{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T04:38:40Z","timestamp":1772858320740,"version":"3.50.1"},"reference-count":35,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2022,6,23]],"date-time":"2022-06-23T00:00:00Z","timestamp":1655942400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This study proposes a simple convolutional neural network (CNN)-based model for vehicle classification in low resolution surveillance images collected by a standard security camera installed distant from a traffic scene. In order to evaluate its effectiveness, the proposed model is tested on a new dataset containing tiny (100 \u00d7 100 pixels) and low resolution (96 dpi) vehicle images. The proposed model is then compared with well-known VGG16-based CNN models in terms of accuracy and complexity. Results indicate that although the well-known models provide higher accuracy, the proposed method offers an acceptable accuracy (92.9%) as well as a simple and lightweight solution for vehicle classification in low quality images. Thus, it is believed that this study might provide useful perception and understanding for further research on the use of standard low-cost cameras to enhance the ability of the intelligent systems such as intelligent transportation system applications.<\/jats:p>","DOI":"10.3390\/s22134740","type":"journal-article","created":{"date-parts":[[2022,6,23]],"date-time":"2022-06-23T22:43:00Z","timestamp":1656024180000},"page":"4740","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Deep Learning-Based Vehicle Classification for Low Quality Images"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5406-5198","authenticated-orcid":false,"given":"Sumeyra","family":"Tas","sequence":"first","affiliation":[{"name":"Graduate School of Natural and Applied Sciences, Atilim University, Incek Golbasi, Ankara 06830, Turkey"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5477-6387","authenticated-orcid":false,"given":"Ozgen","family":"Sari","sequence":"additional","affiliation":[{"name":"Graduate School of Natural and Applied Sciences, Atilim University, Incek Golbasi, Ankara 06830, Turkey"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9459-0042","authenticated-orcid":false,"given":"Yaser","family":"Dalveren","sequence":"additional","affiliation":[{"name":"Department of Avionics, Atilim University, Kizilcasar Mahallesi, Incek Golbasi, Ankara 06830, Turkey"}]},{"given":"Senol","family":"Pazar","sequence":"additional","affiliation":[{"name":"Department of Computer Programming, Biruni University, Istanbul 34010, Turkey"},{"name":"Ankageo Co. Ltd., Yildiz Technical University Ikitelli Technopark, Istanbul 34220, Turkey"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9739-7619","authenticated-orcid":false,"given":"Ali","family":"Kara","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Gazi University, Eti Mahallesi, Yukselis Sokak, Maltepe, Ankara 06570, Turkey"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0448-7613","authenticated-orcid":false,"given":"Mohammad","family":"Derawi","sequence":"additional","affiliation":[{"name":"Department of Electronic Systems, Norwegian University of Science and Technology, 2815 Gj\u00f8vik, Norway"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1109\/OJITS.2021.3096756","article-title":"Vehicle Classification in Intelligent Transport Systems: An Overview, Methods and Software Perspective","volume":"2","author":"Gholamhosseinian","year":"2021","journal-title":"IEEE Open J. 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