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Study and Optimization of Mask R-CNN Algorithm for Surface Defect Detection on Railway Tracks
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Ruifeng MENG1, Zhen LIANG1, Chao JIA2, Zhi QIAO3, Chen ZHAO1
Urban Rapid Rail Transit | 2024, 37(5) : 68 - 77
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Urban Rapid Rail Transit | 2024, 37(5): 68-77
Civil Engineering Technology
Study and Optimization of Mask R-CNN Algorithm for Surface Defect Detection on Railway Tracks
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Ruifeng MENG1, Zhen LIANG1, Chao JIA2, Zhi QIAO3, Chen ZHAO1
Affiliations
  • 1 School of Aeronautics Inner Mongolia University of Technology Hohhot 010051
  • 2 Inner Mongolia Lingyi High-Tech Group Co., Ltd. Hohhot 010010
  • 3 Inner Mongolia Expressway Group Co., Ltd. Hohhot 010051
doi: 10.3969/j.issn.1672-6073.2024.05.011
Outline
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Surface defect detection technology plays an essential role in railway inspections by effectively preventing railway accidents and ensuring operational safety. This study addresses the issues of poor detection accuracy and low sensitivity to small targets in existing railway defect detection technologies. For the mask regionbased convolutional neural network (Mask RCNN) algorithm model, a model improvement scheme was proposed by incorporating an attention mechanism. This scheme introduces a Channelwise Spatial Module (CSM) into the feature extraction network for segmentation defect detection, effectively eliminating interference, obtaining multiscale feature representations, and acquiring more spatial and shallow information, thereby enhancing the edge detection capability for surface defects on railway tracks. In the same experimental environment, compared with the Mask RCNN algorithm, after adding the CSM, the mAP value of the Mask RCNN model increased by 6.5%. Among them, the AP values for the recognition of "depression,” “crack,” and “fatigue wear" defects on railway tracks increased by 6.3%, 6.9%, and 6.1%, respectively. The horizontal segmentation effects of the three defects improved by 11.6%, 12.5%, and 12.9%, respectively, compared with the Faster RCNN model, and the segmentation effects of the three defects enhanced by 8.8%, 10.0%, and 10.3%, respectively. This study demonstrates that the Mask RCNN model with CSM can better recognize three types of defects, enhance the detection accuracy and sensitivity to small targets, provide more secure and robust technical support, and guarantee intelligent track inspection.

urban rail transit  /  channel-wise spatial attention mechanism  /  rail defects  /  instance segmentation  /  mask R-CNN algorithm
Ruifeng MENG, Zhen LIANG, Chao JIA, Zhi QIAO, Chen ZHAO. Study and Optimization of Mask R-CNN Algorithm for Surface Defect Detection on Railway Tracks[J]. Urban Rapid Rail Transit, 2024 , 37 (5) : 68 -77 . DOI: 10.3969/j.issn.1672-6073.2024.05.011
Year 2024 volume 37 Issue 5
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Article Info
doi: 10.3969/j.issn.1672-6073.2024.05.011
  • Receive Date:2023-12-26
  • Online Date:2025-07-09
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  • Received:2023-12-26
  • Revised:2024-05-23
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    1 School of Aeronautics Inner Mongolia University of Technology Hohhot 010051
    2 Inner Mongolia Lingyi High-Tech Group Co., Ltd. Hohhot 010010
    3 Inner Mongolia Expressway Group Co., Ltd. Hohhot 010051
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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