{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T11:45:47Z","timestamp":1776858347866,"version":"3.51.2"},"reference-count":24,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T00:00:00Z","timestamp":1776816000000},"content-version":"vor","delay-in-days":111,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100019600","name":"Tr\u01b0\u1eddng \u0110\u1ea1i h\u1ecdc B\u00e1ch Khoa H\u00e0 N\u1ed9i","doi-asserted-by":"publisher","award":["T2023-PC-030"],"award-info":[{"award-number":["T2023-PC-030"]}],"id":[{"id":"10.13039\/100019600","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Intelligent Systems"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:p>This paper presents a unified real\u2010time framework for tire tread identification that integrates tire\u2010mark detection and tread retrieval into a single end\u2010to\u2010end pipeline. Unlike existing studies that directly apply standard YOLO or metric\u2010learning models, the proposed system introduces three key innovations: (1) a YOLOv11\u2010based detector customized with adaptive anchor re\u2010estimation and balanced focal loss, which significantly improves localization on blurred, partial, or low\u2010contrast tire impressions commonly found in forensic scenes; (2) a ConvNeXt\u2010ECA embedding head within a triplet network, designed to capture fine\u2010grained ridge textures under varying illumination and heterogeneous surface conditions; and (3) the construction of one of the first diverse forensic tire\u2010mark datasets in Vietnam, supporting improved robustness across diverse scenes and benchmark evaluation for tread retrieval. Experimental results show that the enhanced YOLOv11 detector achieves 96.52% precision and 93.91% recall, while the proposed retrieval module obtains a Top\u20101 accuracy of 84.78% and mAP of 91.28%. Together, these contributions support practical forensic decision\u2010making in real\u2010world scenarios.<\/jats:p>","DOI":"10.1155\/int\/7723883","type":"journal-article","created":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T11:06:13Z","timestamp":1776855973000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Real\u2010Time Tire Tread Identification From Wheel Tracks Using YOLOv11 and Triplet Embedding"],"prefix":"10.1155","volume":"2026","author":[{"given":"Hoang Tran","family":"Manh","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dung Pham","family":"Anh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2734-5781","authenticated-orcid":false,"given":"Phat","family":"Nguyen Huu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2026,4,22]]},"reference":[{"key":"e_1_2_12_1_2","volume-title":"QKSET: A Forensic Dataset for Tire Impression Analysis","author":"Authors Q. D.","year":"2024"},{"key":"e_1_2_12_2_2","article-title":"YOLOv4: Optimal Speed and Accuracy of Object Detection","author":"Bochkovskiy A.","year":"2020","journal-title":"arXiv Preprint arXiv:2004.10934"},{"key":"e_1_2_12_3_2","doi-asserted-by":"crossref","unstructured":"LiuZ.et al. A ConvNet for the 2020s (ConvNeXt) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2022 CVPR 11976\u201311986 https:\/\/doi.org\/10.1109\/CVPR52688.2022.01167.","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"e_1_2_12_4_2","doi-asserted-by":"crossref","unstructured":"WangQ. WuB. ZhuP. LiP. ZuoW. andHuQ. ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2020 CVPR 11534\u201311542 https:\/\/doi.org\/10.1109\/CVPR42600.2020.01155.","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"e_1_2_12_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.forsciint.2012.03.013"},{"key":"e_1_2_12_6_2","volume-title":"Advances in Neural Information Processing Systems","author":"Ren S.","year":"2015"},{"key":"e_1_2_12_7_2","doi-asserted-by":"crossref","unstructured":"LiuW.et al. SSD: Single Shot Multibox Detector The European Conference on Computer Vision (ECCV) 2016 21\u201337 https:\/\/doi.org\/10.1007\/978-3-319-46448-0_2 2-s2.0-84990068627.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"e_1_2_12_8_2","volume-title":"Ultralytics YOLOv8","author":"Jocher G.","year":"2023"},{"key":"e_1_2_12_9_2","article-title":"Tire Tread Detection Using Fusion of Spatial and Frequency Domain Features","author":"Chen Q.","year":"2024","journal-title":"Proceedings\u2014Institution of Mechanical Engineers Part D: J. Automobile Eng., Early Access"},{"key":"e_1_2_12_10_2","doi-asserted-by":"crossref","unstructured":"HuangD. Y. ChenC. I. ChengC. H. HuW. C. andWangY. W. Recognition of Tire Tread Patterns Based on Gabor Wavelets and Support Vector Machine Proceedings of the International Conference on Computational Collective Intelligence 2010 ICCCI 92\u2013101 https:\/\/doi.org\/10.1007\/978-3-642-16699-6_12.","DOI":"10.1007\/978-3-642-16696-9_11"},{"key":"e_1_2_12_11_2","doi-asserted-by":"publisher","DOI":"10.3390\/s24092778"},{"key":"e_1_2_12_12_2","doi-asserted-by":"crossref","DOI":"10.1201\/9781420006827","volume-title":"Tire and Tire Track Evidence: Recovery and Forensic Examination","author":"Bodziak W. J.","year":"2008"},{"key":"e_1_2_12_13_2","unstructured":"KochG. ZemelR. andSalakhutdinovR. Siamese Neural Networks for One-Shot Image Recognition ICML Deep Learning Workshop 2015."},{"key":"e_1_2_12_14_2","doi-asserted-by":"crossref","unstructured":"SchroffF. KalenichenkoD. andPhilbinJ. FaceNet: A Unified Embedding for Face Recognition and Clustering 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2015 IEEE 815\u2013823 https:\/\/doi.org\/10.1109\/CVPR.2015.7298682 2-s2.0-84946751287.","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"e_1_2_12_15_2","unstructured":"LiZ. WangH.et al. Method of Tire Pattern Image Retrieval Based on Wavelet Transform and Siamese Network Proceedings of the International Conference on Aviation Safety and Information Technology 2020 IEEE."},{"key":"e_1_2_12_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.forsciint.2024.112009"},{"key":"e_1_2_12_17_2","volume-title":"YOLOv11: Official Release","author":"Ultralytics","year":"2024"},{"key":"e_1_2_12_18_2","doi-asserted-by":"publisher","DOI":"10.1080\/09540091.2021.1886479"},{"key":"e_1_2_12_19_2","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-019-0197-0"},{"key":"e_1_2_12_20_2","doi-asserted-by":"publisher","DOI":"10.3390\/s24113477"},{"key":"e_1_2_12_21_2","doi-asserted-by":"publisher","DOI":"10.3390\/s24237802"},{"key":"e_1_2_12_22_2","article-title":"Few-Shot Tire Tracks Recognition Based on Metric Learning","volume":"132","author":"Tang J.","year":"2025","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"e_1_2_12_23_2","unstructured":"DosovitskiyA.et al. An Image is Worth 16\u00d716 Words: Transformers for Image Recognition at Scale Proceedings of the International Conference on Learning Representations 2021 ICLR."},{"key":"e_1_2_12_24_2","doi-asserted-by":"crossref","unstructured":"WuJ. ZhangR. andWangY. Few-Shot Composition Learning for Image Retrieval With Limited Samples Proceedings of the AAAI Conference on Artificial Intelligence 2023 IEEE 2565\u20132573 https:\/\/doi.org\/10.1609\/aaai.v37i2.25354.","DOI":"10.1609\/aaai.v37i2.25354"}],"container-title":["International Journal of Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/int\/7723883","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1155\/int\/7723883","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/int\/7723883","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T11:06:20Z","timestamp":1776855980000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/int\/7723883"}},"subtitle":[],"editor":[{"given":"Richard","family":"Murray","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2026,1]]},"references-count":24,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["10.1155\/int\/7723883"],"URL":"https:\/\/doi.org\/10.1155\/int\/7723883","archive":["Portico"],"relation":{},"ISSN":["0884-8173","1098-111X"],"issn-type":[{"value":"0884-8173","type":"print"},{"value":"1098-111X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1]]},"assertion":[{"value":"2026-01-21","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-03-23","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-04-22","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"7723883"}}