Deformable YOLOX: Detection and Rust Warning Method of Transmission Line Connection Fittings Based on Image Processing Technology

Abstract

Dense distribution and significant size difference of transmission line connecting fittings are difficult to maintain, and long-term exposure to the outdoor environment is vulnerable to adverse environmental effects of rust failure. The common image processing methods and deep learning algorithms are not competent for this kind of dense small-target detection task, so the target detection model based on an image processing hierarchical algorithm is proposed in this article, which uses anchor-free and decoupled head design ideas, through ASFF multiscale information feature fusion strategy and ECA + VariFocal Loss interactive saliency area capture strategy to construct a dense small-target detection network suitable for a complex environment. The experimental results show that the comprehensive performance of Deformable YOLOX is superior to 13 current advanced target detection algorithms. Compared with the baseline model, Deformable YOLOX can better understand the multiscale semantic information of the image and learn the small details that are more difficult to distinguish. Combined with a target detection algorithm, an early warning algorithm for rust grade assessment of connecting fittings is proposed, and an online monitoring system is designed, which has practical engineering application value.