Online Pantograph-Catenary Contact Point Detection in Complicated Background Based on Multiple Strategies

Abstract

Monitoring train operation status is one of the most important tasks for ensuring rail operation safety. Pantograph and catenary (PAC) are collecting systems of the electric current from traction power supply system, and the stability of the contact between pantograph and catenary guarantees the stable power. However, most existing contact point (CPT) detection methods are always difficult to achieve precise positioning results, especially in complicated background. This article proposes a novel fast and accurate contact point detection method based on multiple strategies, which combine three modules. First, an improved kernelized correlation filter model in real-time tracking module was adopted to track the contact region. Then the pixel-level detection module was used to detect contact point in contact region via the proposed contact point regression residual network (CPRR-Net). Finally, a filter-based optimization module was added to correct the contact position using the Kalman filter. This work additionally employed a new rail dataset PAC-TPL2020 to prove the effectiveness and feasibility of the proposed multiple strategies in real-world scenarios, and the experimental results demonstrated the robustness and high accuracy (97.07% within 3 pixels and 99.97% within 5 pixels) of our model. It is noteworthy that our mothed runs at 65 frames per second for monitoring PAC contact points.

Keywords

• Condition monitoring
• contact point
• deep convolutional neural network
• image processing
• pantograph and catenary system