基于改进Faster RCNN的输电线路航拍绝缘子检测

doi:10.19678/j.issn.1000-3428.0059872

摘要/Abstract

摘要： 为提高航拍图像中输电线路绝缘子的检测准确性，提出一种改进的Faster RCNN网络模型。在原始Faster RCNN网络模型上运用多尺度训练，同时根据绝缘子自身特性调整滑动窗口产生的候选区域比例，并引入检测困难样本的对手生成策略，实现不同尺寸及部分遮挡输电线路绝缘子的准确检测。实验结果表明，改进的Faster RCNN网络模型相比原始Faster RCNN网络模型的检测精确度提升了4.33个百分点，能更准确地检测出目标绝缘子。

关键词: 电力巡检, 绝缘子, 目标检测, 多尺度训练, 对手生成策略

Abstract: To improve the detection accuracy of transmission line insulators in aerial images, this paper proposes an improved Faster RCNN model.The method applies multi-scale training to the original Faster RCNN model, and adjusts the proportion of the candidate regions generated by sliding window according to the characteristics of insulators.In addition, an opponent generation strategy for detecting difficult samples is introduced to realize accurate detection of insulators for different sizes and partial shielded transmission lines.Experimental results show that the accuracy of the improved Faster RCNN model is 4.33 percentage points higher than that of the original Faster RCNN model, proving that the model can detect the target insulators more accurately.

Key words: power inspection, insulator, object detection, multiscale training, opponent generation strategy

中图分类号:

TP391

易继禹, 陈慈发, 龚国强. 基于改进Faster RCNN的输电线路航拍绝缘子检测[J]. 计算机工程, 2021, 47(6): 292-298,304.

YI Jiyu, CHEN Cifa, GONG Guoqiang. Aerial Insulator Detection of Transmission Line Based on Improved Faster RCNN[J]. Computer Engineering, 2021, 47(6): 292-298,304.

https://www.ecice06.com/CN/Y2021/V47/I6/292

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参考文献

[1] MIAO Xiangpeng.Identification and damage detection of catenary insulator based on image processing[D].Chengdu:Southwest Jiaotong University,2017.(in Chinese)苗向鹏.基于图像处理的接触网绝缘子识别与破损检测[D].成都:西南交通大学,2017.
[2] TANG Mingwen,DAI Lihao,LIN Chaohui,et al.Application of unmanned aerial vehicle in inspecting transmission lines[J].Electric Power,2013,46(3):35-38.(in Chinese)汤明文,戴礼豪,林朝辉,等.无人机在电力线路巡视中的应用[J].中国电力,2013,46(3):35-38.
[3] ZHANG Xiangquan,SU Jianjun.UAV inspection technology for overhead transmission lines[M].Beijing:China Electric Power Press,2016.(in Chinese)张祥全,苏建军.架空输电线路无人机巡检技术[M].北京:中国电力出版社,2016.
[4] WANG Wei,LIU Guohai.Image edge detection of the insulator[J].Microcomputer Information,2008,24(27):308-309,154.(in Chinese)王伟,刘国海.绝缘子图像的边缘检测[J].微计算机信息,2008,24(27):308-309,154.
[5] ZHAI Yongjie,WANG Di,ZHANG Muliu,et al.Fault detection of insulator based on saliency and adaptive morphology[J].Multimedia Tools and Applications,2017,76(9):12051-12064.
[6] YAO Chunyu,JIN Lijun,YAN Shujia.Recognition of insulator string in power grid patrol images[J].Journal of System Simulation,2012,24(9):1818-1822.(in Chinese)姚春羽,金立军,闫书佳.电网巡检图像中绝缘子的识别[J].系统仿真学报,2012,24(9):1818-1822.
[7] ZHANG Jingjing,HAN Jun,ZHAO Yabo,et al.Insulator recognition and defects detection based on shape perceptual[J].Journal of Image and Graphics,2014,19(8):1194-1201.(in Chinese)张晶晶,韩军,赵亚博,等.形状感知的绝缘子识别与缺陷诊断[J].中国图象图形学报,2014,19(8):1194-1201.
[8] XU Yaoliang,ZHANG Shaocheng,YANG Ning,et al.An algorithm to extract insulator image from aerial image[J].Journal of Shanghai University of Electric Power,2011,27(5):515-518.(in Chinese)徐耀良,张少成,杨宁,等.航拍图像中绝缘子的提取算法[J].上海电力学院学报,2011,27(5):515-518.
[9] WANG Yinli,YAN Bin.Vision based detection and location for cracked insulator[J].Computer Engineering and Design,2014,35(2):583-587.(in Chinese)王银立,闫斌.基于视觉的绝缘子"掉串"缺陷的检测与定位[J].计算机工程与设计,2014,35(2):583-587.
[10] LUO Huan,TIAN Xiang.The electricity equipment image detection research based on improved Canny operator[J].Electrical Measurement & Instrumentation,2014,51(10):77-81.(in Chinese)罗桓,田翔.基于改进Canny算子的电力设备图像检测研究[J].电测与仪表,2014,51(10):77-81.
[11] ZHAI Yongjie,WANG Di,ZHAO Zhenbing.Recognition method of insulator based on object proposals and structure research[J].Journal of North China Electric Power University(Natural Science Edition),2016,43(4):66-71,78.(in Chinese)翟永杰,王迪,赵振兵.基于目标建议与结构搜索的绝缘子识别方法[J].华北电力大学学报(自然科学版),2016,43(4):66-71,78.
[12] JIANG Huilan,CUI Hubao,LIU Fei,et al.High voltage transmission line fault classification based on fuzzy logic and support vector machines[J].Electric Power,2005,38(3):13-17.(in Chinese)姜惠兰,崔虎宝,刘飞,等.基于模糊逻辑和支持向量机的高压输电线路故障分类器[J].中国电力,2005,38(3):13-17.
[13] HOSANG J,BENENSON R,DOLLAR P,et al.What makes for effective detection proposals?[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2016,38(4):814-830.
[14] WANG Wanguo,TIAN Bing,LIU Yue,et al.Study on the electrical devices detection in UAV images based on region based convolutional neural networks[J].Journal of Geo-Information Science,2017,19(2):256-263.(in Chinese)王万国,田兵,刘越,等.基于RCNN的无人机巡检图像电力小部件识别研究[J].地球信息科学学报,2017,19(2):256-263.
[15] LI Junfeng,WANG Qinruo,LI Min.Electric equipment image recognition based on deep learning and random forest[J].High Voltage Engineering,2017,43(11):3705-3711.(in Chinese)李军锋,王钦若,李敏.结合深度学习和随机森林的电力设备图像识别[J].高电压技术,2017,43(11):3705-3711.
[16] MA Peng,FAN Yanfang.Small sample smart substation power equipment component detection based on deep transfer learning[J].Power Grid Technology,2020,44(3):1148-1159.(in Chinese)马鹏,樊艳芳.基于深度迁移学习的小样本智能变电站电力设备部件检测[J].电网技术,2020,44(3):1148-1159.
[17] REN S,HE K,GIRSHICK R,et al.Faster RCNN:towards real-time object detection with region proposal networks[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2015,38(6):1137-1149.
[18] GIRSHICK R,DONAHUE J,DARRELL T,et al.Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2013:1879-1886.
[19] GIRSHICK R.Fast RCNN[C]//Proceedings of IEEE International Conference on Computer Vision.Washington D.C.,USA:IEEE Press,2015:1440-1448.
[20] WANG X,SHRIVASTAVA A,GUPTA A.A-Fast-RCNN:hard positive generation via adversary for object detection[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2017:3039-3048.
[21] BYEON Y H,KWAK K C.A performance comparison of pedestrian detection using Faster RCNN and ACF[C]//Proceedings of the 6th IIAI International Congress on Advanced Applied Informatics.Washington D.C.,USA:IEEE Press,2017:858-863.
[22] RENMON J,FARHADI A.YOLOv3:an incremental[C]//Proceedings of 2018 IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2018:1-6.

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