-touched detector was used commonly in textile production limes. It has some defect with high false alarm rate, response delay and high maintenance cost. In order to overcome such defects, a new kind device was developed and used to detect roller tangled in the production lines. It is based on image processing. The core algorithm was composed of Canny edge detection, removing interference, detection of perpendicularity line and detection of broken tow. After the four steps, the broken tow could be recognized quickly and correctly. The algorithm is robust and high efficiency. So, the detection device has characteristic of stable, quickly-response and low maintains cost. It can keep superiority with long lifespan even in more formidable conditions. It guarantees a safe and stable production condition.展开更多
In this thesis, a strategy realizing the computer-aided detection (CAD) of the epileptic waves in EEG isintroduced. The expert criterion, continuous wavelet transformation, neural networks, and characteristic paramete...In this thesis, a strategy realizing the computer-aided detection (CAD) of the epileptic waves in EEG isintroduced. The expert criterion, continuous wavelet transformation, neural networks, and characteristic parametermeasuremente these modern signa1 processing weapons were synthesized togetLher to form a so-called multi-method.It was estimated that the advantages of all the powerful techniques could be exploited systematically. Therefore, theCAD’s capacities in the long-term monitoring, trCaAnent and control of epilepsy might be enhanced. In this strategy,the raw EEG signals were uniformed and the expelt criterion were applied to discard most of aItifacts in them at first,and then the signals were pre-processed by continuous wavelet transformation. Some characteristic parameters wereextracted from the raw signals and the pre-processed ones. Consequently groups of eighteen parameters were sent totrain or test BP networks. By applying this theme a correct-detection rate of 84.3% for spike and sharp waves, and88.9% for sPike and sharp slow waves were obtained. In the next step, some non-linear tools wtll also be equippedwith the CAD system.展开更多
Background:Computer-aided detection(CAD)software has been introduced to automatically interpret digital chest X-rays.This study aimed to evaluate the performance of CAD software(JF CXR-1 v3.0,which was developed by a ...Background:Computer-aided detection(CAD)software has been introduced to automatically interpret digital chest X-rays.This study aimed to evaluate the performance of CAD software(JF CXR-1 v3.0,which was developed by a domestic Hi-tech enterprise)in tuberculosis(TB)case finding in China.Methods:In 2019,we conducted an internal evaluation of the performance of JF CXR-1 v3.0 by reading standard images annotated by a panel of experts.In 2020,using the reading results of chest X-rays by a panel of experts as the reference standard,we conducted an on-site prospective study to evaluate the performance of JF CXR-1 v3.0 and local radiologists in TB case finding in 13 township health centers in Zhongmu County,Henan Province.Results:Internal assessment results based on 277 standard images showed that JF CXR-1 v3.0 had a sensitivity of 85.94%(95%confidence interval[CI]:77.42%,94.45%)and a specificity of 74.65%(95%CI:68.81%,80.49%)to distinguish active TB from other imaging conditions.In the on-site evaluation phase,images from 3705 outpatients who underwent chest X-ray detection were read by JF CXR-1 v3.0 and local radiologists in parallel.The imaging diagnosis of local radiologists for active TB had a sensitivity of 32.89%(95%CI:22.33%,43.46%)and a specificity of 99.28%(95%CI:99.01%,99.56%),while JF CXR-1 v3.0 showed a significantly higher sensitivity of 92.11%(95%CI:86.04%,98.17%)(p<0.05)and maintained high specificity at 94.54%(95%CI:93.81%,95.28%).Conclusions:CAD software could play a positive role in improving the TB case finding capability of township health centers.展开更多
BACKGROUND Computer-aided diagnosis(CAD)may assist endoscopists in identifying and classifying polyps during colonoscopy for detecting colorectal cancer.AIM To build a system using CAD to detect and classify polyps ba...BACKGROUND Computer-aided diagnosis(CAD)may assist endoscopists in identifying and classifying polyps during colonoscopy for detecting colorectal cancer.AIM To build a system using CAD to detect and classify polyps based on the Yamada classification.METHODS A total of 24045 polyp and 72367 nonpolyp images were obtained.We established a computer-aided detection and Yamada classification model based on the YOLOv7 neural network algorithm.Frame-based and image-based evaluation metrics were employed to assess the performance.RESULTS Computer-aided detection and Yamada classification screened polyps with a precision of 96.7%,a recall of 95.8%,and an F1-score of 96.2%,outperforming those of all groups of endoscopists.In regard to the Yamada classification of polyps,the CAD system displayed a precision of 82.3%,a recall of 78.5%,and an F1-score of 80.2%,outper-forming all levels of endoscopists.In addition,according to the image-based method,the CAD had an accuracy of 99.2%,a specificity of 99.5%,a sensitivity of 98.5%,a positive predictive value of 99.0%,a negative predictive value of 99.2%for polyp detection and an accuracy of 97.2%,a specificity of 98.4%,a sensitivity of 79.2%,a positive predictive value of 83.0%,and a negative predictive value of 98.4%for poly Yamada classification.CONCLUSION We developed a novel CAD system based on a deep neural network for polyp detection,and the Yamada classi-fication outperformed that of nonexpert endoscopists.This CAD system could help community-based hospitals enhance their effectiveness in polyp detection and classification.展开更多
BACKGROUND Colorectal cancer has a high incidence and mortality rate,and the effectiveness of routine colonoscopy largely depends on the endoscopist’s expertise.In recent years,computer-aided detection(CADe)systems h...BACKGROUND Colorectal cancer has a high incidence and mortality rate,and the effectiveness of routine colonoscopy largely depends on the endoscopist’s expertise.In recent years,computer-aided detection(CADe)systems have been increasingly integrated into colonoscopy to improve detection accuracy.However,while most studies have focused on adenoma detection rate(ADR)as the primary outcome,the more sensitive adenoma miss rate(AMR)has been less frequently analyzed.AIM To evaluate the effectiveness of CADe in colonoscopy and assess the advantages of AMR over ADR.METHODS A comprehensive literature search was conducted in PubMed,Embase,and the Cochrane Central Register of Controlled Trials using predefined search strategies to identify relevant studies published up to August 2,2024.Statistical analyses were performed to compare outcomes between groups,and potential publication bias was assessed using funnel plots.The quality of the included studies was evaluated using the Cochrane Risk of Bias tool and the Grading of Recommendations,Assessment,Development,and Evaluation approach.RESULTS Five studies comprising 1624 patients met the inclusion criteria.AMR was significantly lower in the CADe-assisted group than in the routine colonoscopy group(147/927,15.9%vs 345/960,35.9%;P<0.01).However,CADe did not provide a significant advantage in detecting advanced adenomas or lesions measuring 6-9 mm or≥10 mm.The polyp miss rate(PMR)was also lower in the CADe-assisted group[odds ratio(OR),0.35;95% confidence interval(CI):0.23-0.52;P<0.01].While the overall ADR did not differ significantly between groups,the ADR during the first-pass examination was higher in the CADe-assisted group(OR,1.37;95%CI:1.10-1.69;P=0.004).The level of evidence for the included randomized controlled trials was graded as moderate.CONCLUSION CADe can significantly reduce AMR and PMR while improving ADR during initial detection,demonstrating its potential to enhance colonoscopy performance.These findings highlight the value of CADe in improving the detection of colorectal neoplasms,particularly small and histologically distinct adenomas.展开更多
Security and safety remain paramount concerns for both governments and individuals worldwide.In today’s context,the frequency of crimes and terrorist attacks is alarmingly increasing,becoming increasingly intolerable...Security and safety remain paramount concerns for both governments and individuals worldwide.In today’s context,the frequency of crimes and terrorist attacks is alarmingly increasing,becoming increasingly intolerable to society.Consequently,there is a pressing need for swift identification of potential threats to preemptively alert law enforcement and security forces,thereby preventing potential attacks or violent incidents.Recent advancements in big data analytics and deep learning have significantly enhanced the capabilities of computer vision in object detection,particularly in identifying firearms.This paper introduces a novel automatic firearm detection surveillance system,utilizing a one-stage detection approach named MARIE(Mechanism for Realtime Identification of Firearms).MARIE incorporates the Single Shot Multibox Detector(SSD)model,which has been specifically optimized to balance the speed-accuracy trade-off critical in firearm detection applications.The SSD model was further refined by integrating MobileNetV2 and InceptionV2 architectures for superior feature extraction capabilities.The experimental results demonstrate that this modified SSD configuration provides highly satisfactory performance,surpassing existing methods trained on the same dataset in terms of the critical speedaccuracy trade-off.Through these innovations,MARIE sets a new standard in surveillance technology,offering a robust solution to enhance public safety effectively.展开更多
The presence of aluminum(Al^(3+))and fluoride(F^(−))ions in the environment can be harmful to ecosystems and human health,highlighting the need for accurate and efficient monitoring.In this paper,an innovative approac...The presence of aluminum(Al^(3+))and fluoride(F^(−))ions in the environment can be harmful to ecosystems and human health,highlighting the need for accurate and efficient monitoring.In this paper,an innovative approach is presented that leverages the power of machine learning to enhance the accuracy and efficiency of fluorescence-based detection for sequential quantitative analysis of aluminum(Al^(3+))and fluoride(F^(−))ions in aqueous solutions.The proposed method involves the synthesis of sulfur-functionalized carbon dots(C-dots)as fluorescence probes,with fluorescence enhancement upon interaction with Al^(3+)ions,achieving a detection limit of 4.2 nmol/L.Subsequently,in the presence of F^(−)ions,fluorescence is quenched,with a detection limit of 47.6 nmol/L.The fingerprints of fluorescence images are extracted using a cross-platform computer vision library in Python,followed by data preprocessing.Subsequently,the fingerprint data is subjected to cluster analysis using the K-means model from machine learning,and the average Silhouette Coefficient indicates excellent model performance.Finally,a regression analysis based on the principal component analysis method is employed to achieve more precise quantitative analysis of aluminum and fluoride ions.The results demonstrate that the developed model excels in terms of accuracy and sensitivity.This groundbreaking model not only showcases exceptional performance but also addresses the urgent need for effective environmental monitoring and risk assessment,making it a valuable tool for safeguarding our ecosystems and public health.展开更多
In recent years,advancements in autonomous vehicle technology have accelerated,promising safer and more efficient transportation systems.However,achieving fully autonomous driving in challenging weather conditions,par...In recent years,advancements in autonomous vehicle technology have accelerated,promising safer and more efficient transportation systems.However,achieving fully autonomous driving in challenging weather conditions,particularly in snowy environments,remains a challenge.Snow-covered roads introduce unpredictable surface conditions,occlusions,and reduced visibility,that require robust and adaptive path detection algorithms.This paper presents an enhanced road detection framework for snowy environments,leveraging Simple Framework forContrastive Learning of Visual Representations(SimCLR)for Self-Supervised pretraining,hyperparameter optimization,and uncertainty-aware object detection to improve the performance of YouOnly Look Once version 8(YOLOv8).Themodel is trained and evaluated on a custom-built dataset collected from snowy roads in Tromsø,Norway,which covers a range of snow textures,illumination conditions,and road geometries.The proposed framework achieves scores in terms of mAP@50 equal to 99%and mAP@50–95 equal to 97%,demonstrating the effectiveness of YOLOv8 for real-time road detection in extreme winter conditions.The findings contribute to the safe and reliable deployment of autonomous vehicles in Arctic environments,enabling robust decision-making in hazardous weather conditions.This research lays the groundwork for more resilient perceptionmodels in self-driving systems,paving the way for the future development of intelligent and adaptive transportation networks.展开更多
Background Colonic polyps are frequently encountered in clinics. Computed tomographic colonography （CTC）, as a painless and quick detection, has high values in clinics. In this study, we evaluated the application va...Background Colonic polyps are frequently encountered in clinics. Computed tomographic colonography （CTC）, as a painless and quick detection, has high values in clinics. In this study, we evaluated the application value of computer-aided detection （CAD） in CTC detection of colonic polyps in the Chinese population.Methods CTC was performed with a GE 64-row multidetector computed tomography （MDCT） scanner. Data of 50 CTC patients （39 patients positive for at least one polyp of ≥0.5 cm in size and the other 11 patients negative by endoscopic detection） were retrospectively reviewed first without computer-aided detection （CAD） and then with CAD by four radiologists （two were experienced and another two inexperienced） blinded to colonoscopy findings. The sensitivity,specificity, positive predictive value, negative predictive value, and accuracy of detected colonic polyps, as well as the areas under the ROC curves （Az value） with and without CAD were calculated.Results CAD increased the overall sensitivity, specificity, positive predictive value, negative predictive value and accuracy of the colonic polyps detected by experienced and inexperienced readers. The sensitivity in detecting small polyps （5-9 mm） with CAD in experienced and inexperienced readers increased from 82% and 44% to 93% and 82%,respectively （P 〉0.05 and P 〈0.001）. With the use of CAD, the overall false positive rate and false negative rate for the detection of polyps by experienced and inexperienced readers decreased in different degrees. Among 13 sessile polyps not detected by CAD, two were 〉1.0 cm, eleven were 5-9 mm in diameter, and nine were fiat-shaped lesions.Conclusions The application of CAD in combination with CTC can increase the ability to detect colonic polyps,particularly for inexperienced readers. However, CAD is of limited value for the detection of flat polyps.展开更多
With the approval of more and more genetically modified(GM)crops in our country,GM safety management has become more important.Transgenic detection is a major approach for transgenic safety management.Nevertheless,a c...With the approval of more and more genetically modified(GM)crops in our country,GM safety management has become more important.Transgenic detection is a major approach for transgenic safety management.Nevertheless,a convenient and visual technique with low equipment requirements and high sensitivity for the field detection of GM plants is still lacking.On the basis of the existing recombinase polymerase amplification(RPA)technique,we developed a multiplex RPA(multi-RPA)method that can simultaneously detect three transgenic elements,including the cauliflower mosaic virus 35S gene(CaMV35S)promoter,neomycin phosphotransferaseⅡgene(NptⅡ)and hygromycin B phosphotransferase gene(Hyg),thus improving the detection rate.Moreover,we coupled this multi-RPA technique with the CRISPR/Cas12a reporter system,which enabled the detection results to be clearly observed by naked eyes under ultraviolet(UV)light(254 nm;which could be achieved by a portable UV flashlight),therefore establishing a multi-RPA visual detection technique.Compared with the traditional test strip detection method,this multi-RPA-CRISPR/Cas12a technique has the higher specificity,higher sensitivity,wider application range and lower cost.Compared with other polymerase chain reaction(PCR)techniques,it also has the advantages of low equipment requirements and visualization,making it a potentially feasible method for the field detection of GM plants.展开更多
Plants play a crucial role in maintaining ecological balance and biodiversity.However,plant health is easily affected by environmental stresses.Hence,the rapid and precise monitoring of plant health is crucial for glo...Plants play a crucial role in maintaining ecological balance and biodiversity.However,plant health is easily affected by environmental stresses.Hence,the rapid and precise monitoring of plant health is crucial for global food security and ecological balance.Currently,traditional detection strategies for monitoring plant health mainly rely on expensive equipment and complex operational procedures,which limit their widespread application.Fortunately,near-infrared(NIR)fluorescence and surface-enhanced Raman scattering(SERS)techniques have been recently highlighted in plants.NIR fluorescence imaging holds the advantages of being non-invasive,high-resolution and real-time,which is suitable for rapid screening in large-scale scenarios.While SERS enables highly sensitive and specific detection of trace chemical substances within plant tissues.Therefore,the complementarity of NIR fluorescence and SERS modalities can provide more comprehensive and accurate information for plant disease diagnosis and growth status monitoring.This article summarizes these two modalities in plant applications,and discusses the advantages of multimodal NIR fluorescence/SERS for a better understanding of a plant’s response to stress,thereby improving the accuracy and sensitivity of detection.展开更多
An improved model based on you only look once version 8(YOLOv8)is proposed to solve the problem of low detection accuracy due to the diversity of object sizes in optical remote sensing images.Firstly,the feature pyram...An improved model based on you only look once version 8(YOLOv8)is proposed to solve the problem of low detection accuracy due to the diversity of object sizes in optical remote sensing images.Firstly,the feature pyramid network(FPN)structure of the original YOLOv8 mode is replaced by the generalized-FPN(GFPN)structure in GiraffeDet to realize the"cross-layer"and"cross-scale"adaptive feature fusion,to enrich the semantic information and spatial information on the feature map to improve the target detection ability of the model.Secondly,a pyramid-pool module of multi atrous spatial pyramid pooling(MASPP)is designed by using the idea of atrous convolution and feature pyramid structure to extract multi-scale features,so as to improve the processing ability of the model for multi-scale objects.The experimental results show that the detection accuracy of the improved YOLOv8 model on DIOR dataset is 92%and mean average precision(mAP)is 87.9%,respectively 3.5%and 1.7%higher than those of the original model.It is proved the detection and classification ability of the proposed model on multi-dimensional optical remote sensing target has been improved.展开更多
In recent years,the number of patientswith colon disease has increased significantly.Colon polyps are the precursor lesions of colon cancer.If not diagnosed in time,they can easily develop into colon cancer,posing a s...In recent years,the number of patientswith colon disease has increased significantly.Colon polyps are the precursor lesions of colon cancer.If not diagnosed in time,they can easily develop into colon cancer,posing a serious threat to patients’lives and health.A colonoscopy is an important means of detecting colon polyps.However,in polyp imaging,due to the large differences and diverse types of polyps in size,shape,color,etc.,traditional detection methods face the problem of high false positive rates,which creates problems for doctors during the diagnosis process.In order to improve the accuracy and efficiency of colon polyp detection,this question proposes a network model suitable for colon polyp detection(PD-YOLO).This method introduces the self-attention mechanism CBAM(Convolutional Block Attention Module)in the backbone layer based on YOLOv7,allowing themodel to adaptively focus on key information and ignore the unimportant parts.To help themodel do a better job of polyp localization and bounding box regression,add the SPD-Conv(Symmetric Positive Definite Convolution)module to the neck layer and use deconvolution instead of upsampling.Theexperimental results indicate that the PD-YOLO algorithm demonstrates strong robustness in colon polyp detection.Compared to the original YOLOv7,on the Kvasir-SEG dataset,PD-YOLO has shown an increase of 5.44 percentage points in AP@0.5,showcasing significant advantages over other mainstream methods.展开更多
Anomaly Detection (AD) has been extensively adopted in industrial settings to facilitate quality control of products. It is critical to industrial production, especially to areas such as aircraft manufacturing, which ...Anomaly Detection (AD) has been extensively adopted in industrial settings to facilitate quality control of products. It is critical to industrial production, especially to areas such as aircraft manufacturing, which require strict part qualification rates. Although being more efficient and practical, few-shot AD has not been well explored. The existing AD methods only extract features in a single frequency while defects exist in multiple frequency domains. Moreover, current methods have not fully leveraged the few-shot support samples to extract input-related normal patterns. To address these issues, we propose an industrial few-shot AD method, Feature Extender for Anomaly Detection (FEAD), which extracts normal patterns in multiple frequency domains from few-shot samples under the guidance of the input sample. Firstly, to achieve better coverage of normal patterns in the input sample, we introduce a Sample-Conditioned Transformation Module (SCTM), which transforms support features under the guidance of the input sample to obtain extra normal patterns. Secondly, to effectively distinguish and localize anomaly patterns in multiple frequency domains, we devise an Adaptive Descriptor Construction Module (ADCM) to build and select pattern descriptors in a series of frequencies adaptively. Finally, an auxiliary task for SCTM is designed to ensure the diversity of transformations and include more normal patterns into support features. Extensive experiments on two widely used industrial AD datasets (MVTec-AD and VisA) demonstrate the effectiveness of the proposed FEAD.展开更多
Unmanned aerial vehicle(UAV)imagery poses significant challenges for object detection due to extreme scale variations,high-density small targets(68%in VisDrone dataset),and complex backgrounds.While YOLO-series models...Unmanned aerial vehicle(UAV)imagery poses significant challenges for object detection due to extreme scale variations,high-density small targets(68%in VisDrone dataset),and complex backgrounds.While YOLO-series models achieve speed-accuracy trade-offs via fixed convolution kernels and manual feature fusion,their rigid architectures struggle with multi-scale adaptability,as exemplified by YOLOv8n’s 36.4%mAP and 13.9%small-object AP on VisDrone2019.This paper presents YOLO-LE,a lightweight framework addressing these limitations through three novel designs:(1)We introduce the C2f-Dy and LDown modules to enhance the backbone’s sensitivity to small-object features while reducing backbone parameters,thereby improving model efficiency.(2)An adaptive feature fusion module is designed to dynamically integrate multi-scale feature maps,optimizing the neck structure,reducing neck complexity,and enhancing overall model performance.(3)We replace the original loss function with a distributed focal loss and incorporate a lightweight self-attention mechanism to improve small-object recognition and bounding box regression accuracy.Experimental results demonstrate that YOLO-LE achieves 39.9%mAP@0.5 on VisDrone2019,representing a 9.6%improvement over YOLOv8n,while maintaining 8.5 GFLOPs computational efficiency.This provides an efficient solution for UAV object detection in complex scenarios.展开更多
文摘In the present research,we describe a computer-aided detection(CAD)method aimed at automatic fetal head circumference(HC)measurement in 2D ultrasonography pictures during all trimesters of pregnancy.The HC might be utilized toward determining gestational age and tracking fetal development.This automated approach is particularly valuable in low-resource settings where access to trained sonographers is limited.The CAD system is divided into two steps:to begin,Haar-like characteristics were extracted from ultrasound pictures in order to train a classifier using random forests to find the fetal skull.We identified the HC using dynamic programming,an elliptical fit,and a Hough transform.The computer-aided detection(CAD)program was well-trained on 999 pictures(HC18 challenge data source),and then verified on 335 photos from all trimesters in an independent test set.A skilled sonographer and an expert in medicine personally marked the test set.We used the crown-rump length(CRL)measurement to calculate the reference gestational age(GA).In the first,second,and third trimesters,the median difference between the standard GA and the GA calculated by the skilled sonographer stayed at 0.7±2.7,0.0±4.5,and 2.0±12.0 days,respectively.The regular duration variance between the baseline GA and the health investigator’s GA remained 1.5±3.0,1.9±5.0,and 4.0±14 a couple of days.The mean variance between the standard GA and the CAD system’s GA remained between 0.5 and 5.0,with an additional variation of 2.9 to 12.5 days.The outcomes reveal that the computer-aided detection(CAD)program outperforms an expert sonographer.When paired with the classifications reported in the literature,the provided system achieves results that are comparable or even better.We have assessed and scheduled this computerized approach for HC evaluation,which includes information from all trimesters of gestation.
基金the National Natural Science Foundation of China(No.813716234)the National Basic Research Program(973) of China(No.2010CB834302)the Shanghai Jiao Tong University Medical Engineering Cross Research Funds(Nos.YG2013MS30 and YG2011MS51)
文摘CT colonography （CTC） is a non-invasive screening technique for the detection of eolorectal polyps, as an alternative to optical colonoscopy in clinical practice. Computer-aided detection （CAD） for CTC refers to a scheme which automatically detects colorectal polyps and masses in CT images of the colon. It has the potential to increase radiologists＇ detection performance and greatly shorten the detection time. Over the years, technical developments have advanced CAD for CTC substantially. In this paper, key techniques used in CAD for polyp detection are reviewed. Illustrations about the performance of existing CAD schemes show their relatively high sensitivity and low false positive rate. However, these CAD schemes are still suffering from technical or clinical problems. Some existing challenges faced by CAD are also pointed out at the end of this paper.
基金This work was partially supported by the NIH/NCI grant#CA206171 of the National Cancer Institute and the PSC-CUNY award 62310–0050.
文摘Computer aided detection(CADe)of pulmonary nodules plays an important role in assisting radiologists’diagnosis and alleviating interpretation burden for lung cancer.Current CADe systems,aiming at simulating radiologists’examination procedure,are built upon computer tomography(CT)images with feature extraction for detection and diagnosis.Human visual perception in CT image is reconstructed from sinogram,which is the original raw data acquired from CT scanner.In this work,different from the conventional image based CADe system,we propose a novel sinogram based CADe system in which the full projection information is used to explore additional effective features of nodules in the sinogram domain.Facing the challenges of limited research in this concept and unknown effective features in the sinogram domain,we design a new CADe system that utilizes the self-learning power of the convolutional neural network to learn and extract effective features from sinogram.The proposed system was validated on 208 patient cases from the publicly available online Lung Image Database Consortium database,with each case having at least one juxtapleural nodule annotation.Experimental results demonstrated that our proposed method obtained a value of 0.91 of the area under the curve(AUC)of receiver operating characteristic based on sinogram alone,comparing to 0.89 based on CT image alone.Moreover,a combination of sinogram and CT image could further improve the value of AUC to 0.92.This study indicates that pulmonary nodule detection in the sinogram domain is feasible with deep learning.
文摘Background:The main cause of breast cancer is the deterioration of malignant tumor cells in breast tissue.Early diagnosis of tumors has become the most effective way to prevent breast cancer.Method:For distinguishing between tumor and non-tumor in MRI,a new type of computer-aided detection CAD system for breast tumors is designed in this paper.The CAD system was constructed using three networks,namely,the VGG16,Inception V3,and ResNet50.Then,the influence of the convolutional neural network second migration on the experimental results was further explored in the VGG16 system.Result:CAD system built based on VGG16,Inception V3,and ResNet50 has higher performance than mainstream CAD systems.Among them,the system built based on VGG16 and ResNet50 has outstanding performance.We further explore the impact of the secondary migration on the experimental results in the VGG16 system,and these results show that the migration can improve system performance of the proposed framework.Conclusion:The accuracy of CNN represented by VGG16 is as high as 91.25%,which is more accurate than traditional machine learningmodels.The F1 score of the three basic networks that join the secondary migration is close to 1.0,and the performance of the VGG16-based breast tumor CAD system is higher than Inception V3,and ResNet50.
文摘Breast cancer is one of the most common and deadliest types of cancer among women and early detection is of major importance to decrease mortality rates. Microcalcification clusters and masses are two major indicators of malignancy in the early stages of this disease, when mammography is typically used as the screening technology. Computer-Aided Diagnosis (CAD) systems can support the radiologists’ work, by performing a double-reading process, which provides a second opinion that the physician can take into account in the detection process. This paper presents a CAD model based on computer vision procedures for locating suspicious regions that are later analyzed by artificial neural networks, support vector machines and linear discriminant analysis, to classify them into benign or malignant, based on a set of features that are extracted from lesions to characterize their visual content. A genetic algorithm is used to find the subset of features that provide the greatest discriminant power. Our results show that the SVM presented the highest overall accuracy and specificity for classifying microcalcification clusters, while the NN outperformed the rest for mass-classification in the same parameters. Overall accuracy, sensitivity and specificity were measured.
文摘The mechanical-touched detector was used commonly in textile production limes. It has some defect with high false alarm rate, response delay and high maintenance cost. In order to overcome such defects, a new kind device was developed and used to detect roller tangled in the production lines. It is based on image processing. The core algorithm was composed of Canny edge detection, removing interference, detection of perpendicularity line and detection of broken tow. After the four steps, the broken tow could be recognized quickly and correctly. The algorithm is robust and high efficiency. So, the detection device has characteristic of stable, quickly-response and low maintains cost. It can keep superiority with long lifespan even in more formidable conditions. It guarantees a safe and stable production condition.
文摘In this thesis, a strategy realizing the computer-aided detection (CAD) of the epileptic waves in EEG isintroduced. The expert criterion, continuous wavelet transformation, neural networks, and characteristic parametermeasuremente these modern signa1 processing weapons were synthesized togetLher to form a so-called multi-method.It was estimated that the advantages of all the powerful techniques could be exploited systematically. Therefore, theCAD’s capacities in the long-term monitoring, trCaAnent and control of epilepsy might be enhanced. In this strategy,the raw EEG signals were uniformed and the expelt criterion were applied to discard most of aItifacts in them at first,and then the signals were pre-processed by continuous wavelet transformation. Some characteristic parameters wereextracted from the raw signals and the pre-processed ones. Consequently groups of eighteen parameters were sent totrain or test BP networks. By applying this theme a correct-detection rate of 84.3% for spike and sharp waves, and88.9% for sPike and sharp slow waves were obtained. In the next step, some non-linear tools wtll also be equippedwith the CAD system.
基金supported by the National Science and Technology Major Project of China[2017ZX10201302-008]the CAMS Innovation Fund for Medical Sciences[2021-I2M-1-037].
文摘Background:Computer-aided detection(CAD)software has been introduced to automatically interpret digital chest X-rays.This study aimed to evaluate the performance of CAD software(JF CXR-1 v3.0,which was developed by a domestic Hi-tech enterprise)in tuberculosis(TB)case finding in China.Methods:In 2019,we conducted an internal evaluation of the performance of JF CXR-1 v3.0 by reading standard images annotated by a panel of experts.In 2020,using the reading results of chest X-rays by a panel of experts as the reference standard,we conducted an on-site prospective study to evaluate the performance of JF CXR-1 v3.0 and local radiologists in TB case finding in 13 township health centers in Zhongmu County,Henan Province.Results:Internal assessment results based on 277 standard images showed that JF CXR-1 v3.0 had a sensitivity of 85.94%(95%confidence interval[CI]:77.42%,94.45%)and a specificity of 74.65%(95%CI:68.81%,80.49%)to distinguish active TB from other imaging conditions.In the on-site evaluation phase,images from 3705 outpatients who underwent chest X-ray detection were read by JF CXR-1 v3.0 and local radiologists in parallel.The imaging diagnosis of local radiologists for active TB had a sensitivity of 32.89%(95%CI:22.33%,43.46%)and a specificity of 99.28%(95%CI:99.01%,99.56%),while JF CXR-1 v3.0 showed a significantly higher sensitivity of 92.11%(95%CI:86.04%,98.17%)(p<0.05)and maintained high specificity at 94.54%(95%CI:93.81%,95.28%).Conclusions:CAD software could play a positive role in improving the TB case finding capability of township health centers.
基金Supported by Science and Technology Projects in Guangzhou,No.2023A04J2282。
文摘BACKGROUND Computer-aided diagnosis(CAD)may assist endoscopists in identifying and classifying polyps during colonoscopy for detecting colorectal cancer.AIM To build a system using CAD to detect and classify polyps based on the Yamada classification.METHODS A total of 24045 polyp and 72367 nonpolyp images were obtained.We established a computer-aided detection and Yamada classification model based on the YOLOv7 neural network algorithm.Frame-based and image-based evaluation metrics were employed to assess the performance.RESULTS Computer-aided detection and Yamada classification screened polyps with a precision of 96.7%,a recall of 95.8%,and an F1-score of 96.2%,outperforming those of all groups of endoscopists.In regard to the Yamada classification of polyps,the CAD system displayed a precision of 82.3%,a recall of 78.5%,and an F1-score of 80.2%,outper-forming all levels of endoscopists.In addition,according to the image-based method,the CAD had an accuracy of 99.2%,a specificity of 99.5%,a sensitivity of 98.5%,a positive predictive value of 99.0%,a negative predictive value of 99.2%for polyp detection and an accuracy of 97.2%,a specificity of 98.4%,a sensitivity of 79.2%,a positive predictive value of 83.0%,and a negative predictive value of 98.4%for poly Yamada classification.CONCLUSION We developed a novel CAD system based on a deep neural network for polyp detection,and the Yamada classi-fication outperformed that of nonexpert endoscopists.This CAD system could help community-based hospitals enhance their effectiveness in polyp detection and classification.
文摘BACKGROUND Colorectal cancer has a high incidence and mortality rate,and the effectiveness of routine colonoscopy largely depends on the endoscopist’s expertise.In recent years,computer-aided detection(CADe)systems have been increasingly integrated into colonoscopy to improve detection accuracy.However,while most studies have focused on adenoma detection rate(ADR)as the primary outcome,the more sensitive adenoma miss rate(AMR)has been less frequently analyzed.AIM To evaluate the effectiveness of CADe in colonoscopy and assess the advantages of AMR over ADR.METHODS A comprehensive literature search was conducted in PubMed,Embase,and the Cochrane Central Register of Controlled Trials using predefined search strategies to identify relevant studies published up to August 2,2024.Statistical analyses were performed to compare outcomes between groups,and potential publication bias was assessed using funnel plots.The quality of the included studies was evaluated using the Cochrane Risk of Bias tool and the Grading of Recommendations,Assessment,Development,and Evaluation approach.RESULTS Five studies comprising 1624 patients met the inclusion criteria.AMR was significantly lower in the CADe-assisted group than in the routine colonoscopy group(147/927,15.9%vs 345/960,35.9%;P<0.01).However,CADe did not provide a significant advantage in detecting advanced adenomas or lesions measuring 6-9 mm or≥10 mm.The polyp miss rate(PMR)was also lower in the CADe-assisted group[odds ratio(OR),0.35;95% confidence interval(CI):0.23-0.52;P<0.01].While the overall ADR did not differ significantly between groups,the ADR during the first-pass examination was higher in the CADe-assisted group(OR,1.37;95%CI:1.10-1.69;P=0.004).The level of evidence for the included randomized controlled trials was graded as moderate.CONCLUSION CADe can significantly reduce AMR and PMR while improving ADR during initial detection,demonstrating its potential to enhance colonoscopy performance.These findings highlight the value of CADe in improving the detection of colorectal neoplasms,particularly small and histologically distinct adenomas.
文摘Security and safety remain paramount concerns for both governments and individuals worldwide.In today’s context,the frequency of crimes and terrorist attacks is alarmingly increasing,becoming increasingly intolerable to society.Consequently,there is a pressing need for swift identification of potential threats to preemptively alert law enforcement and security forces,thereby preventing potential attacks or violent incidents.Recent advancements in big data analytics and deep learning have significantly enhanced the capabilities of computer vision in object detection,particularly in identifying firearms.This paper introduces a novel automatic firearm detection surveillance system,utilizing a one-stage detection approach named MARIE(Mechanism for Realtime Identification of Firearms).MARIE incorporates the Single Shot Multibox Detector(SSD)model,which has been specifically optimized to balance the speed-accuracy trade-off critical in firearm detection applications.The SSD model was further refined by integrating MobileNetV2 and InceptionV2 architectures for superior feature extraction capabilities.The experimental results demonstrate that this modified SSD configuration provides highly satisfactory performance,surpassing existing methods trained on the same dataset in terms of the critical speedaccuracy trade-off.Through these innovations,MARIE sets a new standard in surveillance technology,offering a robust solution to enhance public safety effectively.
基金supported by the National Natural Science Foundation of China(No.U21A20290)Guangdong Basic and Applied Basic Research Foundation(No.2022A1515011656)+2 种基金the Projects of Talents Recruitment of GDUPT(No.2023rcyj1003)the 2022“Sail Plan”Project of Maoming Green Chemical Industry Research Institute(No.MMGCIRI2022YFJH-Y-024)Maoming Science and Technology Project(No.2023382).
文摘The presence of aluminum(Al^(3+))and fluoride(F^(−))ions in the environment can be harmful to ecosystems and human health,highlighting the need for accurate and efficient monitoring.In this paper,an innovative approach is presented that leverages the power of machine learning to enhance the accuracy and efficiency of fluorescence-based detection for sequential quantitative analysis of aluminum(Al^(3+))and fluoride(F^(−))ions in aqueous solutions.The proposed method involves the synthesis of sulfur-functionalized carbon dots(C-dots)as fluorescence probes,with fluorescence enhancement upon interaction with Al^(3+)ions,achieving a detection limit of 4.2 nmol/L.Subsequently,in the presence of F^(−)ions,fluorescence is quenched,with a detection limit of 47.6 nmol/L.The fingerprints of fluorescence images are extracted using a cross-platform computer vision library in Python,followed by data preprocessing.Subsequently,the fingerprint data is subjected to cluster analysis using the K-means model from machine learning,and the average Silhouette Coefficient indicates excellent model performance.Finally,a regression analysis based on the principal component analysis method is employed to achieve more precise quantitative analysis of aluminum and fluoride ions.The results demonstrate that the developed model excels in terms of accuracy and sensitivity.This groundbreaking model not only showcases exceptional performance but also addresses the urgent need for effective environmental monitoring and risk assessment,making it a valuable tool for safeguarding our ecosystems and public health.
文摘In recent years,advancements in autonomous vehicle technology have accelerated,promising safer and more efficient transportation systems.However,achieving fully autonomous driving in challenging weather conditions,particularly in snowy environments,remains a challenge.Snow-covered roads introduce unpredictable surface conditions,occlusions,and reduced visibility,that require robust and adaptive path detection algorithms.This paper presents an enhanced road detection framework for snowy environments,leveraging Simple Framework forContrastive Learning of Visual Representations(SimCLR)for Self-Supervised pretraining,hyperparameter optimization,and uncertainty-aware object detection to improve the performance of YouOnly Look Once version 8(YOLOv8).Themodel is trained and evaluated on a custom-built dataset collected from snowy roads in Tromsø,Norway,which covers a range of snow textures,illumination conditions,and road geometries.The proposed framework achieves scores in terms of mAP@50 equal to 99%and mAP@50–95 equal to 97%,demonstrating the effectiveness of YOLOv8 for real-time road detection in extreme winter conditions.The findings contribute to the safe and reliable deployment of autonomous vehicles in Arctic environments,enabling robust decision-making in hazardous weather conditions.This research lays the groundwork for more resilient perceptionmodels in self-driving systems,paving the way for the future development of intelligent and adaptive transportation networks.
文摘Background Colonic polyps are frequently encountered in clinics. Computed tomographic colonography （CTC）, as a painless and quick detection, has high values in clinics. In this study, we evaluated the application value of computer-aided detection （CAD） in CTC detection of colonic polyps in the Chinese population.Methods CTC was performed with a GE 64-row multidetector computed tomography （MDCT） scanner. Data of 50 CTC patients （39 patients positive for at least one polyp of ≥0.5 cm in size and the other 11 patients negative by endoscopic detection） were retrospectively reviewed first without computer-aided detection （CAD） and then with CAD by four radiologists （two were experienced and another two inexperienced） blinded to colonoscopy findings. The sensitivity,specificity, positive predictive value, negative predictive value, and accuracy of detected colonic polyps, as well as the areas under the ROC curves （Az value） with and without CAD were calculated.Results CAD increased the overall sensitivity, specificity, positive predictive value, negative predictive value and accuracy of the colonic polyps detected by experienced and inexperienced readers. The sensitivity in detecting small polyps （5-9 mm） with CAD in experienced and inexperienced readers increased from 82% and 44% to 93% and 82%,respectively （P 〉0.05 and P 〈0.001）. With the use of CAD, the overall false positive rate and false negative rate for the detection of polyps by experienced and inexperienced readers decreased in different degrees. Among 13 sessile polyps not detected by CAD, two were 〉1.0 cm, eleven were 5-9 mm in diameter, and nine were fiat-shaped lesions.Conclusions The application of CAD in combination with CTC can increase the ability to detect colonic polyps,particularly for inexperienced readers. However, CAD is of limited value for the detection of flat polyps.
基金the Experimental Technology Research Project of Zhejiang University(SYB202138)National Natural Science Foundation of China(32000195)。
文摘With the approval of more and more genetically modified(GM)crops in our country,GM safety management has become more important.Transgenic detection is a major approach for transgenic safety management.Nevertheless,a convenient and visual technique with low equipment requirements and high sensitivity for the field detection of GM plants is still lacking.On the basis of the existing recombinase polymerase amplification(RPA)technique,we developed a multiplex RPA(multi-RPA)method that can simultaneously detect three transgenic elements,including the cauliflower mosaic virus 35S gene(CaMV35S)promoter,neomycin phosphotransferaseⅡgene(NptⅡ)and hygromycin B phosphotransferase gene(Hyg),thus improving the detection rate.Moreover,we coupled this multi-RPA technique with the CRISPR/Cas12a reporter system,which enabled the detection results to be clearly observed by naked eyes under ultraviolet(UV)light(254 nm;which could be achieved by a portable UV flashlight),therefore establishing a multi-RPA visual detection technique.Compared with the traditional test strip detection method,this multi-RPA-CRISPR/Cas12a technique has the higher specificity,higher sensitivity,wider application range and lower cost.Compared with other polymerase chain reaction(PCR)techniques,it also has the advantages of low equipment requirements and visualization,making it a potentially feasible method for the field detection of GM plants.
基金funded by the National Natural Science Foundation of China(Nos.22374055,22022404,22074050,82172055)the National Natural Science Foundation of Hubei Province(No.22022CFA033)the Fundamental Research Funds for the Central Universities(Nos.CCNU24JCPT001,CCNU24JCPT020)。
文摘Plants play a crucial role in maintaining ecological balance and biodiversity.However,plant health is easily affected by environmental stresses.Hence,the rapid and precise monitoring of plant health is crucial for global food security and ecological balance.Currently,traditional detection strategies for monitoring plant health mainly rely on expensive equipment and complex operational procedures,which limit their widespread application.Fortunately,near-infrared(NIR)fluorescence and surface-enhanced Raman scattering(SERS)techniques have been recently highlighted in plants.NIR fluorescence imaging holds the advantages of being non-invasive,high-resolution and real-time,which is suitable for rapid screening in large-scale scenarios.While SERS enables highly sensitive and specific detection of trace chemical substances within plant tissues.Therefore,the complementarity of NIR fluorescence and SERS modalities can provide more comprehensive and accurate information for plant disease diagnosis and growth status monitoring.This article summarizes these two modalities in plant applications,and discusses the advantages of multimodal NIR fluorescence/SERS for a better understanding of a plant’s response to stress,thereby improving the accuracy and sensitivity of detection.
基金supported by the National Natural Science Foundation of China(No.62241109)the Tianjin Science and Technology Commissioner Project(No.20YDTPJC01110)。
文摘An improved model based on you only look once version 8(YOLOv8)is proposed to solve the problem of low detection accuracy due to the diversity of object sizes in optical remote sensing images.Firstly,the feature pyramid network(FPN)structure of the original YOLOv8 mode is replaced by the generalized-FPN(GFPN)structure in GiraffeDet to realize the"cross-layer"and"cross-scale"adaptive feature fusion,to enrich the semantic information and spatial information on the feature map to improve the target detection ability of the model.Secondly,a pyramid-pool module of multi atrous spatial pyramid pooling(MASPP)is designed by using the idea of atrous convolution and feature pyramid structure to extract multi-scale features,so as to improve the processing ability of the model for multi-scale objects.The experimental results show that the detection accuracy of the improved YOLOv8 model on DIOR dataset is 92%and mean average precision(mAP)is 87.9%,respectively 3.5%and 1.7%higher than those of the original model.It is proved the detection and classification ability of the proposed model on multi-dimensional optical remote sensing target has been improved.
基金funded by the Undergraduate Higher Education Teaching and Research Project(No.FBJY20230216)Research Projects of Putian University(No.2023043)the Education Department of the Fujian Province Project(No.JAT220300).
文摘In recent years,the number of patientswith colon disease has increased significantly.Colon polyps are the precursor lesions of colon cancer.If not diagnosed in time,they can easily develop into colon cancer,posing a serious threat to patients’lives and health.A colonoscopy is an important means of detecting colon polyps.However,in polyp imaging,due to the large differences and diverse types of polyps in size,shape,color,etc.,traditional detection methods face the problem of high false positive rates,which creates problems for doctors during the diagnosis process.In order to improve the accuracy and efficiency of colon polyp detection,this question proposes a network model suitable for colon polyp detection(PD-YOLO).This method introduces the self-attention mechanism CBAM(Convolutional Block Attention Module)in the backbone layer based on YOLOv7,allowing themodel to adaptively focus on key information and ignore the unimportant parts.To help themodel do a better job of polyp localization and bounding box regression,add the SPD-Conv(Symmetric Positive Definite Convolution)module to the neck layer and use deconvolution instead of upsampling.Theexperimental results indicate that the PD-YOLO algorithm demonstrates strong robustness in colon polyp detection.Compared to the original YOLOv7,on the Kvasir-SEG dataset,PD-YOLO has shown an increase of 5.44 percentage points in AP@0.5,showcasing significant advantages over other mainstream methods.
基金supported by the National Natural Science Foundation of China(No.52188102).
文摘Anomaly Detection (AD) has been extensively adopted in industrial settings to facilitate quality control of products. It is critical to industrial production, especially to areas such as aircraft manufacturing, which require strict part qualification rates. Although being more efficient and practical, few-shot AD has not been well explored. The existing AD methods only extract features in a single frequency while defects exist in multiple frequency domains. Moreover, current methods have not fully leveraged the few-shot support samples to extract input-related normal patterns. To address these issues, we propose an industrial few-shot AD method, Feature Extender for Anomaly Detection (FEAD), which extracts normal patterns in multiple frequency domains from few-shot samples under the guidance of the input sample. Firstly, to achieve better coverage of normal patterns in the input sample, we introduce a Sample-Conditioned Transformation Module (SCTM), which transforms support features under the guidance of the input sample to obtain extra normal patterns. Secondly, to effectively distinguish and localize anomaly patterns in multiple frequency domains, we devise an Adaptive Descriptor Construction Module (ADCM) to build and select pattern descriptors in a series of frequencies adaptively. Finally, an auxiliary task for SCTM is designed to ensure the diversity of transformations and include more normal patterns into support features. Extensive experiments on two widely used industrial AD datasets (MVTec-AD and VisA) demonstrate the effectiveness of the proposed FEAD.
文摘Unmanned aerial vehicle(UAV)imagery poses significant challenges for object detection due to extreme scale variations,high-density small targets(68%in VisDrone dataset),and complex backgrounds.While YOLO-series models achieve speed-accuracy trade-offs via fixed convolution kernels and manual feature fusion,their rigid architectures struggle with multi-scale adaptability,as exemplified by YOLOv8n’s 36.4%mAP and 13.9%small-object AP on VisDrone2019.This paper presents YOLO-LE,a lightweight framework addressing these limitations through three novel designs:(1)We introduce the C2f-Dy and LDown modules to enhance the backbone’s sensitivity to small-object features while reducing backbone parameters,thereby improving model efficiency.(2)An adaptive feature fusion module is designed to dynamically integrate multi-scale feature maps,optimizing the neck structure,reducing neck complexity,and enhancing overall model performance.(3)We replace the original loss function with a distributed focal loss and incorporate a lightweight self-attention mechanism to improve small-object recognition and bounding box regression accuracy.Experimental results demonstrate that YOLO-LE achieves 39.9%mAP@0.5 on VisDrone2019,representing a 9.6%improvement over YOLOv8n,while maintaining 8.5 GFLOPs computational efficiency.This provides an efficient solution for UAV object detection in complex scenarios.
