Pantograph Spark Fault Detection using YOLO

Amenya Bright, Ding Jin


Pantograph-catenary is now the dominant form of current collection for modern electric trains because they can be used for higher voltages. Faults in pantograph-catenary systems threaten the operation and safety of railway transportation. They need to be continuously monitored and controlled to maintain safe transport. Pantograph may be damaged as a result of extreme weather conditions which can affect its normal operation, leading to failure of pantograph and overhead contact line systems. Poor contact between pantograph and overhead contact line causes thermal erosion to the wire. When the pantographs are exposed to air, they could deteriorate due to electrochemical reaction with the environment since they are made of metals. Movement of catenary lines and pantograph in high crosswinds has been found to cause the wire to be trapped in the pantograph. There is a serious issue regarding the quality of images generated by pantograph video monitoring system on high-speed railway trains which often shows inconsistencies of catenary faults. The application of traditional image processing and deep learning techniques have been unable to meet the requirements of spark detection. In this paper,  a modern deep learning algorithm is proposed to detect sparks in the pantograph. Specifically, the YOLOv3 model is used to counter this problem that traditional image processing algorithms have been unable to. The results on a very large sample of data show the efficiency and real-time performance of the proposed method, which meets the requirements of pantograph spark detection in high-speed railway.

Keywords: High-speed railway pantograph; Spark detection; Deep learning; YOLOv3;

DOI: 10.7176/ISDE/12-3-02

Publication date:September 30th 2021

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