Please use this identifier to cite or link to this item: http://dspace.univ-guelma.dz/jspui/handle/123456789/14924
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dc.contributor.authorBenkamouch, Wiam-
dc.date.accessioned2023-11-21T10:26:29Z-
dc.date.available2023-11-21T10:26:29Z-
dc.date.issued2023-
dc.identifier.urihttp://dspace.univ-guelma.dz/jspui/handle/123456789/14924-
dc.description.abstractThe objective is to design a system for automatic analysis of highway scenes. The road network plays a crucial role in the development of a country as it serves as the foundation for various sectors such as transportation of goods and people. Therefore, the integration of driving assistance systems is crucial in the design of new vehicles. In this work, we have developed a vehicle detection application based on video. To achieve this goal, we have implemented three algorithms. The first one is a part of machine learning (SVM), and the other two are part of deep learning (YOLOV5 and YOLOV8)en_US
dc.language.isofren_US
dc.publisheruniversity of guelmaen_US
dc.subjectObject detection, machine learning, deep learning, learning, video.en_US
dc.titleDétection d’objet s et deep Learning dans un trafic routieren_US
dc.typeWorking Paperen_US
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