Insulators and Their Breakage Detection Based on Improved YOLOv5s
LI Jin
WANG Lingtao
ZHANG Pengpeng
Abstract：As one of the important components of overhead lines,insulators are used to maintain good insulation between the live body and the pole tower.However,insulators are exposed to the natural environment all year round and have a high probability of failure.In view of the problem of identifying insulator damage that needs to be solved urgently due to the damage of insulators af-fected by environmental external forces and thus affecting the safety and stability of transmission line operation,on the basis of the object detection algorithm YOLOv5s(You Only Look Once v5s),the convolution in the C3 module in the network is replaced with a multi-head self-attention layer(MHSA)to improve the algorithm's attention to the global information of the image.The attention mechanism CBAM(Convolutional Block Attention Module)is introduced to enhance the fusion of attention features of the target in the dual dimensions of channel and space.This paper replaces the loss function of the network with SIoU(Scylla Intersection over Union),introduces directionality into the cost of the loss function,and improves the training and inference performance of the algo-rithm.The experimental results show that compared with the original YOLOv5s,the improved model not only ensures the accuracy and detection speed,but also reduces the number of model parameters,increases the recall rate by 13.3%,increases the average accuracy by 7.5%,and significantly improves the recognition effect,which effectively solves the problem caused by insulator fault identification.
Keywords：breakage of insulatorsYOLOv5sattention mechanismloss functionmulti-head self-attention layer
Publication Date：2025-12-20
Online Publishing Date：2026-03-09(First online date of this platform, not the publication date of the document)
Pages：8( 3582-3589 )