[1]FENG Han,JIANG Yong.A substation insulator string detection method based on an improved Yolov5[J].CAAI Transactions on Intelligent Systems,2023,18(2):325-332.[doi:10.11992/tis.202201027]
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A substation insulator string detection method based on an improved Yolov5

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 18 Number of periods: 2023 2 Page number: 325-332 Column: 学术论文—机器感知与模式识别 Public date: 2023-05-05
Title:
A substation insulator string detection method based on an improved Yolov5
Author(s):
FENG Han1; 2; 3; 4;  JIANG Yong2; 3; 4
1. School of Information Science and Engineering, Northeastern University, Shenyang 110006, China;
2. Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China;
3. Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China;
4. Institute for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
Keywords:
Yolov5; insulator; attention mechanism; backlighting environment; computer vision; deep learning; artificial intelligence; target detection
CLC:
TP391.4
DOI:
10.11992/tis.202201027
Abstract:
An improved method for detecting insulators, based on Yolov5, is proposed to address the challenge faced by substation insulator string water rinsing robots in accurately identifying insulators in complex lighting conditions. To overcome the issue of poor image quality in backlight environments causing algorithm failure, the data enhancement process includes the use of a simulated overexposure enhancement algorithm. The network’s Neck is optimally cropped to improve the inference speed for the substation insulator detection task, and the attention mechanism is utilized to address the accuracy degradation after cropping. Results show that the improved Yolov5 has improved the inference speed by 25% while maintaining the same detection accuracy and achieved significant improvement in the detection accuracy for images under backlighting.
References:
[1] 李静, 许鹏娟, 刘树鑫, 等. 复合绝缘子污闪动态发展过程与影响因素[J]. 沈阳工业大学学报, 2019, 41(5): 481–488
LI Jing, XU Pengjuan, LIU Shuxin, et al. Dynamic development process and influencing factors of pollution flashover for composite insulators[J]. Journal of Shenyang University of Technology, 2019, 41(5): 481–488
[2] 郑伟, 郑新才, 黄梅, 等. 输变电设备防污闪措施比较和分析[J]. 电气应用, 2010, 29(5): 68–70
ZHENG Wei, ZHENG Xincai, HUANG Mei, et al. Comparison and analysis of anti-flash measures for transmission and substation equipment[J]. Electrical applications, 2010, 29(5): 68–70
[3] 白雪莲, 杨玲. 浅谈输变电设备的防污闪措施[J]. 中国电子商务, 2010(4): 167–167
BAI Xuelian, YANG Ling. Introduction to anti-fouling flash measures for transmission and substation equipment[J]. China electronic commerce, 2010(4): 167–167
[4] 翟永杰, 王迪, 张木柳. 基于聚类分析和Adaboost算法的绝缘子串识别[J]. 传感器世界, 2016, 22(9): 7–11
ZHAI Yongjie, WANG Di, ZHANG Muliu. Insulator recognition based on clustering analysis and Adaboost algorithm[J]. Sensor world, 2016, 22(9): 7–11
[5] 陈文浩, 姚利娜, 李丰哲. 无人机电网巡检中的绝缘子缺陷检测与定位[J]. 计算机应用, 2019, 39(S1): 210–214
CHEN Wenhao, YAO Lina, LI Fengzhe. Insulator defect detection and location in UAV grid inspection[J]. Journal of computer applications, 2019, 39(S1): 210–214
[6] 方挺, 董冲, 胡兴柳, 等. 航拍图像中绝缘子串的轮廓提取和故障检测[J]. 上海交通大学学报, 2013, 47(12): 1818–1822
FANG Ting, DONG Chong, HU Xingliu, et al. Contour extraction and fault detection of insulator strings in aerial images[J]. Journal of Shanghai Jiao Tong University, 2013, 47(12): 1818–1822
[7] 胡淋波, 姚建刚, 孔维辉, 等. 基于红外图像的高压绝缘子串自动定位方法[J]. 红外技术, 2015, 37(12): 1047–1051
HU Linbo, YAO Jiangang, KONG Weihui, et al. High voltage insulator string automatic location method based on infrared image[J]. Infrared technology, 2015, 37(12): 1047–1051
[8] 姚建刚, 付鹏, 李唐兵, 等. 基于红外图像的绝缘子串自动提取和状态识别[J]. 湖南大学学报(自然科学版), 2015, 42(2): 74–80
YAO Jiangang, FU Peng, LI Tangbing, et al. Algorithm research of automatically extracting the area of insulator from infrared image and state identification[J]. Journal of Hunan university (natural sciences edition), 2015, 42(2): 74–80
[9] 潘翀, 沈鹏飞, 张忠, 等. 基于无人机巡检图像的绝缘子串实时定位研究[J]. 电瓷避雷器, 2020(1): 234–240
PAN Chong, SHEN Pengfei, ZHANG Zhong, et al. Research on real-time positioning of insulator strings based on UAV inspection images[J]. Insulators and surge arresters, 2020(1): 234–240
[10] 刘澈, 姜勇, 姚森敬, 等. 基于多传感器信息融合的绝缘子串自动识别方法[J]. 机械设计与制造, 2019(4): 220–224
LIU Che, JIANG Yong, YAO Senjing, et al. The method to automatic recognition of insulator based on multi-sensor information fusion[J]. Machinery design & manufacture, 2019(4): 220–224
[11] 翟永杰, 王迪, 赵振兵, 等. 基于空域形态一致性特征的绝缘子串定位方法[J]. 中国电机工程学报, 2017, 37(5): 1568–1578
ZHAI Yongjie, WANG Di, ZHAO Zhenbing, et al. Insulator string location method based on spatial configuration consistency feature[J]. Proceedings of the csee, 2017, 37(5): 1568–1578
[12] 沈新平, 彭刚, 袁志强. 基于霍夫变换和RANSAC算法的绝缘子定位方法[J]. 电子测量技术, 2017, 40(6): 132–137
SHEN Xinping, PENG Gang, YUAN Zhiqiang. Insulator location method based on Hough transformation and RANSAC algorithm[J]. Electronic measurement technology, 2017, 40(6): 132–137
[13] 颜宏文, 陈金鑫. 基于改进YOLOv3的绝缘子串定位与状态识别方法[J]. 高电压技术, 2020, 46(2): 423–432
YAN Hongwen, CHEN Jinxin. Insulator string positioning and state recognition method based on improved YOLOv3 algorithm[J]. High voltage engineering, 2020, 46(2): 423–432
[14] HUANG X, SHANG E, XUE J, et al. A Multi-feature fusion-based deep learning for insulator image identification and fault detection[C]//2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference. Chongqing: IEEE, 2020: 1957-1960.
[15] GAO Feng, WANG Jiao, KONG Zhizhan, et al. Recognition of insulator explosion based on deep learning[C]//2017 14th International Computer Conference on Wavelet Active Media Technology and Information Processing. Chengdu: VESTC Press, 2017 : 79?82.
[16] TAO Xian, ZHANG Dapeng, WANG Zihao, 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, 2020, 50(4): 1486–1498.
[17] WANG C Y, LIAO H Y M, WU Y H, et al. CSPNet: a new backbone that can enhance learning capability of CNN[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Seattle: IEEE, 2020 : 1571?1580.
[18] HU Jie, SHEN Li, SUN Gang. Squeeze-and-excitation networks[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 7132?7141.
[19] WANG Qilong, WU Banggu, ZHU Pengfei, et al. ECA-net: efficient channel attention for deep convolutional neural networks[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle: IEEE, 2020: 11531?11539.
[20] LIN T Y, DOLLáR P, GIRSHICK R, et al. Feature pyramid networks for object detection[C]//IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017: 936?944.
[21] LI Hanhao, XIONG Pengfei, WANG Lingxue. Pyramid Attention Network for Semantic Segmentation[C]// Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 1805?1818.
[22] ZHU Linlin, GENG Xun, LI Zheng, et al. Improving YOLOv5 with attention mechanism for detecting boulders from planetary images[J]. Remote sensing, 2021, 13(18): 3776.
[23] 徐诚极, 王晓峰, 杨亚东. Attention-YOLO: 引入注意力机制的YOLO检测算法[J]. 计算机工程与应用, 2019, 55(6): 13–23,125
XU Chengji, WANG Xiaofeng, YANG Yadong. Attention-YOLO: YOLO detection algorithm that introduces attention mechanism[J]. Computer engineering and applications, 2019, 55(6): 13–23,125
[24] LIU Wei, ANGUELOV D, ERHAN D, et al. SSD: Single Shot MultiBox Detector[C]//European Conference on Computer Vision. Cham: Springer, 2016: 21?37.
[25] BOCHKOVSKIY A, WANG C Y, LIAO H Y M. Yolov4: optimal speed and accuracy of object detection[C]// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle: IEEE, 2020: 3370?3387.
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