A pantograph-catenary arcing detection model for high-speed railway based on semantic segmentation and generative adversarial network

Xiaohong Liu, Xiaoyu Wang, Wei Quan, Guoxin Gu, Xiaoqian Xu and Shibin Gao

International Journal of Rail Transportation, 2025, vol. 13, issue 4, 773-794

Abstract: Pantograph-catenary arcing refers to an abnormal phenomenon occurring in pantograph-catenary system due to poor contact or other factors, which significantly impacts the normal operation of high-speed railway. Therefore, the detection of arcing occurrences holds significant importance for the intelligent maintenance of pantograph-catenary systems. However, the scarcity of arcing data in pantograph-catenary datasets limits the efficacy of supervised learning methods for arcing detection. To address this issue, we propose a novel pantograph-catenary arcing detection model that integrates semantic segmentation with generative adversarial networks. The model first modifies the loss function of the U$^2$ 2-Net network to tailor it specifically for pantograph-catenary semantic segmentation. To generate finer normal pantograph-catenary images, attention mechanism is incorporated into the SPADE-based pantograph-catenary scene generation model. Finally, an improved differencing method is employed to compute the arcing image by subtracting the generated normal pantograph-catenary image from the actual pantograph-catenary image. The experimental results validate the effectiveness of the method for pantograph-catenary arcing detection in the absence of prior arcing knowledge, with a recall rate of 75.3% and an F1-Score of 69.63%. Compared to other advanced pantograph-catenary arcing methods, this method exhibits superior performance.

Date: 2025
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DOI: 10.1080/23248378.2024.2382117

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