Abstract: 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 features, 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 improve 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 convolution 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 acyclic 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 accuracy and light weight.