{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T18:58:51Z","timestamp":1784314731175,"version":"3.55.0"},"reference-count":50,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51975347"],"award-info":[{"award-number":["51975347"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100019014","name":"Shanghai Science and Technology Program","doi-asserted-by":"publisher","award":["22010501600"],"award-info":[{"award-number":["22010501600"]}],"id":[{"id":"10.13039\/501100019014","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2024]]},"DOI":"10.1109\/access.2024.3466559","type":"journal-article","created":{"date-parts":[[2024,9,23]],"date-time":"2024-09-23T17:29:20Z","timestamp":1727112560000},"page":"196249-196265","source":"Crossref","is-referenced-by-count":8,"title":["YOLOv8n-RSDD: A High-Performance Low-Complexity Rail Surface Defect Detection Network"],"prefix":"10.1109","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-4881-8720","authenticated-orcid":false,"given":"Zhanao","family":"Fang","sequence":"first","affiliation":[{"name":"School of Urban Railway Transportation, Shanghai University of Engineering Science, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liming","family":"Li","sequence":"additional","affiliation":[{"name":"School of Urban Railway Transportation, Shanghai University of Engineering Science, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2030-0986","authenticated-orcid":false,"given":"Lele","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Urban Railway Transportation, Shanghai University of Engineering Science, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2327-4245","authenticated-orcid":false,"given":"Shubin","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Urban Railway Transportation, Shanghai University of Engineering Science, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0272-0357","authenticated-orcid":false,"given":"Qianwen","family":"Zhong","sequence":"additional","affiliation":[{"name":"School of Urban Railway Transportation, Shanghai University of Engineering Science, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ting","family":"Zhu","sequence":"additional","affiliation":[{"name":"China Railway Shanghai Group Co., Ltd., Science and Technology Research Institute, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.12700\/APH.19.3.2022.3.14"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2015.01.024"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2020.3039301"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2021.3085848"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CAC.2018.8623082"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/2565500"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.3390\/s22249970"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.32604\/cmes.2023.043832"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref11","article-title":"YOLOv3: An incremental improvement","volume-title":"arXiv:1804.02767","author":"Redmon","year":"2018"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2024.3376563"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/HPEC55821.2022.9926331"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.3389\/fnbot.2023.1143032"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2022.3165287"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.3390\/s22093537"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.jksuci.2024.101929"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2022.104914"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.3390\/machines10070523"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2023.3249216"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2023.107662"},{"key":"ref24","first-page":"1","article-title":"VanillaNet: The power of minimalism in deep learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"36","author":"Chen"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/s11554-024-01436-6"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1807.06521"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2010.11929"},{"key":"ref28","first-page":"1","article-title":"ImageNet classification with deep convolutional neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"25","author":"Krizhevsky"},{"key":"ref29","article-title":"Very deep convolutional networks for large-scale image recognition","volume-title":"arXiv:1409.1556","author":"Simonyan","year":"2014"},{"key":"ref30","article-title":"Rail-5k: A real-world dataset for rail surface defects detection","volume-title":"arXiv:2106.14366","author":"Zhang","year":"2021"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00264"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2018.2853958"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2020.3004397"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"ref35","first-page":"10096","article-title":"EfficientNetv2: Smaller models and faster training","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Tan"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00165"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00764"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01352"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP39728.2021.9414568"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP49357.2023.10096516"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1738\/1\/012051"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1007\/s11119-020-09754-y"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW54120.2021.00312"},{"key":"ref47","volume-title":"ultralytics\/yolov5: v5.0\u2014YOLOv5-P6 1280 models, AWS, Supervise.ly and YouTube integrations","author":"Jocher","year":"2021"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2209.02976"},{"key":"ref49","first-page":"578","article-title":"TVM: An automated end-to-end optimizing compiler for deep learning","volume-title":"Proc. 13th USENIX Symp. Operating Syst. Design Implement. (OSDI)","author":"Chen"},{"key":"ref50","article-title":"High performance GPU code generation for matrix-matrix multiplication using MLIR: Some early results","volume-title":"arXiv:2108.13191","author":"Katel","year":"2021"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/10380310\/10689418.pdf?arnumber=10689418","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T07:14:46Z","timestamp":1735715686000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10689418\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":50,"URL":"https:\/\/doi.org\/10.1109\/access.2024.3466559","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]}}}