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Volume 45 Issue 11
Nov.  2023
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HU Taishan, LIU Hao, LIU Gang, MEI Qi, MA Yutang, LIAO Minchuan. Infrared Image Fault Detection Method of Arrester Based on Improved YOLOv3[J]. Infrared Technology , 2023, 45(11): 1256-1261.
Citation: HU Taishan, LIU Hao, LIU Gang, MEI Qi, MA Yutang, LIAO Minchuan. Infrared Image Fault Detection Method of Arrester Based on Improved YOLOv3[J]. Infrared Technology , 2023, 45(11): 1256-1261.
shu

Infrared Image Fault Detection Method of Arrester Based on Improved YOLOv3

  • 1.

    State Key Laboratory of HVDC, Electric Power Research Institute, CSG, Guangzhou 510663, China

  • 2.

    Electric Power Research Institute of Yunnan Power Grid Co., Ltd, Kunming 650000, China

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  • Received Date: June 06, 2021
  • Revised Date: August 19, 2021
    Graphical Abstract
    • Abstract
  • Aiming at the problems of low recognition accuracy and slow detection speed of existing metal oxide arrester (MOA) infrared image fault detection methods, a MOA infrared image fault detection method based on improved YOLOv3 is proposed. Firstly, darknet19 network is used to replace the original darknet53 network of YOLOv3. During feature learning, the target frames in MOA images are analyzed by K-means clustering algorithm according to different MOA length width ratios in samples. The anchor frames in the center of samples are re clustered to get the appropriate number and size of anchor frames. Finally, the improved YOLOv3 model is used to complete the MOA infrared image fault detection. The experimental results show that the recognition accuracy of the improved model reaches 96.3%, and the recognition speed is 6.75ms.
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    • References (20)
  • [1]
    任大江, 叶海鹏, 李建萍, 等. 一起500 kV金属氧化锌避雷器故障原因分析[J]. 电瓷避雷器, 2020(3): 127-132. https://www.cnki.com.cn/Article/CJFDTOTAL-DCPQ202302023.htm

    REN Dajiang, YE Haipeng, LI Jianping, et al. Analysis of the causes of a 500 kV metal zinc oxide arrester fault[J]. Insulators and Surge Arresters, 2020(3): 127-132. https://www.cnki.com.cn/Article/CJFDTOTAL-DCPQ202302023.htm
    [2]
    张明龙, 钱健, 王健, 等. 基于配电避雷器全电流监测的系统过电压告警研究[J]. 高压电器, 2020, 56(8): 256-260, 267. DOI: 10.13296/j.1001-1609.hva.2020.08.039

    ZHANG Minglong, QIAN Jian, WANG Jian, et al. Research on over-voltage alarm based on full current monitoring of distribution arrester[J]. High Voltage Apparatus, 2020, 56(8): 256-260, 267. DOI: 10.13296/j.1001-1609.hva.2020.08.039
    [3]
    何贵先, 行鸿彦, 季鑫源, 等. 金属氧化物避雷器在线监测的谐波校正及研究[J]. 电子测量与仪器学报, 2017, 31(10): 1549-1554. https://www.cnki.com.cn/Article/CJFDTOTAL-DZIY201710004.htm

    HE Guixian, XING Hongyan, JI Xinyuan, et al. Harmonic correction and study for MOA on-line monitoring algorithm[J]. Journal of Electronic Measurement and Instrumentation, 2020(3): 127-132. https://www.cnki.com.cn/Article/CJFDTOTAL-DZIY201710004.htm
    [4]
    陈登义, 周雪会, 李鹏. 电涌保护器老化监测指标对比研究[J]. 电瓷避雷器, 2016(5): 90-93. https://www.cnki.com.cn/Article/CJFDTOTAL-DCPQ201605017.htm

    CHEN Dengyi, ZHOU Xuehui, LI Peng, et al. Comparative study about index of SPD aging monitoring[J]. High Voltage Apparatus, 2016(5): 90-93. https://www.cnki.com.cn/Article/CJFDTOTAL-DCPQ201605017.htm
    [5]
    陈丹, 傅中君, 柳益君, 等. 一种氧化锌避雷器阻性电流的提取方法[J]. 电测与仪表, 2019, 56(13): 117-122. https://www.cnki.com.cn/Article/CJFDTOTAL-DCYQ201913021.htm

    CHEN Dan, FU Zhongjun, LIU Yijun, et al. A method for the resistive current extraction of metal oxide surge arresters[J]. Electrical Measurement & Instrumentation, 2019, 56(13): 117-122. https://www.cnki.com.cn/Article/CJFDTOTAL-DCYQ201913021.htm
    [6]
    李艳鹏, 晋涛, 梁基重, 等. 红外技术在避雷器绝缘缺陷检测中的应用[J]. 电测与仪表, 2019, 56(20): 87-90. https://www.cnki.com.cn/Article/CJFDTOTAL-DCYQ201920015.htm

    LI Yanpeng, JIN Tao, LIANG Jichong, et al. The application of infrared diagnosis in detection of lightning arrester insulation defects[J]. Electrical Measurement & Instrumentation, 2019, 56(20): 87-90. https://www.cnki.com.cn/Article/CJFDTOTAL-DCYQ201920015.htm
    [7]
    李红光, 于若男, 丁文锐. 基于深度学习的小目标检测研究进展[J/OL]. 航空学报: 1-19, [2020-12-19]. http://kns.cnki.net/kcms/detail/11.1929.V.20201026.0947.004.html.

    LI Hongguang, YU Ruonan, DING Wenrui. Research development of small object tracking based on deep learning[J/OL]. Acta Aeronautica et Astronautica Sinica: 1-19, [2020-12-19]. http://kns.cnki.net/kcms/detail/11.1929.V.20201026.0947.004.html.
    [8]
    黄继鹏, 史颖欢, 高阳. 面向小目标的多尺度Faster-RCNN检测算法[J]. 计算机研究与发展, 2019, 56(2): 319-327. https://www.cnki.com.cn/Article/CJFDTOTAL-JFYZ201902008.htm

    HUANG Jipeng, SHI Yinhuan, GAO Yang. Multi-scale faster-RCNN algorithm for small object detection[J]. Journal of Computer Research and Development, 2019, 56(2): 319-327. https://www.cnki.com.cn/Article/CJFDTOTAL-JFYZ201902008.htm
    [9]
    赵振兵, 崔雅萍, 戚银城, 等. 基于改进的R-FCN航拍巡线图像中的绝缘子检测方法[J]. 计算机科学, 2019, 46(3): 159-163. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJA201903024.htm

    ZHAO Zhenbing, CUI Yaping, QI Yincheng, et al. Detection method of insulator in aerial inspection image based on modified R-FCN[J]. Computer Science, 2019, 46(3): 159-163. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJA201903024.htm
    [10]
    李文璞, 谢可, 廖逍, 等. 基于Faster RCNN变电设备红外图像缺陷识别方法[J]. 南方电网技术, 2019, 13(12): 79-84. https://www.cnki.com.cn/Article/CJFDTOTAL-NFDW202110012.htm

    LI Wenpu, XIE Ke, LIAO Yao, et al. Intelligent diagnosis method of infrared image for transformer equipment based on improved faster RCNN[J]. Southern Power System Technology, 2019, 13(12): 79-84. https://www.cnki.com.cn/Article/CJFDTOTAL-NFDW202110012.htm
    [11]
    刘云鹏, 裴少通, 武建华, 等. 基于深度学习的输变电设备异常发热点红外图片目标检测方法[J]. 南方电网技术, 2019, 13(2): 27-33. https://www.cnki.com.cn/Article/CJFDTOTAL-NFDW201902006.htm

    LIU Yunpeng, PEI Shaotong, WU Jianhua, et al. Deep learning based target detection method for abnormal hot spots infrared images of transmission and transformation equipment[J]. Southern Power System Technology, 2019, 13(2): 27-33. https://www.cnki.com.cn/Article/CJFDTOTAL-NFDW201902006.htm
    [12]
    赵琰, 刘荻, 赵凌君. 基于YOLOv3的复杂环境红外弱小目标检测[J]. 航空兵器, 2019, 26(6): 29-34. https://www.cnki.com.cn/Article/CJFDTOTAL-HKBQ201906006.htm

    ZHAO Yan, LIU Di, ZHAO Lingjun. Infrared dim and small target detection based on YOLOv3 in complex environment[J]. Aero Weaponry, 2019, 26(6): 29-34. https://www.cnki.com.cn/Article/CJFDTOTAL-HKBQ201906006.htm
    [13]
    罗元, 王薄宇, 陈旭. 基于深度学习的目标检测技术的研究综述[J]. 半导体光电, 2020, 41(1): 1-10. https://www.cnki.com.cn/Article/CJFDTOTAL-BDTG202001001.htm

    LUO Yuan, WANG Boyu, CHEN Xu. Research progress of target detection technology based on deep learning[J]. Semiconductor Optoelectronics, 2020, 41(1): 1-10. https://www.cnki.com.cn/Article/CJFDTOTAL-BDTG202001001.htm
    [14]
    王永平, 张红民, 彭闯, 等. 基于YOLO v3的高压开关设备异常发热点目标检测方法[J]. 红外技术, 2020, 42(10): 983-987. http://hwjs.nvir.cn/article/id/hwjs202010011

    WANG Yongping, ZHANG Hongmin, PENG Chuang, et al. The target detection method for abnormal heating point of high-voltage switchgear based on YOLO v3[J]. Infrared Technology, 2020, 42(10): 983-987. http://hwjs.nvir.cn/article/id/hwjs202010011
    [15]
    王芳, 李传强, 伍博, 等. 基于多尺度特征融合的红外小目标检测方法[J]. 红外技术, 2021, 43(7): 688-695. http://hwjs.nvir.cn/article/id/483e7824-ea0f-4381-96b7-22257615d475

    WANG Fang, LI Chuanqiang, WU Bo, et al. Infrared small target detection method based on multi-scale feature fusion[J]. Infrared Technology, 2021, 43(7): 688-695. http://hwjs.nvir.cn/article/id/483e7824-ea0f-4381-96b7-22257615d475
    [16]
    刘云鹏, 张喆, 裴少通, 等. 基于深度学习的红外图像中劣化绝缘子片的分割方法[J]. 电测与仪表, 2022, 59(9): 63-68. https://www.cnki.com.cn/Article/CJFDTOTAL-DCYQ202209009.htm

    LIU Yunpeng, ZHANG Zhe, PEI Shaotong, et al. Faulty insulator segmentation method in infrared image based on deep learning[J]. Electrical Measurement & Instrumentation, 2022, 59(9): 63-68. https://www.cnki.com.cn/Article/CJFDTOTAL-DCYQ202209009.htm
    [17]
    中华人民共和国国家发展和改革委员会. 带电设备红外诊断应用规范[S]. DL/T 664-2008, [2008-11-01].

    National Development and Reform Commission. Application Rules of infrared diagnosis for live electrical equipment[S]. DL/T 664-2008, [2008-11-01].
    [18]
    汪烈兵, 姜雄飞, 石春光, 等. 基于图像滤波与Hough变换的红外弱小目标检测[J]. 红外技术, 2020, 42(7): 683-687. http://hwjs.nvir.cn/article/id/hwjs202007012

    WANG Liebing, JIANG Xiongfei, SHI Chunguang, et al. Infrared small target detection based on image filtering and hough transform[J]. Infrared Technology, 2020, 42(7): 683-687. http://hwjs.nvir.cn/article/id/hwjs202007012
    [19]
    Redmon J, Farhadi A. YOLOv3: An incremental improvement [J/OL]//Computer Science, 2018 https://arxiv.org/abs/1804.02767.
    [20]
    CHENG G, LIU L. Survey of image segmentation methods based on clustering[C]//IEEE International Conference on Information Technology (ICIBA), 2020: 1111-1115.
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