3D tensor-based point cloud and image fusion for robust detection and measurement of rail surface defects

Wang, Qihang; Wang, Xiaoming; He, Qing; Huang, Jun; Huang, Hong; Wang, Ping; Yu, Tianle; Zhang, Min

doi:10.1016/j.autcon.2024.105342

Cited by 4 publications

(1 citation statement)

References 49 publications

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“…Inspection of railway infrastructure is a crucial measure for ensuring the safety of train operations. The railway infrastructure comprises several vital components, with the rail being one of the most critical elements that require specialized maintenance [1][2][3][4][5]. The use of full-section profile data from the rail to assess the correlation between the rail and wheels significantly enhances existing track quality evaluation standards [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Error correction method based on dual-beam laser for curved rail profile measurement

Liu,

Wang,

Cheng

et al. 2024

Meas. Sci. Technol.

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The current method of dynamic rail profile measurement involves the installation of a line-structured light sensor at the base of the train. The accuracy of this measurement is influenced by the vertical relationship between the laser plane of the light sensor and the longitudinal direction of the rail (LDR). When a train travels in a straight line, the normal of the laser plane aligns with the LDR. However, when the train curves, the angle at which its wheels connect with the rails causes the laser plane's normal direction to deviate from the LDR, leading to measurement errors. To address this issue, we propose a method for curved rail profile measurement using a dual-beam laser to correct these errors. This method involves generating an auxiliary 3D rail reflecting the LDR and a virtual 3D rail reflecting the normal direction of the laser plane from the cross-section image of the dual-beam laser. An optimization function is then formulated to determine the optimal auxiliary plane (optimal-AP) by analyzing the alignment or intersection between the auxiliary and virtual 3D rails. Distorted contour points are projected onto the optimal-AP to rectify errors. Experiments validate the accuracy and effectiveness of this proposed method. The results show that, regardless of pitch or yaw movement between the laser plane and the LDR, the error in measuring corrected profile wear remains consistently below 0.10 millimeters, thereby meeting the accuracy standard for rail wear measurement. This approach rectifies measurement errors in curved rail profiles from a 3D perspective, ensuring accurate measurements even under complex working conditions. It also provides a valuable reference for error analysis and improving dynamic rail profile measurement accuracy.

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