{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T15:59:18Z","timestamp":1791907158535,"version":"4.3.4"},"reference-count":44,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T00:00:00Z","timestamp":1722988800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"China-Laos-Thailand Education Digitization International Joint Research and Development Center of Yunnan Province","award":["202203AP140006"],"award-info":[{"award-number":["202203AP140006"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Railway track defects pose significant safety risks and can lead to accidents, economic losses, and loss of life. Traditional manual inspection methods are either time-consuming, costly, or prone to human error. This paper proposes RailTrack-DaViT, a novel vision transformer-based approach for railway track defect classification. By leveraging the Dual Attention Vision Transformer (DaViT) architecture, RailTrack-DaViT effectively captures both global and local information, enabling accurate defect detection. The model is trained and evaluated on multiple datasets including rail, fastener and fishplate, multi-faults, and ThaiRailTrack. A comprehensive analysis of the model\u2019s performance is provided including confusion matrices, training visualizations, and classification metrics. RailTrack-DaViT demonstrates superior performance compared to state-of-the-art CNN-based methods, achieving the highest accuracies: 96.9% on the rail dataset, 98.9% on the fastener and fishplate dataset, and 98.8% on the multi-faults dataset. Moreover, RailTrack-DaViT outperforms baselines on the ThaiRailTrack dataset with 99.2% accuracy, quickly adapts to unseen images, and shows better model stability during fine-tuning. This capability can significantly reduce time consumption when applying the model to novel datasets in practical applications.<\/jats:p>","DOI":"10.3390\/jimaging10080192","type":"journal-article","created":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T11:33:52Z","timestamp":1723030432000},"page":"192","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["RailTrack-DaViT: A Vision Transformer-Based Approach for Automated Railway Track Defect Detection"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2289-3116","authenticated-orcid":false,"given":"Aniwat","family":"Phaphuangwittayakul","sequence":"first","affiliation":[{"name":"International College of Digital Innovation, Chiang Mai University, Chiang Mai 50200, Thailand"},{"name":"Lancang-Mekong Digital Intelligence (Shijiazhuang) Technology Research Center, Shijiazhuang 051230, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Napat","family":"Harnpornchai","sequence":"additional","affiliation":[{"name":"Faculty of Economics, Chiang Mai University, Chiang Mai 50200, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8390-3229","authenticated-orcid":false,"given":"Fangli","family":"Ying","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Bioreactor Engineering, Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai 200237, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinming","family":"Zhang","sequence":"additional","affiliation":[{"name":"International College of Digital Innovation, Chiang Mai University, Chiang Mai 50200, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.qref.2018.04.002","article-title":"Do high-speed railways lead to urban economic growth in China? 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