基于改进YOLOv5的轻量化绝缘子缺陷检测算法A lightweight insulator defect detection algorithm based on the improved YOLOv5
季世超,曲星合,宋庆彬,肖杨明,缪正,李宇航,邹国平
JI Shichao,QU Xinghe,SONG Qingbin,XIAO Yangming,MIAO Zheng,LI Yuhang,ZOU Guoping
摘要(Abstract)：
无人机巡检已经成为当下输电线路巡检的主流方式，绝缘子缺陷的检测是无人机巡检中的重要环节。因此，提出了一种基于改进YOLOv5的轻量化绝缘子缺陷检测算法。首先，使用轻量型的Ghost卷积代替普通卷积；然后，使用重复加权BiFPN（双向特征金字塔网络）替换原特征提取网络，提高网络对不同尺度的特征提取能力；最后，引入CA（坐标注意力机制）提高了主干特征提取效率。实验结果表明，绝缘子检测的平均精度值提升了1.7个百分点，模型大小减少了13.1%，改进后的算法模型在提升检测精度的同时更加轻量化，可实现绝缘子缺陷的快速检测。
Unmanned aerial vehicle(UAV) inspections now have emerged as a predominant approach for the examination of transmission lines, with a pivotal focus on the detection of insulator defects. In this context, a lightweight insulator defect detection algorithm, founded on the improved YOLOv5, is introduced. Firstly, the lightweight Ghost convolution is employed to replace conventional convolution. Subsequently, the original feature extraction network is replaced with a repeated weighted bidirectional feature pyramid network(BiFPN) to bolster feature extraction capability across various scales. Finally, the coordinate attention(CA) mechanism is introduced to enhance the efficiency of backbone feature extraction. Experimental results reveal a significant 1.7% enhancement in the average precision of insulator detection, accompanied by a 13.1% reduction in the model's size. This refined algorithm model not only elevates detection accuracy but also streamlines its footprint, thereby enabling more efficient and rapid insulator defect detection.
关键词(KeyWords)：
输电线路;绝缘子;缺陷检测;YOLOv5
transmission line;insulator;defect detection;YOLOv5
基金项目(Foundation): 国网浙江省电力有限公司科技项目（2021-HUZJTKJ-06）
作者(Author):
季世超,曲星合,宋庆彬,肖杨明,缪正,李宇航,邹国平
JI Shichao,QU Xinghe,SONG Qingbin,XIAO Yangming,MIAO Zheng,LI Yuhang,ZOU Guoping
DOI: 10.19585/j.zjdl.202312008
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