The insulator is one of the components that are prone to failure in the power system,and the failure will seriously affect the power supply safety of the power network.Therefore,how to identify the faulty insulators has become an important condition for the safe operation of power network.However,the insulator is a small component in the power system,which causes poor results of the existing de-tection methods.To solve these problems,an YOLOv4-tiny based defect detection algorithm for insulators is presented.This algorithm ap-plies the effective channel attention network to the feature extraction network of YOLOv4-tiny,and significantly enhances the feature ex-traction capability of the backbone network.In the feature fusion stage,the original FPN is improved into a two-way feature pyramid structure of two feature fusion paths,which enables more full fusion between different scale features.Finally,choose Focal loss instead of the binary cross-entropy loss function as loss function,which can solve the problem of unbalanced number of positive and negative sam-ples in the detection process.The experimental results show that,the proposed algorithm improves the average classification accuracy and miss detection,and performs well.
insulatorsafety of power network operationYOLOv4-tinyfeature pyramidfocal loss
维普
万方数据
