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Design of a Real-Time-Integrated System Based on Stereovision and YOLOv5 to Detect Objects
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Author(s): Oumayma Rachidi (ENSAM, Moulay Ismail University of Meknes, Morocco), Ed-Dahmani Chafik (ENSAM, Moulay Ismail University of Meknes, Morocco)and Badr Bououlid (ENSAM, Moulay Ismail University of Meknes, Morocco)
Copyright: 2024
Pages: 15
Source title:
Enhancing Performance, Efficiency, and Security Through Complex Systems Control
Source Author(s)/Editor(s): Idriss Chana (ESTM, Moulay Ismail University of Meknès, Morocco), Aziz Bouazi (ESTM, Moulay Ismail University of Meknès, Morocco)and Hussain Ben-azza (ENSAM, Moulay Ismail University of Meknes, Morocco)
DOI: 10.4018/979-8-3693-0497-6.ch016
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Abstract
Real-time object detection represents a major part in the development of advanced driver assistance systems (ADAS). Pedestrian detection has become one of the most important tasks in the field of object detection due to the increasing number of road accidents. This study concerns the design and implementation of a Raspberry Pi 4-based embedded stereovision system to detect 80 object classes including persons and estimate 3D distance for traffic safety. Stereo camera calibration and deep learning algorithms are discussed. The study shows the system's design and a custom stereo camera designed and built using 3D printer as well as the implementation of YOLOv5s in the Raspberry Pi 4. The object detector is trained on the context object detection task (COCO) 2020 dataset and was tested using one of the two cameras. The Raspberry Pi displays a live video including bounding boxes and the number of frames per second (FPS).
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