Transmission Line Insulator Defect Detection Based on Swin Transformer and Context

Abstract

Insulators are important components of power transmission lines. Once a failure occurs, it may cause a large-scale blackout and other hidden dangers. Due to the large image size and complex background, detecting small defect objects is a challenge. We make improvements based on the two-stage network Faster R-convolutional neural networks (CNN). First, we use a hierarchical Swin Transformer with shifted windows as the feature extraction network, instead of ResNet, to extract more discriminative features, and then design the deformable receptive field block to encode global and local context information, which is utilized to capture key clues for detecting objects in complex backgrounds. Finally, the filling data augmentation method is proposed for the problem of insufficient defects and more images of insulator defects under different backgrounds are added to the training set to improve the robustness of the model. As a result, the recall increases from 89.5% to 92.1%, and the average precision increases from 81.0% to 87.1%. To further prove the superiority of the proposed algorithm, we also tested the model on the public data set Pascal visual object classes (VOC), which also yields outstanding results.