{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T15:40:34Z","timestamp":1785512434872,"version":"3.56.0"},"reference-count":31,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2019,2,26]],"date-time":"2019-02-26T00:00:00Z","timestamp":1551139200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["GR 2016R1D1A3B03931911"],"award-info":[{"award-number":["GR 2016R1D1A3B03931911"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Make and model recognition (MMR) of vehicles plays an important role in automatic vision-based systems. This paper proposes a novel deep learning approach for MMR using the SqueezeNet architecture. The frontal views of vehicle images are first extracted and fed into a deep network for training and testing. The SqueezeNet architecture with bypass connections between the Fire modules, a variant of the vanilla SqueezeNet, is employed for this study, which makes our MMR system more efficient. The experimental results on our collected large-scale vehicle datasets indicate that the proposed model achieves 96.3% recognition rate at the rank-1 level with an economical time slice of 108.8 ms. For inference tasks, the deployed deep model requires less than 5 MB of space and thus has a great viability in real-time applications.<\/jats:p>","DOI":"10.3390\/s19050982","type":"journal-article","created":{"date-parts":[[2019,2,26]],"date-time":"2019-02-26T11:00:44Z","timestamp":1551178844000},"page":"982","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":104,"title":["Real-Time Vehicle Make and Model Recognition with the Residual SqueezeNet Architecture"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2581-5268","authenticated-orcid":false,"given":"Hyo Jong","family":"Lee","sequence":"first","affiliation":[{"name":"Division of Computer Science and Engineering, CAIIT, Chonbuk National University, Jeonju 54896, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ihsan","family":"Ullah","sequence":"additional","affiliation":[{"name":"Department of Robotics Engineering, Daegu Gyeongbuk Institute of Science and Technology, Daegu 42988, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weiguo","family":"Wan","sequence":"additional","affiliation":[{"name":"Division of Computer Science and Engineering, CAIIT, Chonbuk National University, Jeonju 54896, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongbin","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Electrics and Electronic Engineering, Shanghai University of Engineering Science, Shanghai 201620, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhijun","family":"Fang","sequence":"additional","affiliation":[{"name":"School of Electrics and Electronic Engineering, Shanghai University of Engineering Science, Shanghai 201620, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,2,26]]},"reference":[{"key":"ref_1","first-page":"58","article-title":"Intelligent transport systems in Korea","volume":"3","author":"Lim","year":"2012","journal-title":"IJEI Int. 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