{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T12:01:38Z","timestamp":1775217698923,"version":"3.50.1"},"reference-count":17,"publisher":"Institution of Engineering and Technology (IET)","issue":"1","license":[{"start":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T00:00:00Z","timestamp":1752537600000},"content-version":"vor","delay-in-days":195,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["ietresearch.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["IET Image Processing"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Accurate detection of surface anomalies in railway tracks is critical for ensuring train operation safety and enabling intelligent railway management. However, the scarcity and pronounced imbalance of anomaly samples significantly constrain model training and generalisation. Moreover, complex environmental factors such as illumination variability, sensor noise, and motion blur pose additional challenges to model robustness in real\u2010world applications. This study presents a Fuzzy\u2010YOLO model tailored for limited sample datasets. Built upon YOLOv11, Fuzzy\u2010YOLO incorporates a fuzzy\u2010non\u2010maximum suppression (NMS) mechanism and integrates a lightweight fuzzy residual neural network (RFNN\u2010Res) module based on fuzzy logic for anomaly classification. The final anomaly type is determined via a weighted voting strategy. Experimental evaluations demonstrate that Fuzzy\u2010YOLO achieves a mean average precision (mAP) of 98.90%, exhibiting notably enhanced stability compared to YOLOv11 under conditions of varying illumination, noise, and motion\u2010induced blur.<\/jats:p>","DOI":"10.1049\/ipr2.70156","type":"journal-article","created":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T07:12:24Z","timestamp":1752563544000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Fuzzy\u2010YOLO Model for Rail Anomaly Detection: Robustness Under Limited Sample and Interference Conditions"],"prefix":"10.1049","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-2185-2251","authenticated-orcid":false,"given":"Liyuan","family":"Yang","sequence":"first","affiliation":[{"name":"Kunming University  Kunming Yunnan China"},{"name":"College of Computing Informatics and Mathematics Universiti Teknologi MARA  Shah Alam Selangor Malaysia"}]},{"given":"Ming","family":"Yang","sequence":"additional","affiliation":[{"name":"Delong Software Technology (Tianjin) Co., Ltd  Tianjin China"}]},{"given":"Ghazali","family":"Osman","sequence":"additional","affiliation":[{"name":"College of Computing Informatics and Mathematics Universiti Teknologi MARA  Shah Alam Selangor Malaysia"}]},{"given":"Safawi Abdul","family":"Rahman","sequence":"additional","affiliation":[{"name":"College of Computing Informatics and Mathematics Universiti Teknologi MARA  Shah Alam Selangor Malaysia"}]},{"given":"Muhammad Firdaus","family":"Mustapha","sequence":"additional","affiliation":[{"name":"College of Computing Informatics and Mathematics Universiti Teknologi MARA  Shah Alam Selangor Malaysia"}]}],"member":"265","published-online":{"date-parts":[[2025,7,15]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58462-7_5"},{"key":"e_1_2_10_3_1","doi-asserted-by":"crossref","unstructured":"J.Redmon S.Divvala R.Girshick et\u00a0al. \"You Only Look Once: Unified Real\u2010Time Object Detection[C] \" inProceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016) 779\u2013788.","DOI":"10.1109\/CVPR.2016.91"},{"key":"e_1_2_10_4_1","unstructured":"R.KhanamandM.Hussain(2024). \"Yolov11: An Overview of the Key Architectural Enhancements \" arXiv preprint arXiv:2410.17725 October 23 2024."},{"key":"e_1_2_10_5_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2021.3117357","article-title":"Real\u2010time Defect Detection of Track Components: Considering Class Imbalance and Subtle Difference Between Classes","volume":"70","author":"Tu Z.","year":"2021","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.12700\/APH.19.3.2022.3.14"},{"key":"e_1_2_10_7_1","doi-asserted-by":"crossref","unstructured":"C.Mandriota E.Stella M.Nitti et\u00a0al. \"Rail Corrugation Detection by Gabor Filtering \" inProceedings of the 2001 International Conference on Image Processing (IEEE 2001) 626\u2013268.","DOI":"10.1109\/ICIP.2001.958571"},{"key":"e_1_2_10_8_1","doi-asserted-by":"crossref","unstructured":"L.Shang Q.Yang J.Wang et\u00a0al. \"Detection of Rail Surface Defects Based on CNN Image Recognition and Classification \" inProceedings of the20th International Conference on Advanced Communication Technology (IEEE 2018) 45\u201351.","DOI":"10.23919\/ICACT.2018.8323642"},{"issue":"22","key":"e_1_2_10_9_1","article-title":"Wheelset Tread Defect Detection Based on Improved YOLOv5","volume":"59","author":"Sun Y.","year":"2022","journal-title":"[In Chinese.] Laser & Optoelectronics Progress"},{"key":"e_1_2_10_10_1","unstructured":"C.Zhang Y.Xu J.He andH.Yang \u201cFast Detection of Train Wheelset Tread Defects Based on Residual Attention YOLO\u2010v5 \u201d[In Chinese.]Electric Drive for Locomotives no.6(2024):1\u20139."},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.19713\/j.cnki.43\u20101423\/u.T20200388"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-024-09781-0"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-64225-y"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13369\u2010023\u201008483\u20104"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11554-024-01519-4"},{"key":"e_1_2_10_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2019.00161"},{"key":"e_1_2_10_17_1","doi-asserted-by":"publisher","DOI":"10.1049\/iet-est.2020.0041"},{"key":"e_1_2_10_18_1","unstructured":"Z.Zhang S.Yu S.Yang Y.Zhou andB.Zhao \u201cRail\u20105k: A Real\u2010World Dataset for Rail Surface Defects Detection \u201d (2021). arXiv preprint arXiv:2106.14366 June 28 2021."}],"container-title":["IET Image 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