Pedestrian and Vehicle Detection Using the YOLO Framework and Multi-Model Approaches

Object detection is a significant problem in the field of computer vision, and it has widespread applications in areas such as autonomous driving, security surveillance, and medical imaging. This paper delves into an in-depth study based on the You Only Look Once (YOLO) object detection framework. By conducting experiments on multiple commonly used object detection datasets. We evaluated the performance of the YOLO model in terms of both accuracy and real-time capability. Through experimental validation, the design of the YOLO model enables simultaneous object detection and localization, significantly improving detection efficiency.