Multimodal Imitation Learning for Arc Detection in Complex Railway Environments

The pantograph-catenary system (PCS) is a critical component of railway vehicles, and its performance directly affects current collection quality. The arc rate serves as an essential measurement indicator for monitoring the PCS state. However, in complex railway environments—where arc sizes and shapes can vary significantly and are further influenced by factors such as reflected light, glare, and adverse weather—the traditional arc detection methods are easily affected by unstable current collection and power fluctuations, resulting in increased false detection rates and reduced measurement accuracy. Deep learning methods, while promising, also face limitations when dealing with such diverse arc morphologies…

• NN
  National Natural Science Foundation of China

  Awards: 52302444, 52202424
• NS
  Nuclear Science and Technology Research Institute

  Award: CYY-2024-YF-020
• NS
  Natural Science Foundation of Sichuan Province

  Award: 2025ZNSFSC0400
• FR
  Fundamental Research Funds for the Central Universities

  Award: 2682023GF012
• BA
  Basic and Applied Basic Research Foundation of Guangdong Province

  Award: 2023A1515011230