Insulator defect detection algorithm based on improved YOLOv5
DOI:
https://doi.org/10.54097/fcis.v3i2.7168Keywords:
YOLOv5, A small goal, An insulator, Defect detection, BiFPN, CBAM attention mechanism, K means algorithmAbstract
Aiming at the problems such as small target scale, complex background, difficult detection, false detection and leakage detection of aerial insulators in transmission lines, this paper proposes an insulator defect detection algorithm based on improved YOLOv5. Firstly, CBAM attention module is added to the backbone network of YOLOv5 to improve the feature extraction capability of insulator pictures. Secondly, in the feature extraction part, PANet structure is replaced by BiFPN structure to make full use of the underlying feature information. Finally, the improved K-means algorithm is used to determine the prior frame and improve the defect detection accuracy of the insulator. Experimental results show that this method can improve the identification accuracy of insulator defect detection in transmission lines.
Downloads
References
Gao Youhua, Wang Caiyun, Liu Xiaoming, et al. Analysis of electric field of basin-type insulator existing metal particles and its influence on surface flashover [J]. New Technology of Electrical Engineering,2015,34(8) :56-61.
Huang Ruiying, HUANG Daochun, Zhou Jun, et al. Research on bird damage risk region of 400kV DC transmission line [J]. New Technology of Electrical Engineering, 2017, 36(2): 68-73.
Chen Wenhao, Yao Lina, Li Fengzhe. Insulator Defect Detection and Location in UAV Power Grid Inspection [J]. Computer Applications, 2019,39(S1); 210-214.
Tan Jicheng. Automatic Insulator Detection for Power Line Using Aerial Images Powered by Convolutional Neural Networks[J]. Journal of Physics: Conference Series, 2021, 1748(4).
Fan P, Shen H M, Zhao C, Wei Z, Yao J G. ZhouZ Q. FuR, Hu Q. Defect Identification Detection Research for Insulator of Transmission Lines Based on Deep Learning[J]. Journal of Physics: ConferenceSeries,2021,1828(1).
Wang Wanguo, Tian Bing, Liu Yue, et al. Research on power component recognition of UAV inspection image based on RCNN [] Journal of Geo-Information Science, 2017, 19(2) : 256-263.
Chen Jingwen, Zhou Xin, Zhang Rong, et al. Aerial photo insulator detection based on U-net network [J]. Journal of Shaanxi University of Science and Technology, 2018, 36(4): 153-157.
Du Fenglin. Application of Object Detection Algorithm Based on Deep Learning [D]. Hefei: Anhui University,2018.
Woo S, Park J, Lee JY, et al. CBAM: Convolutional block attention module. Proceedings of the 15th European Conference on Computer Vision. Munich: Springer, 2018.3-19.
Tan MX, Pang RM, Le QV. Efficient Det: Scalable and efficient object detection. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Seattle: IEEE, 2020. 10778-10787.
Lin TY, Dollar P, Girshick R, et al. Feature pyramid networks for object detection. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu: IEEE, 2017.936- 944.
Liu S, Qi L, Qin HF, et al. Path aggregation network for instance segmentation. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018. 8759- -8768.
Zhao QJ, Sheng T, Wang YT, et al. M2Det: A single-shot object detector based on multi-level feature pyramid network. Proceedings of the 33rd AAAI Conference on Artificial. Intelligence. Honolulu: AAAI, 2019.9259- 9266.
Ghiasi G, Lin TY, Le QV. NAS-FPN: Learning scalable feature pyramid architecture for object detection. 2019 IEEE/CVF Conference on Computer V ision and Pattern Recognition. Long Beach: IEEE, 2019.7029-7038.
Bai HY, Wen S, Chan SHG. Crowd counting on images with scale variation and isolated clusters. 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW). Seoul: IEEE, 2019. 18- -27.
Downloads
Published
Issue
Section
How to Cite
