{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,7]],"date-time":"2025-06-07T04:04:29Z","timestamp":1749269069764,"version":"3.41.0"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2025,6,6]],"date-time":"2025-06-06T00:00:00Z","timestamp":1749168000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,6,6]],"date-time":"2025-06-06T00:00:00Z","timestamp":1749168000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SN COMPUT. SCI."],"DOI":"10.1007\/s42979-025-04034-w","type":"journal-article","created":{"date-parts":[[2025,6,6]],"date-time":"2025-06-06T11:48:14Z","timestamp":1749210494000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Automated Vehicle Damage Inspection: A Comprehensive Evaluation of Deep Learning Models and Real-World Applicability"],"prefix":"10.1007","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-0665-1048","authenticated-orcid":false,"given":"Onikepo D.","family":"Amodu","sequence":"first","affiliation":[]},{"given":"Oluwaseun","family":"Lottu","sequence":"additional","affiliation":[]},{"given":"Ridwan","family":"Imran","sequence":"additional","affiliation":[]},{"given":"Adel","family":"Shaban","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,6,6]]},"reference":[{"issue":"7","key":"4034_CR1","first-page":"40","volume":"51","author":"A Alam","year":"2012","unstructured":"Alam A, Asari VK, Ahmad MO. A survey on computer vision-based damage detection methods for vehicles. Intern J Comp Appl. 2012;51(7):40\u20135.","journal-title":"Intern J Comp Appl"},{"key":"4034_CR2","doi-asserted-by":"publisher","DOI":"10.1111\/jfr3.12475","author":"E Mart\u00ednez-Gomariz","year":"2018","unstructured":"Mart\u00ednez-Gomariz E, G\u00f3mez M, Russo B, S\u00e1nchez P, Montes J-A. Methodology for the damage assessment of vehicles exposed to flooding in urban areas. J Flood Risk Manag. 2018. https:\/\/doi.org\/10.1111\/jfr3.12475.","journal-title":"J Flood Risk Manag"},{"key":"4034_CR3","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1493","author":"R Marcinkevi\u010ds","year":"2023","unstructured":"Marcinkevi\u010ds R, Vogt JE. Interpretable and explainable machine learning: a methods-centric overview with concrete examples. WIREs Data Min Knowl Discovery. 2023. https:\/\/doi.org\/10.1002\/widm.1493.","journal-title":"WIREs Data Min Knowl Discovery"},{"key":"4034_CR4","doi-asserted-by":"publisher","DOI":"10.32473\/flairs.v34i1.128473","author":"L Li","year":"2021","unstructured":"Li L, Ono K, Ngan C-K. A deep learning and transfer learning approach for vehicle damage detection. Intern Conf Proceed. 2021. https:\/\/doi.org\/10.32473\/flairs.v34i1.128473.","journal-title":"Intern Conf Proceed"},{"key":"4034_CR5","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S and Sun J.Deep residual learning for image recognition. 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp. 770\u2013778, 2016, https:\/\/doi.org\/10.1109\/cvpr.2016.90.","DOI":"10.1109\/cvpr.2016.90"},{"key":"4034_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.scs.2023.104649","author":"MdM Rahman","year":"2023","unstructured":"Rahman MdM, Thill J-C. Impacts of connected and autonomous vehicles on urban transportation and environment: A comprehensive review. Sustain Cities Soc. 2023. https:\/\/doi.org\/10.1016\/j.scs.2023.104649.","journal-title":"Sustain Cities Soc"},{"key":"4034_CR7","doi-asserted-by":"publisher","DOI":"10.24996\/ijs.2021.62.6.30","author":"MN Abdullah","year":"2021","unstructured":"Abdullah MN, Ali YH. Vehicles detection system at different weather conditions. Iraqi J Sci. 2021. https:\/\/doi.org\/10.24996\/ijs.2021.62.6.30.","journal-title":"Iraqi J Sci"},{"key":"4034_CR8","doi-asserted-by":"publisher","DOI":"10.48175\/ijarsct-5414","author":"B Mallikarjuna","year":"2022","unstructured":"Mallikarjuna B, Arun Kumar KL. Vehicle damage detection and classification using image processing. Int J Adv Resin Sci Commun Technol. 2022. https:\/\/doi.org\/10.48175\/ijarsct-5414.","journal-title":"Int J Adv Resin Sci Commun Technol"},{"issue":"15","key":"4034_CR9","doi-asserted-by":"publisher","first-page":"21741","DOI":"10.1007\/s11042-022-12567-y","volume":"81","author":"K Ahmed","year":"2022","unstructured":"Ahmed K, Gad MA, Aboutabl AE. Performance evaluation of salient object detection techniques. Multimedia Tool Appl. 2022;81(15):21741\u201377. https:\/\/doi.org\/10.1007\/s11042-022-12567-y.","journal-title":"Multimedia Tool Appl"},{"issue":"2","key":"4034_CR10","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1109\/mvt.2019.2892497","volume":"14","author":"S Zang","year":"2019","unstructured":"Zang S, Ding M, Smith D, Tyler P, Rakotoarivelo T, Kaafar MA. The Impact of adverse weather conditions on autonomous vehicles: how rain, snow, fog, and hail affect the performance of a self-driving Car. Veh Technol Mag. 2019;14(2):103\u201311. https:\/\/doi.org\/10.1109\/mvt.2019.2892497.","journal-title":"Veh Technol Mag"},{"key":"4034_CR11","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-024-51185-0","author":"I Rooda","year":"2024","unstructured":"Rooda I, et al. In-depth analysis of transcriptomes in ovarian cortical follicles from children and adults reveals interfollicular heterogeneity. Nat Commun. 2024. https:\/\/doi.org\/10.1038\/s41467-024-51185-0.","journal-title":"Nat Commun"},{"key":"4034_CR12","doi-asserted-by":"publisher","unstructured":"Caruana R, Lou Y, Gehrke J, Koch P, Sturm M and. Elhadad N . Intelligible models for healthcare. Proceedings of the 21th ACM SIGKDD International conference on knowledge discovery and data mining - KDD \u201915, 2015. https:\/\/doi.org\/10.1145\/2783258.2788613.","DOI":"10.1145\/2783258.2788613"},{"key":"4034_CR13","doi-asserted-by":"publisher","DOI":"10.3389\/fenrg.2021.652801","author":"C Fan","year":"2021","unstructured":"Fan C, Chen M, Wang X, Wang J, Huang B. A review on data preprocessing techniques toward efficient and reliable knowledge discovery from building operational data. Front Energy Res. 2021. https:\/\/doi.org\/10.3389\/fenrg.2021.652801.","journal-title":"Front Energy Res"},{"key":"4034_CR14","doi-asserted-by":"publisher","unstructured":". Girshick R. Fast R-CNN. 2015 IEEE International conference on computer vision (ICCV), pp. 1440\u20131448. 2015. https:\/\/doi.org\/10.1109\/iccv.2015.169.","DOI":"10.1109\/iccv.2015.169"},{"key":"4034_CR15","doi-asserted-by":"publisher","DOI":"10.54254\/2755-2721\/88\/20241614","author":"Y Mo","year":"2024","unstructured":"Mo Y. A comprehensive review of models for vehicle detection based on computer vision analysis in autonomous vehicle. Appl Comput Eng. 2024. https:\/\/doi.org\/10.54254\/2755-2721\/88\/20241614.","journal-title":"Appl Comput Eng"},{"key":"4034_CR16","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-021-03612-z","author":"Y Kumar","year":"2022","unstructured":"Kumar Y, Koul A, Singla R, Ijaz MF. Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda. J Ambient Intell Humaniz Comput. 2022. https:\/\/doi.org\/10.1007\/s12652-021-03612-z.","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"4034_CR17","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58583-9_34","author":"B Zoph","year":"2020","unstructured":"Zoph B, Cubuk ED, Ghiasi G, Lin T-Y, Shlens J, Le QV. Learning data augmentation strategies for object detection. Comp Vision. 2020. https:\/\/doi.org\/10.1007\/978-3-030-58583-9_34.","journal-title":"Comp Vision"},{"key":"4034_CR18","doi-asserted-by":"publisher","unstructured":"Lin T.-Y, Goyal P, Girshick R, He K and Dollar P.Focal Loss for Dense Object Detection. 2017 IEEE International Conference on Computer Vision (ICCV). 2017. https:\/\/doi.org\/10.1109\/iccv.2017.324.","DOI":"10.1109\/iccv.2017.324"},{"key":"4034_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2020\/3189691","volume":"2020","author":"N-D Nguyen","year":"2020","unstructured":"Nguyen N-D, Do T, Ngo TD, Le D-D. An evaluation of deep learning methods for small object detection. J Elect Comp Eng. 2020;2020:1\u201318. https:\/\/doi.org\/10.1155\/2020\/3189691.","journal-title":"J Elect Comp Eng"},{"key":"4034_CR20","doi-asserted-by":"publisher","unstructured":"Redmon J, Divvala S, Girshick R., and Farhadi A. You only look once: unified, real-time object detection. 2016 IEEE Conference on computer vision and pattern recognition (CVPR), pp. 779\u2013788. 2016. https:\/\/doi.org\/10.1109\/cvpr.2016.91.","DOI":"10.1109\/cvpr.2016.91"},{"key":"4034_CR21","doi-asserted-by":"publisher","unstructured":"Ribeiro M, Singh S, and Guestrin C. Why should I trust you?\u2019: explaining the predictions of any classifier. Proceedings of the 2016 conference of the north american chapter of the association for computational linguistics: demonstrations, 2016. https:\/\/doi.org\/10.18653\/v1\/n16-3020.","DOI":"10.18653\/v1\/n16-3020"},{"key":"4034_CR22","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-019-01228-7","author":"RR Selvaraju","year":"2020","unstructured":"Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D. Grad-CAM: visual explanations from deep networks via gradient-based localization. Int J Comput Vision. 2020. https:\/\/doi.org\/10.1007\/s11263-019-01228-7.","journal-title":"Int J Comput Vision"},{"key":"4034_CR23","doi-asserted-by":"publisher","DOI":"10.48550\/arxiv.2205.14686","author":"J Al-afandi","year":"2022","unstructured":"Al-afandi J, Magyar B, Horv\u00e1th A. Saliency map based data augmentation. ArXiv (Cornell University). 2022. https:\/\/doi.org\/10.48550\/arxiv.2205.14686.","journal-title":"ArXiv (Cornell University)"},{"key":"4034_CR24","doi-asserted-by":"publisher","unstructured":"Brian Kenji Iwana, Kuroki R, and Uchida S. Explaining convolutional neural networks using softmax gradient layer-wise relevance propagation. 2019, https:\/\/doi.org\/10.1109\/iccvw.2019.00513.","DOI":"10.1109\/iccvw.2019.00513"},{"key":"4034_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.sysarc.2021.102152","author":"S Srivastava","year":"2021","unstructured":"Srivastava S, Narayan S, Mittal S. A survey of deep learning techniques for vehicle detection from UAV images. J Syst Architect. 2021. https:\/\/doi.org\/10.1016\/j.sysarc.2021.102152.","journal-title":"J Syst Architect"},{"issue":"7553","key":"4034_CR26","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y Bengio","year":"2015","unstructured":"Bengio Y, Lecun Y, Hinton G. Deep learning. Nature. 2015;521(7553):436\u201344.","journal-title":"Nature"},{"key":"4034_CR27","doi-asserted-by":"publisher","DOI":"10.56726\/IRJMETS29626","author":"X Jiang","year":"2021","unstructured":"Jiang X, Lu H, Lin Z. Vehicle damage detection and classification using deep learning. Sensors. 2021. https:\/\/doi.org\/10.56726\/IRJMETS29626.","journal-title":"Sensors"},{"issue":"2","key":"4034_CR28","first-page":"103","volume":"7","author":"Y Li","year":"2020","unstructured":"Li Y, Zhang J, Lin C, Yang Y. Deep learning-based vehicle damage detection: a review. J Traf Transport Eng. 2020;7(2):103\u201319.","journal-title":"J Traf Transport Eng"},{"key":"4034_CR29","unstructured":"Nguyen TA, & Park D.S. Automatic vehicle damage classification based on deep convolutional neural networks. In Proceedings of the 23rd International conference on pattern recognition (ICPR) (pp. 3002\u20133007). 2016."},{"key":"4034_CR30","unstructured":"Qian F, Sun X, Ma Y, & Yu L. Detection and classification of vehicle damage based on point cloud. In Proceedings of the 2019 3rd International conference on mechanical, control and automation engineering (pp. 117\u2013121). 2019."},{"key":"4034_CR31","doi-asserted-by":"crossref","unstructured":"Amodu OD, Shaban A, and Akinade G. Revolutionizing vehicle damage inspection: a deep learning approach for automated detection and classification. In IoTBDS (pp.199\u2013208). 2024.","DOI":"10.5220\/0012630700003705"}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-025-04034-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-025-04034-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-025-04034-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,6]],"date-time":"2025-06-06T11:48:19Z","timestamp":1749210499000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-025-04034-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,6]]},"references-count":31,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["4034"],"URL":"https:\/\/doi.org\/10.1007\/s42979-025-04034-w","relation":{},"ISSN":["2661-8907"],"issn-type":[{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,6]]},"assertion":[{"value":"27 October 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 April 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 June 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":"No conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Not Applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"Not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Research Involving Human and \/or Animals"}}],"article-number":"525"}}