Novel Vision-Based Abnormal Behavior Localization of Pantograph-Catenary for High-Speed Trains

Abstract

To ensure the safe operation of high-speed trains, catenary anomaly detection and alerting security have become an urgent problem to solve. In this paper, we propose a novel method for abnormal behavior localization of a pantograph-catenary for high-speed trains. First, a modified faster RCNN is proposed to detect the pantograph faults. By adjusting the parameters of the faster RCNN, the positional accuracy of the candidate box and accuracy of the algorithm are guaranteed. We perform the arc detection after detecting the pantograph head area. The detection accuracy is over 99%. The height of the pantograph center point is also obtained during detection. Then, the actual running mileage of the fault point is calculated. Experiments show that the method proposed in this paper is also applicable to various complex scenes and that this method can determine the fault localization in the shortest time, narrow the maintenance scope, and improve the overhaul efficiency.

Keywords

• Deep learning
• faster RCNN
• fault localization
• Pantograph detection