Defect Identification of Distribution Components Based on Improved YOLOv5-LITE Lightweight
In order to accurately and quickly locate and identify the defects of distribution components,a lightweight defect identification method of distribution components based on improved YOLOv5-LITE is proposed.To make the model easy to deploy to mobile device terminals,this method uses Shufflenetv2 as the backbone network to extract fea-tures,constructs YOLOv5-LITE lightweight neural network model,and removes 1024 convolution and 5×5 Pooling of Shufflenetv2,which is replaced by global average pooling operation to reduce the amount of network parameters and im-prove the speed of model detection.By introducing the 152×152 feature layer,which is conducive to the detection of fine-grained objects,the defect detection of large-,medium-and small-scales is realized.Using deep separable convolu-tion instead of downsampling in PANet architecture makes the network more lightweight.The experimental results show that this method can be adopted to identify three defects:cable separation gasket,cable and insulator falling off and acy-clic insulator.The detection accuracy is 92%,95%,and 95%,respectively.The amount of network parameters is about 1/4 of YOLOv5,and the detection speed is 2 ms/piece.The proposed method has the characteristics of real-time,high accu-racy and light weight.
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