Affiliation:
1. Urban Rail Transit and Logistics College, Beijing Union University, Beijing 100101, China
2. China Academy of Railway Sciences Corporation Limited, Institute of Computing Technology, Beijing 100081, China
Abstract
High-speed railway catenaries are vital components in railway traction power supply systems. To ensure stable contact between the pantograph and the catenary, droppers are positioned between the messenger wire and contact line. The failure of one or more droppers will affect the power supply of the catenary and the operation of the railway. In this paper, we modify the You Only Look Once version five (YOLOv5) model in several ways and propose a method for improving the identification of dropper status and the detection of small defects. Firstly, to focus on small target features, the selective kernel attention module is added to the backbone. Secondly, the feature graphs of different scales extracted from the backbone network are fed into the bidirectional feature pyramid network for multiscale feature fusion. Thirdly, the YOLO head is replaced by a decoupled head to improve the convergence speed and detection accuracy of the model. The experimental results show that the proposed model achieves a mean average precision of 92.9% on the dropper dataset, an increase of 3.8% over the results using YOLOv5s. The detection accuracy of small dropper defects reaches 79.2%, representing an increase of 10.8% compared with YOLOv5s and demonstrating that our model is better at detecting small defects.
Funder
Beijing Natural Science Foundation
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
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