Infrared Image Detection of Overheated Insulation Bushing in Substations Based on Improved YOLOv7 Algorithm
XIAO Tianlong
HE Xinyi
LI Yun
ZHU Li
Abstract：To address the issue of insufficient accuracy in the detection of infrared images of overheated insulators in substations,a detection technology based on the improved you only look once version 7(YOLOv7)algorithm was proposed.The network structure was optimized by introducing an improved cross stage partial network ghost version 3(C3Ghost)module to replace the extended efficient layer aggregation network(E-ELAN)module in the head network,thereby enhancing the model′s ability to recognize small targets.Additionally,a lightweight normalization-based attention module(NAM)was integrated into the backbone network to improve the efficiency of infrared image feature utilization.Furthermore,all convolutions in the network were replaced with ghost convolution(GhostConv)modules,significantly reducing the model size.The results indicated that,compared to the original YOLOv7 algorithm,the improved YOLOv7 algorithm increased the F1 score and mean average precision by 19.51%and 16.57%,respectively,while reducing the model parameters by 16.3 MB and achieving a detection speed of 41 frame/s,which fully demonstrated its effectiveness in practical substation applications.This research not only significantly improved the infrared image detection accuracy of overheated insulation bushing in substations but also provided a reference for subsequent studies in related technologies.
Keywords：C3Ghost moduleE-ELAN moduleGhostConvsmall object recognitionobject detectionNAM module
Publication Date：2024-09-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 349-354 )