Deep Learning (Fast R-CNN)-Based Evaluation of Rail Surface Defects
In current railway rails, trains are propelled by the rolling contact between iron wheels and iron rails, and the high frequency of train repetition on rails results in a significant load exertion on a very small area where the wheel and rail come into contact. Furthermore, a contact stress beyond t...
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| Format: | Article |
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MDPI AG
2024-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/5/1874 |
| _version_ | 1797264866330804224 |
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| author | Jung-Youl Choi Jae-Min Han |
| author_facet | Jung-Youl Choi Jae-Min Han |
| author_sort | Jung-Youl Choi |
| collection | DOAJ |
| description | In current railway rails, trains are propelled by the rolling contact between iron wheels and iron rails, and the high frequency of train repetition on rails results in a significant load exertion on a very small area where the wheel and rail come into contact. Furthermore, a contact stress beyond the allowable stress of the rail may lead to cracks due to plastic deformation. The railway rail, which is the primary contact surface between the wheel and the rail, is prone to rolling contact fatigue cracks. Therefore, a thorough inspection and diagnosis of the condition of the cracks is necessary to prevent fracture. The Detailed Guideline on the Performance Evaluation of Track Facilities in South Korea specifies the detailed requirements for the methods and procedures for conducting track performance evaluations. However, diagnosing rail surface damage and determining the severity solely rely on visual inspection, which depends on the qualitative evaluation and subjective judgment of the inspector. Against this backdrop, rail surface defect detection was investigated using Fast R-CNN in this study. To test the feasibility of the model, we constructed a dataset of rail surface defect images. Through field investigation, 1300 images of rail surface defects were obtained. Aged rails collected from the field were processed, and 1300 images of internal defects were generated through SEM testing; therefore, a total of 1300 pieces of learning data were constructed. The detection results indicated that the mean average precision was 94.9%. The Fast R-CNN exhibited high efficiency in detecting rail surface defects, and it demonstrated a superior recognition performance compared with other algorithms. |
| first_indexed | 2024-04-25T00:35:42Z |
| format | Article |
| id | doaj.art-aaee91b13cb94638812f21455be08926 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-04-25T00:35:42Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-aaee91b13cb94638812f21455be089262024-03-12T16:39:19ZengMDPI AGApplied Sciences2076-34172024-02-01145187410.3390/app14051874Deep Learning (Fast R-CNN)-Based Evaluation of Rail Surface DefectsJung-Youl Choi0Jae-Min Han1Department of Construction Engineering, Dongyang University, No. 145 Dongyangdae-ro, Punggi-eup, Yeongju-si 36040, Republic of KoreaDepartment of Construction Engineering, Dongyang University, No. 145 Dongyangdae-ro, Punggi-eup, Yeongju-si 36040, Republic of KoreaIn current railway rails, trains are propelled by the rolling contact between iron wheels and iron rails, and the high frequency of train repetition on rails results in a significant load exertion on a very small area where the wheel and rail come into contact. Furthermore, a contact stress beyond the allowable stress of the rail may lead to cracks due to plastic deformation. The railway rail, which is the primary contact surface between the wheel and the rail, is prone to rolling contact fatigue cracks. Therefore, a thorough inspection and diagnosis of the condition of the cracks is necessary to prevent fracture. The Detailed Guideline on the Performance Evaluation of Track Facilities in South Korea specifies the detailed requirements for the methods and procedures for conducting track performance evaluations. However, diagnosing rail surface damage and determining the severity solely rely on visual inspection, which depends on the qualitative evaluation and subjective judgment of the inspector. Against this backdrop, rail surface defect detection was investigated using Fast R-CNN in this study. To test the feasibility of the model, we constructed a dataset of rail surface defect images. Through field investigation, 1300 images of rail surface defects were obtained. Aged rails collected from the field were processed, and 1300 images of internal defects were generated through SEM testing; therefore, a total of 1300 pieces of learning data were constructed. The detection results indicated that the mean average precision was 94.9%. The Fast R-CNN exhibited high efficiency in detecting rail surface defects, and it demonstrated a superior recognition performance compared with other algorithms.https://www.mdpi.com/2076-3417/14/5/1874rolling contact fatiguerail surface defectsrail internal defectsdeep learningfast R-CNNrail surface defect detection |
| spellingShingle | Jung-Youl Choi Jae-Min Han Deep Learning (Fast R-CNN)-Based Evaluation of Rail Surface Defects Applied Sciences rolling contact fatigue rail surface defects rail internal defects deep learning fast R-CNN rail surface defect detection |
| title | Deep Learning (Fast R-CNN)-Based Evaluation of Rail Surface Defects |
| title_full | Deep Learning (Fast R-CNN)-Based Evaluation of Rail Surface Defects |
| title_fullStr | Deep Learning (Fast R-CNN)-Based Evaluation of Rail Surface Defects |
| title_full_unstemmed | Deep Learning (Fast R-CNN)-Based Evaluation of Rail Surface Defects |
| title_short | Deep Learning (Fast R-CNN)-Based Evaluation of Rail Surface Defects |
| title_sort | deep learning fast r cnn based evaluation of rail surface defects |
| topic | rolling contact fatigue rail surface defects rail internal defects deep learning fast R-CNN rail surface defect detection |
| url | https://www.mdpi.com/2076-3417/14/5/1874 |
| work_keys_str_mv | AT jungyoulchoi deeplearningfastrcnnbasedevaluationofrailsurfacedefects AT jaeminhan deeplearningfastrcnnbasedevaluationofrailsurfacedefects |