CBAM-SwinT-BL: Small Rail Surface Defect Detection Method Based on Swin Transformer With Block Level CBAM Enhancement
Under high-intensity rail operations, rail tracks endure considerable stresses resulting in various defects such as corrugation and spellings. Failure to effectively detect defects and provide maintenance in time would compromise service reliability and public safety. While advanced models have been...
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IEEE
2024-01-01
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| author | Jiayi Zhao Alison Wun-Lam Yeung Muhammad Ali Songjiang Lai Vincent To-Yee Ng |
| author_facet | Jiayi Zhao Alison Wun-Lam Yeung Muhammad Ali Songjiang Lai Vincent To-Yee Ng |
| author_sort | Jiayi Zhao |
| collection | DOAJ |
| description | Under high-intensity rail operations, rail tracks endure considerable stresses resulting in various defects such as corrugation and spellings. Failure to effectively detect defects and provide maintenance in time would compromise service reliability and public safety. While advanced models have been developed in recent years, efficiently identifying small-scale rail defects has not yet been studied, especially for categories such as Dirt or Squat on rail surface. To address this challenge, this study utilizes Swin Transformer (SwinT) as baseline and incorporates the Convolutional Block Attention Module (CBAM) for enhancement. Our proposed method integrates CBAM successively within the swin transformer blocks, resulting in significant performance improvement in rail defect detection, particularly for categories with small instance sizes. The proposed framework is named CBAM-Enhanced Swin Transformer in Block Level (CBAM-SwinT-BL). Experiment and ablation study have proven the effectiveness of the framework. The proposed framework has a notable improvement in the accuracy of small size defects, such as dirt and dent categories in RIII dataset, with mAP-50 increasing by +23.0% and +38.3% respectively, and the squat category in MUET dataset also reaches +13.2% higher than the original model. Compares to the original SwinT, CBAM-SwinT-BL increase overall precision around +5% in the MUET dataset and +7% in the RIII dataset, reaching 69.1% and 88.1% respectively. Meanwhile, the additional module CBAM merely extend the model training speed by an average of +0.04s/iteration, which is acceptable compared to the significant improvement in system performance. |
| format | Article |
| id | doaj-art-86b05ba3325f4f2280cd86b06ab9bae2 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
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| spelling | doaj-art-86b05ba3325f4f2280cd86b06ab9bae22025-08-20T02:21:51ZengIEEEIEEE Access2169-35362024-01-011218199718200910.1109/ACCESS.2024.350998610772194CBAM-SwinT-BL: Small Rail Surface Defect Detection Method Based on Swin Transformer With Block Level CBAM EnhancementJiayi Zhao0https://orcid.org/0009-0009-7208-0007Alison Wun-Lam Yeung1Muhammad Ali2Songjiang Lai3Vincent To-Yee Ng4Center for Advances in Reliability and Safety (CAiRS), New Territories, Hong Kong, ChinaCenter for Advances in Reliability and Safety (CAiRS), New Territories, Hong Kong, ChinaCenter for Advances in Reliability and Safety (CAiRS), New Territories, Hong Kong, ChinaCenter for Advances in Reliability and Safety (CAiRS), New Territories, Hong Kong, ChinaCenter for Advances in Reliability and Safety (CAiRS), New Territories, Hong Kong, ChinaUnder high-intensity rail operations, rail tracks endure considerable stresses resulting in various defects such as corrugation and spellings. Failure to effectively detect defects and provide maintenance in time would compromise service reliability and public safety. While advanced models have been developed in recent years, efficiently identifying small-scale rail defects has not yet been studied, especially for categories such as Dirt or Squat on rail surface. To address this challenge, this study utilizes Swin Transformer (SwinT) as baseline and incorporates the Convolutional Block Attention Module (CBAM) for enhancement. Our proposed method integrates CBAM successively within the swin transformer blocks, resulting in significant performance improvement in rail defect detection, particularly for categories with small instance sizes. The proposed framework is named CBAM-Enhanced Swin Transformer in Block Level (CBAM-SwinT-BL). Experiment and ablation study have proven the effectiveness of the framework. The proposed framework has a notable improvement in the accuracy of small size defects, such as dirt and dent categories in RIII dataset, with mAP-50 increasing by +23.0% and +38.3% respectively, and the squat category in MUET dataset also reaches +13.2% higher than the original model. Compares to the original SwinT, CBAM-SwinT-BL increase overall precision around +5% in the MUET dataset and +7% in the RIII dataset, reaching 69.1% and 88.1% respectively. Meanwhile, the additional module CBAM merely extend the model training speed by an average of +0.04s/iteration, which is acceptable compared to the significant improvement in system performance.https://ieeexplore.ieee.org/document/10772194/Object detectionsmall rail surface defectsSwin Transformerconvolutional block attention module (CBAM)attention mechanism |
| spellingShingle | Jiayi Zhao Alison Wun-Lam Yeung Muhammad Ali Songjiang Lai Vincent To-Yee Ng CBAM-SwinT-BL: Small Rail Surface Defect Detection Method Based on Swin Transformer With Block Level CBAM Enhancement IEEE Access Object detection small rail surface defects Swin Transformer convolutional block attention module (CBAM) attention mechanism |
| title | CBAM-SwinT-BL: Small Rail Surface Defect Detection Method Based on Swin Transformer With Block Level CBAM Enhancement |
| title_full | CBAM-SwinT-BL: Small Rail Surface Defect Detection Method Based on Swin Transformer With Block Level CBAM Enhancement |
| title_fullStr | CBAM-SwinT-BL: Small Rail Surface Defect Detection Method Based on Swin Transformer With Block Level CBAM Enhancement |
| title_full_unstemmed | CBAM-SwinT-BL: Small Rail Surface Defect Detection Method Based on Swin Transformer With Block Level CBAM Enhancement |
| title_short | CBAM-SwinT-BL: Small Rail Surface Defect Detection Method Based on Swin Transformer With Block Level CBAM Enhancement |
| title_sort | cbam swint bl small rail surface defect detection method based on swin transformer with block level cbam enhancement |
| topic | Object detection small rail surface defects Swin Transformer convolutional block attention module (CBAM) attention mechanism |
| url | https://ieeexplore.ieee.org/document/10772194/ |
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