{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T16:54:37Z","timestamp":1780332877052,"version":"3.54.1"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2025,1,4]],"date-time":"2025-01-04T00:00:00Z","timestamp":1735948800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,1,4]],"date-time":"2025-01-04T00:00:00Z","timestamp":1735948800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2021QE059"],"award-info":[{"award-number":["ZR2021QE059"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2025,3]]},"DOI":"10.1007\/s00521-024-10898-5","type":"journal-article","created":{"date-parts":[[2025,1,4]],"date-time":"2025-01-04T22:21:17Z","timestamp":1736029277000},"page":"5795-5813","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Intelligent recognition system of in-service tire damage driven by strong combination augmentation and contrast fusion"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6981-7794","authenticated-orcid":false,"given":"Dagang","family":"Shen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9966-1906","authenticated-orcid":false,"given":"Jinfeng","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jihong","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,1,4]]},"reference":[{"key":"10898_CR1","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1016\/j.ijfatigue.2018.07.009","volume":"119","author":"W Nyaaba","year":"2019","unstructured":"Nyaaba W, Frimpong S, Anani A (2019) Fatigue damage investigation of ultra-large tire components. Int J Fatigue 119:247\u2013260","journal-title":"Int J Fatigue"},{"issue":"23","key":"10898_CR2","doi-asserted-by":"publisher","first-page":"12333","DOI":"10.3390\/app122312333","volume":"12","author":"K Drozd","year":"2022","unstructured":"Drozd K, Tarkowski S, Caban J, Nieoczym A, Vr\u00e1bel J, Krzysiak Z (2022) Analysis of truck tractor tire damage in the context of the study of road accident causes. Appl Sci 12(23):12333","journal-title":"Appl Sci"},{"issue":"3","key":"10898_CR3","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1556\/606.2022.00588","volume":"17","author":"Mohammad Fahad","year":"2022","unstructured":"Fahad Mohammad, Nagy Richard (2022) Fatigue damage analysis of pavements under autonomous truck tire passes. Pollack Periodica 17(3):59\u201364. https:\/\/doi.org\/10.1556\/606.2022.00588","journal-title":"Pollack Periodica"},{"key":"10898_CR4","unstructured":"Singh S (2015) Critical reasons for crashes investigated in the national motor vehicle crash causation survey. In: Technical report, national center for statistics and analysis"},{"issue":"21","key":"10898_CR5","doi-asserted-by":"publisher","first-page":"7073","DOI":"10.3390\/s21217073","volume":"21","author":"I Kuric","year":"2021","unstructured":"Kuric I, Klar\u00e1k J, S\u00e1ga M, C\u00edsar M, Hajdu\u010d\u00edk A, Wiecek D (2021) Analysis of the possibilities of tire-defect inspection based on unsupervised learning and deep learning. Sensors 21(21):7073","journal-title":"Sensors"},{"key":"10898_CR6","unstructured":"Corbally R, Malekjafarian A A data-driven approach for drive-by damage detection in bridges considering the influence of temperature change. In: Engineering structures"},{"key":"10898_CR7","doi-asserted-by":"crossref","unstructured":"Huber S, Preindl P, Betz J (2022) Tireeye: Optical on-board tire wear detection. In: Annual conference of the PHM society, vol 14","DOI":"10.36001\/phmconf.2022.v14i1.3242"},{"key":"10898_CR8","doi-asserted-by":"publisher","first-page":"438","DOI":"10.1007\/s11668-016-0104-3","volume":"16","author":"AC Kotchon","year":"2016","unstructured":"Kotchon AC, Lipsett MG, Nobes DS (2016) Damage detection in tires using image-based strain measurements. J Fail Anal Prev 16:438\u2013448","journal-title":"J Fail Anal Prev"},{"key":"10898_CR9","doi-asserted-by":"crossref","unstructured":"Wang C, Taylor BD (2011) Sansec temperature sensor for tire safety monitoring application. In: 2011 Future of instrumentation international workshop, IEEE, pp 1\u20134","DOI":"10.1109\/FIIW.2011.6476838"},{"issue":"1","key":"10898_CR10","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1080\/15397734.2018.1496842","volume":"47","author":"P Behroozinia","year":"2019","unstructured":"Behroozinia P, Khaleghian S, Taheri S et al (2019) Damage diagnosis in intelligent tires using time-domain and frequency-domain analysis. Mech Based Des Struct Mach 47(1):54\u201366","journal-title":"Mech Based Des Struct Mach"},{"key":"10898_CR11","doi-asserted-by":"publisher","first-page":"106556","DOI":"10.1016\/j.ijfatigue.2021.106556","volume":"154","author":"F Bj\u00f8rheim","year":"2022","unstructured":"Bj\u00f8rheim F, Siriwardane SC, Pavlou D (2022) A review of fatigue damage detection and measurement techniques. Int J Fatigue 154:106556","journal-title":"Int J Fatigue"},{"key":"10898_CR12","doi-asserted-by":"publisher","first-page":"104139","DOI":"10.1016\/j.autcon.2022.104139","volume":"135","author":"S Shim","year":"2022","unstructured":"Shim S, Kim J, Lee S-W, Cho G-C (2022) Road damage detection using super-resolution and semi-supervised learning with generative adversarial network. Autom Constr 135:104139","journal-title":"Autom Constr"},{"issue":"3","key":"10898_CR13","doi-asserted-by":"publisher","first-page":"1771","DOI":"10.1007\/s00366-021-01584-4","volume":"39","author":"Y He","year":"2023","unstructured":"He Y, Zhang L, Chen Z, Li CY (2023) A framework of structural damage detection for civil structures using a combined multi-scale convolutional neural network and echo state network. Eng Comput 39(3):1771\u20131789","journal-title":"Eng Comput"},{"key":"10898_CR14","doi-asserted-by":"crossref","unstructured":"Reddy C, Anisha P, Mohana R (2021) Assessing wear out of tyre using opencv & convolutional neural networks. In: Journal of physics: conference series, IOP Publishing, vol. 2089, p 012001","DOI":"10.1088\/1742-6596\/2089\/1\/012001"},{"key":"10898_CR15","doi-asserted-by":"crossref","unstructured":"Zhang S, Wu Y, Chang J (2020) Design of tire damage image recognition system based on deep learning. In: Journal of physics: conference series, IOP Publishing, Vol 1631, p 012015","DOI":"10.1088\/1742-6596\/1631\/1\/012015"},{"issue":"2","key":"10898_CR16","doi-asserted-by":"publisher","first-page":"1264","DOI":"10.1109\/TITS.2020.2967316","volume":"22","author":"W Kazmi","year":"2020","unstructured":"Kazmi W, Nabney I, Vogiatzis G et al (2020) An efficient industrial system for vehicle tyre (tire) detection and text recognition using deep learning. IEEE Trans Intell Transp Syst 22(2):1264\u20131275","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"10898_CR17","doi-asserted-by":"crossref","unstructured":"Wu J, Guo S, Zhang X, et al (2015) Simulation on the stress filed of radial tire with tire tread wear under static contact condition. In: Applied mechanics trans tech publications Ltd, vol. 703, pp 245\u2013249","DOI":"10.4028\/www.scientific.net\/AMM.703.245"},{"key":"10898_CR18","unstructured":"Gong M, Chen J, Sun Y Multiscale finite-element analysis of damage behavior of curved ramp bridge deck pavement considering tire\u2013bridge interaction effect. Journal of Engineering Mechanics"},{"key":"10898_CR19","doi-asserted-by":"crossref","unstructured":"Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR\u201905), IEEE, vol. 1, pp 886\u2013893","DOI":"10.1109\/CVPR.2005.177"},{"issue":"7","key":"10898_CR20","first-page":"1405","volume":"36","author":"X Niu","year":"2008","unstructured":"Niu X, Jiao Y (2008) An overview of perceptual hashing. ACTA Electonica Sinica 36(7):1405","journal-title":"ACTA Electonica Sinica"},{"key":"10898_CR21","unstructured":"Chen T, Kornblith S, Norouzi M, al (2020) A simple framework for contrastive learning of visual representations. In: International conference on PMLR, pp 1597\u20131607"},{"key":"10898_CR22","unstructured":"Grill J-B, Strub F, Altch\u00e9 F, al (2020) Bootstrap your own latent: a new approach to self-supervised learning. arXiv preprint arXiv:2006.07733"},{"key":"10898_CR23","unstructured":"Sohn K (2016) Improved deep metric learning with multi-class n-pair loss objective. Advances in neural information processing systems 29"},{"key":"10898_CR24","unstructured":"Howard A, Zhmoginov A, Chen B al (2018) Inverted residuals and linear bottlenecks: Mobile networks for classification, detection and segmentation"},{"issue":"2","key":"10898_CR25","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham M, Van Gool L, Williams CKI et al (2010) The pascal visual object classes (voc) challenge. Int J Comput Vision 88(2):303\u2013338","journal-title":"Int J Comput Vision"},{"key":"10898_CR26","doi-asserted-by":"crossref","unstructured":"Chattopadhay A, Sarkar A, Howlader P, et al (2018) Grade-CAM++: Generalized gradient-based visual explanations for deep convolutional networks. In: 2018 IEEE winter conference on applications of computer vision (WACV), pp 839\u2013847. IEEE","DOI":"10.1109\/WACV.2018.00097"},{"key":"10898_CR27","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"10898_CR28","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7132\u20137141","DOI":"10.1109\/CVPR.2018.00745"},{"key":"10898_CR29","doi-asserted-by":"crossref","unstructured":"Karim SM, Rahman Y, Hai MA, Mahfuza R (2021) Tire wear detection for accident avoidance employing convolutional neural networks. In: 2021 8th NAFOSTED conference on information and computer science (NICS), pp 364\u2013368. IEEE","DOI":"10.1109\/NICS54270.2021.9701504"},{"issue":"2","key":"10898_CR30","first-page":"127","volume":"21","author":"VGV Mahesh","year":"2023","unstructured":"Mahesh VGV, Raj ANJ (2023) Formulation of pattern recognition framework-analysis and detection of tyre cracks utilizing integrated texture features and ensemble learning methods. Adv Electric Electron Eng 21(2):127\u2013143","journal-title":"Adv Electric Electron Eng"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10898-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-10898-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10898-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,28]],"date-time":"2025-02-28T21:44:53Z","timestamp":1740779093000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-10898-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,4]]},"references-count":30,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2025,3]]}},"alternative-id":["10898"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-10898-5","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,4]]},"assertion":[{"value":"7 May 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 December 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 January 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors do not have any Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}