RD-YOLO: Towards Rust Defect Detection for Future Unmanned Transmission Lines Maintenance

Abstract

As a common type of defect, the rust defect of power components is one of the important potential hazards endangering the safe operation of transmission lines. How to quickly and accurately discover and repair the rusted power components is an urgent problem to be solved in power inspection. Aiming at the above problems, this study proposes Rust-Defect YOLO (RD-YOLO) for detecting rust defects in power components of transmission lines. Firstly, the Coordinate Channel Attention Residual Module (CCARM) is proposed to improve the multi-scale detection precision. Secondly, the Receptive Field Block (RFB) and the Efficient Convolutional Block Attention Module (ECBAM) are introduced into the Path Aggregation Network (PANet) to strengthen the fusion of deep and shallow features. Finally, the contrast sample strategy and the Focal loss function are adopted to train and optimize RD-YOLO, and experiments are carried out on a self-built dataset. The experimental results show that the average precision of rust defect detection by RD-YOLO reaches 95%, which is 9% higher than that of the original YOLOX. The comparative experimental results demonstrate that RD-YOLO performs excellently in power components identification and rust defect detection, and has broad application prospects in the future automatic visual inspection of transmission lines.