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Title	Real-Time Object Detection In Autonomous Vehicles With Yolo
ID_Doc	44409
Authors	Alahdal N.M.; Abukhodair F.; Meftah L.H.; Cherif A.
Year	2024
Published	Procedia Computer Science, 246, C
DOI	http://dx.doi.org/10.1016/j.procs.2024.09.392
Abstract	AI analytics enables autonomous cars to detect and recognize objects, such as other vehicles, pedestrians, traffic signs, and obstacles, in real-time. Deep learning models, notably the You Only Look Once (YOLO) model, have demonstrated accuracy and speed in obstacle avoidance. However, current datasets are limited, lacking diversity and labeling, hindering their ability to represent real-world scenarios accurately. Besides, previous studies have focused extensively on specific object classes, such as pedestrians and vehicles, often neglecting other objects like bikes and road signs. To address this, we introduce a novel dataset tailored for AV environments, encompassing various road object types under different conditions. Our innovative methodology relies on self-supervised learning using the late YOLO version to improve model robustness with limited labeled data and AI-driven adaptive model optimization based on real-time feedback. We evaluate three YOLO architectures-YOLOv5, YOLOv7, and YOLOv8-customized for AV object detection. Our assessment covers everyday AV objects such as cars, pedestrians, bicycles, and road signs, emphasizing early detection. We employ the VSim-AV simulator dataset to ensure robust evaluation, augmented with preprocessing techniques to optimize data quality and model generalization. The study reveals that YOLOv5 and YOLOv8 outperform YOLOv7 regarding precision and recall across various object classes, with YOLOv5 leading at 1.3 ms/image and YOLOv8 at 3.3 ms/image. The mean average precision was 0.94 for YOLOv5, 0.441 for YOLOv7, and 0.927 for YOLOv8, highlighting the limitations in current literature and challenges in YOLO model performance. © 2024 The Authors.
Author Keywords	Artificial intelligence; Autonomous vehicles; Deep learning; Object detection; Simulation environment; Smart city; YOLO

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