Automated Vehicle Detection and Real-time Number Plate Recognition

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dc.contributor.authorTalal Yassine Nouibat
dc.contributor.authorAbdelouahab Djoubair Benhamadouche
dc.contributor.authorENC/ Benhamadouche Abdelouahab
dc.date.accessioned2024-07-04T13:37:13Z
dc.date.available2024-07-04T13:37:13Z
dc.date.issued2024-07-04
dc.description.abstractEN In this project, we focused our work on the Algerian market and developed a system for automatic license plate recognition (ANPR) in real time. The system was trained using deep learning algorithms on a dataset of Algerian license plates with different photographing angles. It integrated neural processing units (NPU) to improve image processing performance and recognition speed. We compared the performance of different AI models, including Faster RCNN and YOLO. It showed that YOLOv5 model achieved a high detection accuracy of 99% in real time. This embedded system can be used in various areas of the Algerian market, such as parking control, vehicle tracking and access point control.
dc.identifier.urihttps://depot.univ-msila.dz/handle/123456789/43281
dc.language.isoen
dc.publisherUniversity of Msila
dc.relation.ispartofseries2024
dc.subjectAutomatic Number Plate Recognition
dc.subjectNeural Processing Unit
dc.subjectReal-Time
dc.subjectAlgerian License Plates
dc.subjectANPR
dc.subjectLPR
dc.subjectALPR
dc.subjectNPU
dc.subjectFaster RCNN
dc.subjectYOLO.
dc.titleAutomated Vehicle Detection and Real-time Number Plate Recognition
dc.typeThesis

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