RT Journal Article
T1 Design and development of a wayside aI-assisted vision system for online train wheel inspection
A1 Zakir Shaikh, Muhammad
A1 Mehran, Sanaullah
A1 Nava-Baro, Enrique
A1 Manolova, Agata
A1 Aslam Uqaili, Mohammad
A1 Hussain, Tanweer
A1 Shankar Chowdhry, Bhawani
K1 Visión artificial (Robótica)
K1 Reconocimiento de formas (Informática)
AB Wayside inspection of rolling stock has been around for some time and wheel impact load and fiber-grating sensors are actively explored for getting high-fidelity data. Visual inspection from wayside provides the opportunity to gain high-resolution data, which can help in the early diagnosis of potential faults. It is rarely explored due to complexities associated with calibration, moving and rotating targets, and difficulties associated with data acquisition. This paper explores and presents an in-depth design and development strategy for such systems. It presents the development steps, implementation, and results of a vision inspection system for regular and automated inspection of train wheels. First, various configurations for positioning of the cameras in a three-dimensional setting are considered and discussed, followed by online data acquisition for establishing a data set. Later, a comprehensive comparative analysis was conducted on several object detection algorithms for wheel segmentation task. Different algorithms are evaluated using COCO evaluation metrics, and the best-performing model, YOLOv9, achieves a mAP50 of 0.94 and a recall of 0.91. The developed system has produced satisfactory results in acquiring proper wheel tread images and segmenting the wheel. Further avenues for countering lighting issues and defect detection are provided.
PB Wiley
YR 2024
FD 2024
LK https://hdl.handle.net/10630/36067
UL https://hdl.handle.net/10630/36067
LA eng
NO Shaikh, M.Z., Mehran, S., Baro, E.N., Manolova, A., Uqaili, M.A., Hussain, T. and Chowdhry, B.S. (2024), Design and Development of a Wayside AI-Assisted Vision System for Online Train Wheel Inspection. Engineering Reports e13027. https://doi.org/10.1002/eng2.13027
NO This work was supported by the INDUDECAM Project CPP2021–009117, financiado por MCIN/AEI/10.13039/501100011033 y por la Unión Europea“NextGenerationEU”/PRTR. Universidad de Málaga / CBUA (APC); Sindh Higher Education Commission (RESEARCH)/SHEC/1-11/2021, andHEC-NCRA-CMS Lab, 2(1076)/HEC/M&E/2018/704Funding for open access charge: Universidad de Málaga / CBUA
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 4 mar 2026