Computer Science ›› 2022, Vol. 49 ›› Issue (6A): 412-417.doi: 10.11896/jsjkx.210600089
• Image Processing & Multimedia Technology • Previous Articles Next Articles
MA Bin, FU Yong-kang, WANG Chun-peng, LI Jian, WANG Yu-li
CLC Number:
| [1] YAN T T,YANG G D,YU J Z.Feature fusion based insulator detection for aerial inspection[C]//2017 36th Chinese Control Conference(CCC).IEEE,2017:10972-10977. [2] ZHOU S P,LEI X,QIAO B C,et al.Research on insulator fault diagnosis and remote monitoring system based on infrared images[J].Procedia Computer Science,2017,109:1194-1199. [3] FANG T,JIN X,HU X L,et al.A Fast Insulator-Contour-Detection-Algorithm on Power Transmission Lines Images[C]//Applied Mechanics & Materials.2012:337-343. [4] REDDY M J B,CHANDRA B K,MOHANTA D K.Condition Monitoring of 11 kV Distribution System Insulators Incorporating Complex Imagery Using Combined DOST-SVM Approach[J].IEEE Transactions on Dielectrics and Electrical Insulation,2013,20(2):664-74. [5] ZOU Z X,SHI Z W,GUO Y H,et al.Object detection in 20years:A survey[J].arXiv:1905.05055,2019. [6] ZHAO Z P,XU G Z,QI Y C,et al.Multi-patch deep features for power line insulator status classification from aerial images[C]//2016 International Joint Conference on Neural Networks(IJCNN).IEEE,2016:3187-3194. [7] LIAO S L,AN J B.A Robust Insulator Detection Algorithm Based on Local Features and Spatial Orders for Aerial Images[J].IEEE Geoscience and Remote Sensing Letters,2015,12(5):963-967. [8] PRATES R M,CRUZ R,MAROTTA A P,et al.Insulator visual non-conformity detection in overhead power distribution lines using deep learning[J].Computers & Electrical Engineering,2019,78:343-55. [9] LEU X S,SUI Z H.Intelligent fault detection of high voltage line based on the Faster R-CNN[J].Measurement,2019,138:379-85. [10] ZHAO Z B,ZHEN Z,ZHANG L,et al.Insulator DetectionMethod in Inspection Image Based on Improved Faster R-CNN[J].Energies,2019,12(7):1204. [11] TAO X,ZHANG D,WANG Z,et al.Detection of power line insulator defects using aerial images analyzed with convolutional neural networks[J].IEEE Transactions on Systems,Man,and Cybernetics:Systems,2018,50(4):1486-1498. [12] BOCHKOVSKIY A,WANG C Y,LIAO H Y M.YOLOv4:Optimal Speed and Accuracy of Object Detection[J].arXiv:2004.10934,2020. [13] REDMON J,DIVVALA S,GIRSHICK R,et al.You only look once:Unified,real-time object detection[C]//IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR).2016:779-788. [14] REDMON J,FARHADDI A.YOLO9000:better,faster,stron-ger[C]//Proceedings of the IEEEConference on Computer Vision and Pattern Recognition(CVPR).2017:7263-7271. [15] REDMON J,FARHADI A.Yolov3:An incremental improve-ment[J].arXiv:1804.02767,2018. [16] WANG C Y,LIAO H Y,WU Y H,et al.CSPNet:A New Backbone that can Enhance Learning Capability of CNN[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops(CVPRW).2020:390-391. [17] LIU S,QI L,QIN H F,et al.Path Aggregation Network for Instance Segmentation[C]//IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR).2018:8759-8768. [18] HE K M,ZHANG X Y,REN S Q,et al.Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2015,37(9):1904-1916. [19] ZHENG Z H,WANG P,LIU W,et al.Distance-IoU Loss:Faster and Better Learning for Bounding Box Regression[J].arXiv:1911.08287,2019. [20] LIN T Y,MAIRE M,BELONGIE S,et al.Microsoft COCO:Common Objects in Context[C]//European Conference on Computer Vision.2014:740-755. [21] WANG T C,ANWER R M,CHOLAKKAL H,et al.Learning Rich Features at High-Speed for Single-Shot Object Detection[C]//2019 IEEE/CVF International Conference on Computer Vision(ICCV).2020:1971-1980. [22] LIU W,ANGUELOV D,ERHAN D,et al.SSD:Single ShotMultiBox Detector[C]//European Conference on Computer Vision.Cham:Springer,2016:21-37. [23] LIN T Y,GOYAL P,GIRSHICK R,et al.Focal Loss for Dense Object Detection[C]//Proceedings of the IEEE International Conference on Computer Vision.2017:2980-2988. [24] TAN M X,PANG R M,LE Q V.EfficientDet:Scalable and Efficient Object Detection[C]//IEEE/CVF Conference on Compu-ter Vision and Pattern Recognition(CVPR).2020:10781-10790. [25] REZATOFIGHI H,TSOI N,GWAK J Y,et al.Generalized Intersection over Union:A Metric and A Loss for Bounding Box Regression[C]//IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR).2019:658-666. [26] LING Z N,ZHANG D X,QIU R C,et al.An Accurate and Real-time Method of Self-blast Glass Insulator Location Based on Faster R-CNN and U-net with Aerial Images[J].CSEE Journal of Power and Energy Systems,2019,5(4):474-482. [27] LIU C Y,WU Y Q,LIU J J,et al.MTI-YOLO:A Light-Weight and Real-Time Deep Neural Network for Insulator Detection in Complex Aerial Images[J].Energies,2021,14(5):1426. |
| [1] | LI Fa-guang, YILIHAMU·Yaermaimaiti. Real-time Detection Model of Insulator Defect Based on Improved CenterNet [J]. Computer Science, 2022, 49(5): 84-91. |
| [2] | ZHAO Xiao, LI Shi-lin, LI Fan, YU Zheng-tao, ZHANG Lin-hua, YANG Yong. Double-cycle Consistent Insulator Defect Sample Generation Method Based on Local Fine-grainedInformation Guidance [J]. Computer Science, 2021, 48(6A): 581-586. |
| [3] | FANG Ting and HAN Jia-ming. Detection and Localization of Insulator Defects in Aerial Images [J]. Computer Science, 2016, 43(Z6): 222-225. |
| [4] | . Insulator Image Denoising Based on Pixel Peer Groups and Neighbor Groups [J]. Computer Science, 2012, 39(6): 283-284. |
|
||
Home