Transmission Line Component Defect Detection Based on UAV Patrol Images: A Self-Supervised HC-ViT Method

Abstract

The unmanned aerial vehicle (UAV) patrol inspection has become an efficient method to ensure the operation condition of transmission lines. The detection of key components with defects in transmission lines is a critical task in maintaining a power system’s stability. However, the complex inspection environment and the imbalance between the number of normal component samples and that of defect samples significantly affect the detection accuracy. In this article, we present a novel method for defect detection in UAV patrol images, based on a hierarchical convolutional vision transformer (HC-ViT) and a simple contrastive masked autoencoder (SC-MAE). The HC-ViT backbone integrates the advantages of vision transformer and convolution, while the SC-MAE is a self-supervised learning method that extracts useful features from normal samples. By introducing the normal features into the backbone, we enhance the performance of the defect detection task. We demonstrate the effectiveness of our method through experiments, and show that it can leverage a large amount of unlabeled normal images, reducing the need for manual annotation. Our method offers a new way to exploit the potential features of patrol inspection images.