Unmanned aerial vehicle(UAV)photography has become the main power system inspection method;however,automated fault detection remains a major challenge.Conventional algorithms encounter difficulty in processing all the...Unmanned aerial vehicle(UAV)photography has become the main power system inspection method;however,automated fault detection remains a major challenge.Conventional algorithms encounter difficulty in processing all the detected objects in the power transmission lines simultaneously.The object detection method involving deep learning provides a new method for fault detection.However,the traditional non-maximum suppression(NMS)algorithm fails to delete redundant annotations when dealing with objects having two labels such as insulators and dampers.In this study,we propose an area-based non-maximum suppression(A-NMS)algorithm to solve the problem of one object having multiple labels.The A-NMS algorithm is used in the fusion stage of cropping detection to detect small objects.Experiments prove that A-NMS and cropping detection achieve a mean average precision and recall of 88.58%and 91.23%,respectively,in case of the aerial image datasets and realize multi-object fault detection in aerial images.展开更多
Referring expression comprehension(REC)aims to locate a specific region in an image described by a natural language.Existing two-stage methods generate multiple candidate proposals in the first stage,followed by selec...Referring expression comprehension(REC)aims to locate a specific region in an image described by a natural language.Existing two-stage methods generate multiple candidate proposals in the first stage,followed by selecting one of these proposals as the grounding result in the second stage.Nevertheless,the number of candidate proposals generated in the first stage significantly exceeds ground truth and the recall of critical objects is inadequate,thereby enormously limiting the overall network performance.To address the above issues,the authors propose an innovative method termed Separate Non-Maximum Suppression(Sep-NMS)for two-stage REC.Particularly,Sep-NMS models information from the two stages independently and collaboratively,ultimately achieving an overall improvement in comprehension and identification of the target objects.Specifically,the authors propose a Ref-Relatedness module for filtering referent proposals rigorously,decreasing the redundancy of referent proposals.A CLIP†Relatedness module based on robust multimodal pre-trained encoders is built to precisely assess the relevance between language and proposals to improve the recall of critical objects.It is worth mentioning that the authors are the pioneers in utilising a multimodal pre-training model for proposal filtering in the first stage.Moreover,an Information Fusion module is designed to effectively amalgamate the multimodal information across two stages,ensuring maximum uti-lisation of the available information.Extensive experiments demonstrate that the approach achieves competitive performance with previous state-of-the-art methods.The datasets used are publicly available:RefCOCO,RefCOCO+:https://doi.org/10.1007/978-3-319-46475-6_5 and RefCOCOg:https://doi.org/10.1109/CVPR.2016.9.展开更多
DNA is the basic compound for biological genetic characteristics. After DNA is damaged, DNA-repair synthesis would occur. This function is closely related with the alteration of cancer-cell, heritable disease, etc. Me...DNA is the basic compound for biological genetic characteristics. After DNA is damaged, DNA-repair synthesis would occur. This function is closely related with the alteration of cancer-cell, heritable disease, etc. Measurement of DNA-repair synthesis function is an important method in the study of the above diseases of molecular level and in the risk assessment for chemical mutagens. Since the alteration of DNA appears to be a key step展开更多
As the basic work of image stitching and object recognition,image registration played an important part in the image processing field.Much previous work in registration accuracy and realtime performance progressed ver...As the basic work of image stitching and object recognition,image registration played an important part in the image processing field.Much previous work in registration accuracy and realtime performance progressed very slowly,especially in registrating images with line feature.An innovative method for image registration based on lines is proposed,it can effectively improve the accuracy and real-time performance of image registration.The line feature can deal with some registration problems where point feature does not work.Our registration process is divided into two parts.The first part determines the rough registration transformation relation between reference image and test image.Then the similarity degree among different transformation and modified nonmaximum suppression(MNMS)algorithms are obtained,which produce local optimal solution to optimize the rough registration transformation.The final optimal registration relation can be obtained from two registration parts according to the match scores.The experimental results show that the proposed method makes a more accurate registration relation and performs better in real-time situation.展开更多
The increasing trend towards independent fruit packaging demands a high appearance quality of individually packed fruits.In this paper,we propose an improved YOLOv5-based model,YOLO-Banana,to effectively grade banana ...The increasing trend towards independent fruit packaging demands a high appearance quality of individually packed fruits.In this paper,we propose an improved YOLOv5-based model,YOLO-Banana,to effectively grade banana appearance quality based on the number of banana defect points.Due to the minor and dense defects on the surface of bananas,existing detection algorithms have poor detection results and high missing rates.To address this,we propose a densitybased spatial clustering of applications with noise(DBSCAN)and K-means fusion clustering method that utilizes refined anchor points to obtain better initial anchor values,thereby enhancing the network’s recognition accuracy.Moreover,the optimized progressive aggregated network(PANet)enables better multi-level feature fusion.Additionally,the non-maximum suppression function is replaced with a weighted non-maximum suppression(weighted NMS)function based on distance intersection over union(DIoU).Experimental results show that the model’s accuracy is improved by 2.3%compared to the original YOLOv5 network model,thereby effectively grading the banana appearance quality.展开更多
In order to avoid the problem of poor illumination characteristics and inaccurate positioning accuracy, this paper proposed a pedestrian detection algorithm suitable for low-light environments. The algorithm first app...In order to avoid the problem of poor illumination characteristics and inaccurate positioning accuracy, this paper proposed a pedestrian detection algorithm suitable for low-light environments. The algorithm first applied the multi-scale Retinex image enhancement algorithm to the sample pre-processing of deep learning to improve the image resolution. Then the paper used the faster regional convolutional neural network to train the pedestrian detection model, extracted the pedestrian characteristics, and obtained the bounding boxes through classification and position regression. Finally, the pedestrian detection process was carried out by introducing the Soft-NMS algorithm, and the redundant bounding box was eliminated to obtain the best pedestrian detection position. The experimental results showed that the proposed detection algorithm achieves an average accuracy of 89.74% on the low-light dataset, and the pedestrian detection effect was more significant.展开更多
In edge detection algorithms, there is a common redundancy problem, especially when the gradient direction is close to -135°, -45°, 45°, and 135°. Double edge effect appears on the edges around the...In edge detection algorithms, there is a common redundancy problem, especially when the gradient direction is close to -135°, -45°, 45°, and 135°. Double edge effect appears on the edges around these directions. This is caused by the discrete calculation of non-maximum suppression. Many algorithms use edge points as feature for further task such as line extraction, curve detection, matching and recognition. Redundancy is a very important factor of algorithm speed and accuracy. We find that most edge detection algorithms have redundancy of 50% in the worst case and 0% in the best case depending on the edge direction distribution. The common redundancy rate on natural images is approximately between 15% and 20%. Based on Canny’s framework, we propose a restriction in the hysteresis step. Our experiment shows that proposed restricted hysteresis reduce the redundancy successfully.展开更多
Conventional PID algorithm is unable to track the response with high frequency,and has obvious overshoot in some voice coil motor practical applications.So,combined with the fuzzy PID control theory,we can obtain the ...Conventional PID algorithm is unable to track the response with high frequency,and has obvious overshoot in some voice coil motor practical applications.So,combined with the fuzzy PID control theory,we can obtain the precise control by the method.Meanwhile,through the feedforward control,the performance of quick response and dynamic tracking can be improved.Thus,this control method not only maintains the excellent performance of the controller,but also improves the stability of the system.展开更多
三相四线制三电平有源滤波器(Active Power Filter,APF)系统采用传统三维空间矢量脉宽调制(3D-SVPWM)控制策略运算复杂,通过简化控制策略提高系统的实时性,进行了推导简化传统3D-SVPWM的策略。简化的3D-SVPWM调制算法,减少运算步骤,由ab...三相四线制三电平有源滤波器(Active Power Filter,APF)系统采用传统三维空间矢量脉宽调制(3D-SVPWM)控制策略运算复杂,通过简化控制策略提高系统的实时性,进行了推导简化传统3D-SVPWM的策略。简化的3D-SVPWM调制算法,减少运算步骤,由abc坐标系中的参考电压直接得到矢量的作用时间,省去了坐标系换算及扇区和小扇区的判断。通过在MATLAB下进行仿真实验,验证了所提算法的可行性。采用简化控制策略,在达到传统控制策略效果的同时,提高了系统的实时性、经济性,在工程领域有很大的意义。简化控制策略可运用于三相四线制三电平有源滤波器系统,且效果良好。展开更多
Relying on vision forfeature keypoint extraction,matching,and Perspective-n-points(PnP)calculation to achieve relative pose estimation is an effective solution forcarrierbased aircraft to achieve high-precision autono...Relying on vision forfeature keypoint extraction,matching,and Perspective-n-points(PnP)calculation to achieve relative pose estimation is an effective solution forcarrierbased aircraft to achieve high-precision autonomous landing.Existingmethods typically only perform global consistency feature detection withoutverifying the reliability of the detected features or considering the featuredistribution of key points.To address this issue,we propose a carrier-basedaircraft landing guidance state estimation algorithm based on efficientdistribution sampling of deep features.By extracting key points with highstate values,the relative pose estimation of the landing process is achieved.A multi-level feature coupling is constructed to establish a variable scale keypoint localization mechanism for key point extraction and detection.Anattention mechanism module is constructed to evaluate the state of highreliability key points.The experimental results show that the proposed methodhas better robustness and relative pose estimation accuracy,achieving robustand high-precision online relative pose measurement in airborne monocularvision landing guidance.展开更多
基金the National Grid Corporation Headquarters Science and Technology Project:Key Technology Research,Equipment Development and Engineering Demonstration of Artificial Smart Drived Electric Vehicle Smart Travel Service(No.52020118000G).
文摘Unmanned aerial vehicle(UAV)photography has become the main power system inspection method;however,automated fault detection remains a major challenge.Conventional algorithms encounter difficulty in processing all the detected objects in the power transmission lines simultaneously.The object detection method involving deep learning provides a new method for fault detection.However,the traditional non-maximum suppression(NMS)algorithm fails to delete redundant annotations when dealing with objects having two labels such as insulators and dampers.In this study,we propose an area-based non-maximum suppression(A-NMS)algorithm to solve the problem of one object having multiple labels.The A-NMS algorithm is used in the fusion stage of cropping detection to detect small objects.Experiments prove that A-NMS and cropping detection achieve a mean average precision and recall of 88.58%and 91.23%,respectively,in case of the aerial image datasets and realize multi-object fault detection in aerial images.
基金funded by the National Natural Science Foundation of China(No.62076032).
文摘Referring expression comprehension(REC)aims to locate a specific region in an image described by a natural language.Existing two-stage methods generate multiple candidate proposals in the first stage,followed by selecting one of these proposals as the grounding result in the second stage.Nevertheless,the number of candidate proposals generated in the first stage significantly exceeds ground truth and the recall of critical objects is inadequate,thereby enormously limiting the overall network performance.To address the above issues,the authors propose an innovative method termed Separate Non-Maximum Suppression(Sep-NMS)for two-stage REC.Particularly,Sep-NMS models information from the two stages independently and collaboratively,ultimately achieving an overall improvement in comprehension and identification of the target objects.Specifically,the authors propose a Ref-Relatedness module for filtering referent proposals rigorously,decreasing the redundancy of referent proposals.A CLIP†Relatedness module based on robust multimodal pre-trained encoders is built to precisely assess the relevance between language and proposals to improve the recall of critical objects.It is worth mentioning that the authors are the pioneers in utilising a multimodal pre-training model for proposal filtering in the first stage.Moreover,an Information Fusion module is designed to effectively amalgamate the multimodal information across two stages,ensuring maximum uti-lisation of the available information.Extensive experiments demonstrate that the approach achieves competitive performance with previous state-of-the-art methods.The datasets used are publicly available:RefCOCO,RefCOCO+:https://doi.org/10.1007/978-3-319-46475-6_5 and RefCOCOg:https://doi.org/10.1109/CVPR.2016.9.
文摘DNA is the basic compound for biological genetic characteristics. After DNA is damaged, DNA-repair synthesis would occur. This function is closely related with the alteration of cancer-cell, heritable disease, etc. Measurement of DNA-repair synthesis function is an important method in the study of the above diseases of molecular level and in the risk assessment for chemical mutagens. Since the alteration of DNA appears to be a key step
文摘As the basic work of image stitching and object recognition,image registration played an important part in the image processing field.Much previous work in registration accuracy and realtime performance progressed very slowly,especially in registrating images with line feature.An innovative method for image registration based on lines is proposed,it can effectively improve the accuracy and real-time performance of image registration.The line feature can deal with some registration problems where point feature does not work.Our registration process is divided into two parts.The first part determines the rough registration transformation relation between reference image and test image.Then the similarity degree among different transformation and modified nonmaximum suppression(MNMS)algorithms are obtained,which produce local optimal solution to optimize the rough registration transformation.The final optimal registration relation can be obtained from two registration parts according to the match scores.The experimental results show that the proposed method makes a more accurate registration relation and performs better in real-time situation.
基金supported by the Beijing Science Foundation(No.9232005)the Beijing Municipal Philosophy and Social Science Foundation of China(No.19GLB036)the Beijing Science and Technology Project(No.Z221100005822014)。
文摘The increasing trend towards independent fruit packaging demands a high appearance quality of individually packed fruits.In this paper,we propose an improved YOLOv5-based model,YOLO-Banana,to effectively grade banana appearance quality based on the number of banana defect points.Due to the minor and dense defects on the surface of bananas,existing detection algorithms have poor detection results and high missing rates.To address this,we propose a densitybased spatial clustering of applications with noise(DBSCAN)and K-means fusion clustering method that utilizes refined anchor points to obtain better initial anchor values,thereby enhancing the network’s recognition accuracy.Moreover,the optimized progressive aggregated network(PANet)enables better multi-level feature fusion.Additionally,the non-maximum suppression function is replaced with a weighted non-maximum suppression(weighted NMS)function based on distance intersection over union(DIoU).Experimental results show that the model’s accuracy is improved by 2.3%compared to the original YOLOv5 network model,thereby effectively grading the banana appearance quality.
文摘In order to avoid the problem of poor illumination characteristics and inaccurate positioning accuracy, this paper proposed a pedestrian detection algorithm suitable for low-light environments. The algorithm first applied the multi-scale Retinex image enhancement algorithm to the sample pre-processing of deep learning to improve the image resolution. Then the paper used the faster regional convolutional neural network to train the pedestrian detection model, extracted the pedestrian characteristics, and obtained the bounding boxes through classification and position regression. Finally, the pedestrian detection process was carried out by introducing the Soft-NMS algorithm, and the redundant bounding box was eliminated to obtain the best pedestrian detection position. The experimental results showed that the proposed detection algorithm achieves an average accuracy of 89.74% on the low-light dataset, and the pedestrian detection effect was more significant.
文摘In edge detection algorithms, there is a common redundancy problem, especially when the gradient direction is close to -135°, -45°, 45°, and 135°. Double edge effect appears on the edges around these directions. This is caused by the discrete calculation of non-maximum suppression. Many algorithms use edge points as feature for further task such as line extraction, curve detection, matching and recognition. Redundancy is a very important factor of algorithm speed and accuracy. We find that most edge detection algorithms have redundancy of 50% in the worst case and 0% in the best case depending on the edge direction distribution. The common redundancy rate on natural images is approximately between 15% and 20%. Based on Canny’s framework, we propose a restriction in the hysteresis step. Our experiment shows that proposed restricted hysteresis reduce the redundancy successfully.
文摘Conventional PID algorithm is unable to track the response with high frequency,and has obvious overshoot in some voice coil motor practical applications.So,combined with the fuzzy PID control theory,we can obtain the precise control by the method.Meanwhile,through the feedforward control,the performance of quick response and dynamic tracking can be improved.Thus,this control method not only maintains the excellent performance of the controller,but also improves the stability of the system.
文摘三相四线制三电平有源滤波器(Active Power Filter,APF)系统采用传统三维空间矢量脉宽调制(3D-SVPWM)控制策略运算复杂,通过简化控制策略提高系统的实时性,进行了推导简化传统3D-SVPWM的策略。简化的3D-SVPWM调制算法,减少运算步骤,由abc坐标系中的参考电压直接得到矢量的作用时间,省去了坐标系换算及扇区和小扇区的判断。通过在MATLAB下进行仿真实验,验证了所提算法的可行性。采用简化控制策略,在达到传统控制策略效果的同时,提高了系统的实时性、经济性,在工程领域有很大的意义。简化控制策略可运用于三相四线制三电平有源滤波器系统,且效果良好。
文摘Relying on vision forfeature keypoint extraction,matching,and Perspective-n-points(PnP)calculation to achieve relative pose estimation is an effective solution forcarrierbased aircraft to achieve high-precision autonomous landing.Existingmethods typically only perform global consistency feature detection withoutverifying the reliability of the detected features or considering the featuredistribution of key points.To address this issue,we propose a carrier-basedaircraft landing guidance state estimation algorithm based on efficientdistribution sampling of deep features.By extracting key points with highstate values,the relative pose estimation of the landing process is achieved.A multi-level feature coupling is constructed to establish a variable scale keypoint localization mechanism for key point extraction and detection.Anattention mechanism module is constructed to evaluate the state of highreliability key points.The experimental results show that the proposed methodhas better robustness and relative pose estimation accuracy,achieving robustand high-precision online relative pose measurement in airborne monocularvision landing guidance.
