{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T18:15:00Z","timestamp":1775067300825,"version":"3.50.1"},"reference-count":37,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,11,2]],"date-time":"2022-11-02T00:00:00Z","timestamp":1667347200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004744","name":"Innoviris","doi-asserted-by":"publisher","award":["DRIvINg"],"award-info":[{"award-number":["DRIvINg"]}],"id":[{"id":"10.13039\/501100004744","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004744","name":"Innoviris","doi-asserted-by":"publisher","award":["G094122N"],"award-info":[{"award-number":["G094122N"]}],"id":[{"id":"10.13039\/501100004744","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003130","name":"Research Foundation\u2014Flanders","doi-asserted-by":"publisher","award":["DRIvINg"],"award-info":[{"award-number":["DRIvINg"]}],"id":[{"id":"10.13039\/501100003130","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003130","name":"Research Foundation\u2014Flanders","doi-asserted-by":"publisher","award":["G094122N"],"award-info":[{"award-number":["G094122N"]}],"id":[{"id":"10.13039\/501100003130","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In recent years, Vehicle Make and Model Recognition (VMMR) has attracted a lot of attention as it plays a crucial role in Intelligent Transportation Systems (ITS). Accurate and efficient VMMR systems are required in real-world applications including intelligent surveillance and autonomous driving. The paper introduces a new large-scale dataset and a novel deep learning paradigm for VMMR. A new large-scale dataset dubbed Diverse large-scale VMM (DVMM) is proposed collecting image-samples with the most popular vehicle brands operating in Europe. A novel VMMR framework is proposed which follows a two-branch architecture performing make and model recognition respectively. A two-stage training procedure and a novel decision module are proposed to process the make and model predictions and compute the final model prediction. In addition, a novel metric based on the true positive rate is proposed to compare classification confusion of the proposed 2B\u20132S and the baseline methods. A complex experimental validation is carried out, demonstrating the generality, diversity, and practicality of the proposed DVMM dataset. The experimental results show that the proposed framework provides 93.95% accuracy over the more diverse DVMM dataset and 95.85% accuracy over traditional VMMR datasets. The proposed two-branch approach outperforms the conventional one-branch approach for VMMR over small-, medium-, and large-scale datasets by providing lower vehicle model confusion and reduced inter-make ambiguity. The paper demonstrates the advantages of the proposed two-branch VMMR paradigm in terms of robustness and lower confusion relative to single-branch designs.<\/jats:p>","DOI":"10.3390\/s22218439","type":"journal-article","created":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T03:53:07Z","timestamp":1667447587000},"page":"8439","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Framework for Vehicle Make and Model Recognition\u2014A New Large-Scale Dataset and an Efficient Two-Branch\u2013Two-Stage Deep Learning Architecture"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2501-9010","authenticated-orcid":false,"given":"Yangxintong","family":"Lyu","sequence":"first","affiliation":[{"name":"Department of Electronics and Informatics, Vrije Universiteit Brussel, 1050 Brussels, Belgium"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2202-1163","authenticated-orcid":false,"given":"Ionut","family":"Schiopu","sequence":"additional","affiliation":[{"name":"Department of Electronics and Informatics, Vrije Universiteit Brussel, 1050 Brussels, Belgium"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0688-8173","authenticated-orcid":false,"given":"Bruno","family":"Cornelis","sequence":"additional","affiliation":[{"name":"Department of Electronics and Informatics, Vrije Universiteit Brussel, 1050 Brussels, Belgium"},{"name":"Macq S.A.\/N.V., 1140 Brussels, Belgium"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7290-0428","authenticated-orcid":false,"given":"Adrian","family":"Munteanu","sequence":"additional","affiliation":[{"name":"Department of Electronics and Informatics, Vrije Universiteit Brussel, 1050 Brussels, Belgium"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Yang, L., Luo, P., Change Loy, C., and Tang, X. 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