Pantograph Slider Detection Architecture and Solution Based on Deep Learning
Qichang Guo, Anjie Tang, Jiabin Yuan

TL;DR
This paper introduces a deep learning-based solution to detect wear in pantograph sliders used in high-speed rail systems, aiming to improve detection accuracy and efficiency.
Contribution
The novel contribution is integrating attention mechanisms and a new image stitching method with deep learning for enhanced pantograph slider wear detection.
Findings
A linear array camera improves dataset quality for better detection accuracy.
An attention mechanism enhances segmentation performance in wear detection.
A new image stitching method addresses incomplete image issues in monitoring.
Abstract
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…
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Taxonomy
TopicsDigital Storytelling and Education · Artistic and Creative Research
