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DC Field | Value | Language |
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dc.contributor.author | Benkamouch, Wiam | - |
dc.date.accessioned | 2023-11-21T10:26:29Z | - |
dc.date.available | 2023-11-21T10:26:29Z | - |
dc.date.issued | 2023 | - |
dc.identifier.uri | http://dspace.univ-guelma.dz/jspui/handle/123456789/14924 | - |
dc.description.abstract | The 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.iso | fr | en_US |
dc.publisher | university of guelma | en_US |
dc.subject | Object detection, machine learning, deep learning, learning, video. | en_US |
dc.title | Détection d’objet s et deep Learning dans un trafic routier | en_US |
dc.type | Working Paper | en_US |
Appears in Collections: | Master |
Files in This Item:
File | Description | Size | Format | |
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BENKEMOUCH_WIAM_F5.pdf | 3,84 MB | Adobe PDF | View/Open |
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