Insulator Burst Fault Identification Based on YOLOv4

Jianchen Gao; Jiahong Zhang; Yingna Li; Chuan Li

doi:10.3788/LOP202259.0210005

[1] Lü Z N. A survey of common faults analysis and detection methods for transmission line[J]. Automation & Instrumentation, 161-164, 168(2020).

[2] Deng K, Tu M, Wan W J et al. Discussion on the application of live detection methods for UHV insulators[J]. Electrical Manufacturing, 9, 70-75(2014).

[3] Huang X N, Zhang Z L. A method to extract insulator image from aerial image of helicopter patrol[J]. Power System Technology, 34, 194-197(2010).

[4] Zhao Z B, Jin S X, Liu Y C. Aerial insulator image edge extraction method based on NSCT[J]. Chinese Journal of Scientific Instrument, 33, 2045-2052(2012).

[5] Li W G, Ye G S, Huang F et al. Recognition of insulator based on developed MPEG-7 texture feature[J]. High Voltage Apparatus, 46, 65-68(2010).

[6] Sun Y C, Pan S G, Zhao T et al. Traffic light detection based on optimized YOLOv3 algorithm[J]. Acta Optica Sinica, 40, 1215001(2020).

[7] Ju M R, Luo H B, Wang Z B et al. Improved YOLO V3 algorithm and its application in small target detection[J]. Acta Optica Sinica, 39, 0715004(2019).

[8] Xue Y, Wu H D, Zhang N et al. Detection of insulation piercing connectors and bolts on the transmission line using improved Faster R-CNN[J]. Laser & Optoelectronics Progress, 57, 081008(2020).

[9] Zhang G S, Ge G Y, Zhu R H et al. Gear defect detection based on the improved YOLOv3 network[J]. Laser & Optoelectronics Progress, 57, 121009(2020).

[10] Ren S Q, He K M, Girshick R et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 1137-1149(2017).

[11] He K M, Gkioxari G, Dollár P et al. Mask R-CNN[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, 386-397(2020).

[12] Redmon J, Divvala S, Girshick R et al. You only look once: unified, real-time object detection[C], 779-788(2016).

[13] Redmon J, Farhadi A. YOLO9000: better, faster, stronger[C], 6517-6525(2017).

[14] Redmon J, Farhadi A. YOLOv3: an incremental improvement[EB/OL]. https://arxiv.org/abs/1804.02767

[15] Liu W, Anguelov D, Erhan D et al. SSD: single shot MultiBox detector[M]. Leibe B, Matas J, Sebe N, et al. Computer vision-ECCV 2016. Lecture notes in computer science, 9905, 21-37(2016).

[16] Cheng H Y, Zhai Y J, Chen R. Faster R-CNN based recognition of insulators in aerial images[J]. Modern Electronics Technique, 42, 98-102(2019).

[17] Lai Q P, Yang J, Tan B D et al. An automatic recognition and defect diagnosis model of transmission line insulator based on YOLOv2 network[J]. Electric Power, 52, 31-39(2019).

[18] Dong Z J. Real-time detection of power transmission line key components based on YOLOv3[J]. Electronic Measurement Technology, 42, 173-178(2019).

[19] Bochkovskiy A, Wang C Y, Liao H Y M et al. YOLOv4: optimal speed and accuracy of object detection[EB/OL]. https://arxiv.org/abs/2004.10934