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dc.contributor.author Belaalia Asma, Namoune Khaoula
dc.date.accessioned 2023-10-02T12:44:40Z
dc.date.available 2023-10-02T12:44:40Z
dc.date.issued 2023-07
dc.identifier.uri https://dspace.univ-bba.dz:443/xmlui/handle/123456789/4007
dc.description.abstract The thesis focused on implementing and enhancing object detection techniques using computer vision. Two main strategies were explored: Convolutional Neural Networks (CNN) and the You Only Look Once (YOLO) approach. The study began by examining the fundamentals of neural networks to gain a better understanding of object detection mechanisms. A custom CNN architecture was then developed and implemented to suit the specific datasets. Additionally, the performance of the proposed model was compared to YOLO through the implementation of YOLOv5 and YOLOv8. This allowed for the evaluation of the effectiveness of the custom approach and analysis of the results obtained from the different models. en_US
dc.language.iso en en_US
dc.publisher faculté des sciences et de la technologie* univ bba en_US
dc.relation.ispartofseries ;EL/M/2023/07
dc.subject YOLOv5, YOLOv8, Convolutional Neural Network, computer vision, neural network, artificial intelligence (AI) en_US
dc.title Object Detection Using YOLO en_US
dc.type Thesis en_US


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