Guo Qichang, Tang Anjie, Yuan Jiabin
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210095, China.
School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Sensors (Basel). 2024 Aug 8;24(16):5133. doi: 10.3390/s24165133.
Railway transportation has been integrated into people's lives. According to the "Notice on the release of the General Technical Specification of High-speed Railway Power Supply Safety Testing (6C System) System" issued by the National Railway Administration of China in 2012, it is required to install pantograph and slide monitoring devices in high-speed railway stations, station throats and the inlet and exit lines of high-speed railway sections, and it is required to detect the damage of the slider with high precision. It can be seen that the good condition of the pantograph slider is very important for the normal operation of the railway system. As a part of providing power for high-speed rail and subway, the pantograph must be paid attention to in railway transportation to ensure its integrity. The wear of the pantograph is mainly due to the contact power supply between the slide block and the long wire during high-speed operation, which inevitably produces scratches, resulting in depressions on the upper surface of the pantograph slide block. During long-term use, because the depression is too deep, there is a risk of fracture. Therefore, it is necessary to monitor the slider regularly and replace the slider with serious wear. At present, most of the traditional methods use automation technology or simple computer vision technology for detection, which is inefficient. Therefore, this paper introduces computer vision and deep learning technology into pantograph slide wear detection. Specifically, this paper mainly studies the wear detection of the pantograph slider based on deep learning and the main purpose is to improve the detection accuracy and improve the effect of segmentation. From a methodological perspective, this paper employs a linear array camera to enhance the quality of the data sets. Additionally, it integrates an attention mechanism to improve segmentation performance. Furthermore, this study introduces a novel image stitching method to address issues related to incomplete images, thereby providing a comprehensive solution.
铁路运输已融入人们的生活。根据中国国家铁路局2012年发布的《关于发布高速铁路供电安全检测（6C系统）系统通用技术规范的通知》，要求在高铁站、站场咽喉区及高铁线路进出线段安装受电弓滑板监测装置，并要求高精度检测滑板的损伤情况。由此可见，受电弓滑板的良好状态对铁路系统的正常运行至关重要。作为为高铁和地铁供电的一部分，受电弓在铁路运输中必须得到重视，以确保其完整性。受电弓的磨损主要是由于高速运行时滑块与长导线之间的接触供电，不可避免地产生划痕，导致受电弓滑块上表面出现凹陷。在长期使用过程中，由于凹陷过深，存在断裂风险。因此，有必要定期监测滑块，并更换磨损严重的滑块。目前，大多数传统方法采用自动化技术或简单的计算机视觉技术进行检测，效率较低。因此，本文将计算机视觉和深度学习技术引入受电弓滑板磨损检测中。具体而言，本文主要研究基于深度学习的受电弓滑块磨损检测，主要目的是提高检测精度，提升分割效果。从方法学角度来看，本文采用线阵相机提高数据集质量。此外，集成注意力机制以提高分割性能。此外，本研究引入一种新颖的图像拼接方法来解决图像不完整的问题，从而提供一个全面的解决方案。
