Research on optimization of rail surface defect detection algorithm

Gang Li; Caixia Xue; Yunfeng Ding

doi:10.1117/12.3025700

8 April 2024 Research on optimization of rail surface defect detection algorithm

Gang Li, Caixia Xue, Yunfeng Ding

Proceedings Volume 13090, International Conference on Computer Application and Information Security (ICCAIS 2023); 1309023 (2024) https://doi.org/10.1117/12.3025700
Event: International Conference on Computer Application and Information Security (ICCAIS 2023), 2023, Wuhan, China

Abstract

In the process of detection of rail surface defect with machine vision, crucial step is to denoise and extract feature. Over time, lots of technicist have studied the denoising algorithm and feature recognition algorithm. Nevertheless, there is still some room for studying the accuracy of algorithms. In this paper, an improved algorithm is presented, which applies a bilateral filtering method to denoise ensure that the defect boundary is not weakened, applies grayscale histogram analysis to optimize calculation of the threshold, and applies invariant moment feature extracting to make it possible to accurately identify falling block and collapse defects. The result shows that the detection algorithm of rail surface defects is optimized effectively and the accuracy of recognition is improved.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Gang Li, Caixia Xue, and Yunfeng Ding "Research on optimization of rail surface defect detection algorithm", Proc. SPIE 13090, International Conference on Computer Application and Information Security (ICCAIS 2023), 1309023 (8 April 2024); https://doi.org/10.1117/12.3025700

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