Novel Vision-Based Abnormal Behavior Localization of Pantograph-Catenary for High-Speed Trains
| Autor: | Qianru Yang, Yiping Luo, Scarlett Liu |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
General Computer Science
Vision based Computer science Real-time computing General Engineering Pantograph detection Deep learning faster RCNN 02 engineering and technology 010501 environmental sciences Fault (power engineering) 01 natural sciences fault localization Catenary 0202 electrical engineering electronic engineering information engineering Pantograph 020201 artificial intelligence & image processing General Materials Science Point (geometry) Train Anomaly detection lcsh:Electrical engineering. Electronics. Nuclear engineering Abnormality lcsh:TK1-9971 0105 earth and related environmental sciences |
| Zdroj: | IEEE Access, Vol 7, Pp 180935-180946 (2019) |
| ISSN: | 2169-3536 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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