Lightweight YOLOx-based transmission line insulators and their defects detection

Abstract

A lightweight network model based on YOLOx is proposed for the problems of limited resources of transmission line UAV inspection platform, high complexity of target detection algorithm and slow inference speed. First, the lightweight ShuffleNetV2_Plus network is used as the backbone network for feature extraction, and the Depthwise Convolution (DWConv) in the ShuffleNetV2 network is expanded by replacing 3×3DWConv in the ShuffleUnit module with 5×5DWConv in the ShuffleUnit module, and prune the convolution layer of the model, and prune the 1×1Pointwise Convolution (PWConv) in the ShuffleUnit basic unit module to reduce the network parameters while increasing the network perceptual field. At the same time, add the Efficient Channel Attention (ECA) module in the neck feature fusion part to make the network better focus on important regions and improve the target detection accuracy at a small computational cost. Finally, the ordinary convolution in the YOLOx detection decoupling head is replaced with Depthwise Separable Convolution (DSConv) to further reduce the model complexity. The results show that the inference time of the lightweight network model proposed in this paper is only 5.8ms, the model parameters are only 4.361MB, and the FLOPs are only 10.725G, and the detection accuracy is high on the combined self-built transmission line dataset.