Abstract

Addressing the escalating necessity for proficient and dependable autonomous vehicle systems, this research proposes an advanced object detection approach harnessing the YOLOv8 algorithm. Object detection plays a pivotal role in the functionality of autonomous driving systems, enabling vehicles to perceive and react to their surroundings promptly. YOLOv8, an upgraded iteration of the YOLO algorithm, is acclaimed for its rapidity and precision in object detection endeavors. Our proposed model, distinguished by enhancements in network architecture and training methodologies, surpasses existing models in terms of detection precision and computational efficacy. Through an exhaustive examination, we delve into the network architecture, training regimen, and assessment metrics of the YOLOv8-based model. Experimental findings showcase the model's commendable performance in both quantitative and qualitative assessments, highlighting its resilience in identifying pedestrians, vehicles, traffic signs, and other pertinent objects across varied driving scenarios. The model's predicted outputs attest to its adeptness in precisely localizing and categorizing objects of interest, thereby augmenting the safety and efficiency of autonomous driving systems.