{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deep-learning-based-vehicle-make-model","title":"Deep Learning Based Vehicle Make-Model Classification","arxiv_id":"1809.00953","date":"2018-08-23","proceeding":null,"authors":["Burak Satar","Ahmet Emir Dirik"],"abstract":"This paper studies the problems of vehicle make & model classification. Some\nof the main challenges are reaching high classification accuracy and reducing\nthe annotation time of the images. To address these problems, we have created a\nfine-grained database using online vehicle marketplaces of Turkey. A pipeline\nis proposed to combine an SSD (Single Shot Multibox Detector) model with a CNN\n(Convolutional Neural Network) model to train on the database. In the pipeline,\nwe first detect the vehicles by following an algorithm which reduces the time\nfor annotation. Then, we feed them into the CNN model. It is reached\napproximately 4% better classification accuracy result than using a\nconventional CNN model. Next, we propose to use the detected vehicles as ground\ntruth bounding box (GTBB) of the images and feed them into an SSD model in\nanother pipeline. At this stage, it is reached reasonable classification\naccuracy result without using perfectly shaped GTBB. Lastly, an application is\nimplemented in a use case by using our proposed pipelines. It detects the\nunauthorized vehicles by comparing their license plate numbers and make &\nmodels. It is assumed that license plates are readable.","url_abs":"http://arxiv.org/abs/1809.00953v2","url_pdf":"http://arxiv.org/pdf/1809.00953v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"deep-learning-based-vehicle-make-model","repo_url":"https://github.com/buraksatar/car-detection-model-prediction","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"model","task_name":"model"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"non-maximum-suppression","method_name":"Non Maximum Suppression"},{"method_slug":"ssd","method_name":"SSD"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}