High Precision Robust Real-Time Lightweight Approach for Railway Pantograph Slider Wear Estimation

The pantograph slider is a key component of the pantograph-catenary system. It is important to monitor the wear of sliders for rail transit safety. In this paper, an innovative real-time high-precision lightweight approach is proposed to estimate the wear of the slider. It allows complete monitoring of all sliders of the pantograph. In the first stage, a method based on image processing and object detection by deep learning is proposed to locate the region of the slider. It takes into account the large aspect ratio on the pantograph slider and the inclined angle. In the second stage, the neural network for wear estimation of pantograph slider (WEPSNet) is proposed. It realizes end-to-end contour extraction of the slider. The residual thickness of the slider is calculated by counting the number of pixels and the error is analyzed. Furthermore, the error arising from the perspective projection transformation in the monocular image is discussed. The experimental results demonstrate that, with the similar model size, the proposed WEPSNet outperforms the state-of-the-art method by 1.08% mIoU and 4.63% IMP. Moreover, the accuracy of residual thickness is tested on 120 pantograph slider images, achieving up to 95.91% within the allowable 1mm error, which is 6.68% higher than the state-of-the-art method.