Rail surface defect detection based on deep learning

Xiaoqing Li; Ying Zhou; Hu Chen

doi:10.1117/12.2557212

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3 January 2020 Rail surface defect detection based on deep learning

Xiaoqing Li, Ying Zhou, Hu Chen

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Proceedings Volume 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019); 113730K (2020) https://doi.org/10.1117/12.2557212
Event: Eleventh International Conference on Graphics and Image Processing, 2019, Hangzhou, China

Abstract

In order to ensure the safety of rail transit, detecting the flaws on the rail surface is vitally important. Instead of present manual inspections, detecting defects on rail surface by an automatic approach enables the work more efficient and safe currently. In this paper, we propose a novel two-stage pipeline method for defect detection on rail surface by localizing rails and sliding a deep convolutional neural network (DCNN) on rail surface. Specifically, in the first stage, we use an anchor-free detector to locate the tracks in original images and get the cropped images which focus on rail part. In the second stage, a trained deep convolutional neural network slide on the cropped images to detect defects and we can finally get the types and approximate locations of the defects on rail surface. The experimental results show that the proposed method has robustness and achieves practical performance in defect detection precision.

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Xiaoqing Li, Ying Zhou, and Hu Chen "Rail surface defect detection based on deep learning", Proc. SPIE 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019), 113730K (3 January 2020); https://doi.org/10.1117/12.2557212

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