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

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${ }^2$-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.

KEYWORDS:

• Pantograph-catenary arcing detection
• semantic segmentation
• generative adversarial networks (GANs)
• digital image processing

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [No. 52277127], the Science and Technology Innovation Talent Project of Sichuan Province [No. 2021JDRC0012], the Key Interdisciplinary Basic Research Project of Southwest Jiaotong University [No. 2682021ZTPY089], Open Research Project of National Rail Transit Electrification and Automation Engineering Technology Research Center [No. NEEC-2019-B06].