Research on Insulator Target Detection Based on Convolutional Neural Network
LIAO Xiaoqun
LI Yuan
SHEN Shujian
ZHANG Hanjin
FU Wenxu
LIU Shuai
Abstract：The identification and positioning of power grid insulators is the premise for effective detection of power grid opera-tion status.Based on the composite insulator images of transmission lines captured by drones,in order to solve the problems of high safety factor and low operation efficiency of traditional manual inspection of transmission lines,an improved method is proposed,which is convolutional neural network insulator device research recognition algorithm.By adding attention mechanism CBAM and SENet,the algorithm can effectively improve the positioning of transmission line devices.Experimental results show that the im-proved model reduces execution time compared to previous versions,and the target detection accuracy increases by 8.2%.This method significantly enhances the performance and robustness of the system for detecting insulators on transmission lines,providing more effective results compared to existing algorithms.
Keywords：insulatorconvolutional neural networkattention mechanismFaster RCNN
Publication Date：2025-02-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 409-414,516 )