Object Detection in Automatic Cars Using YOLO Model

Object detection plays a vital role in enabling autonomous vehicles to perceive and interpret their surroundings accurately and in real time. This paper presents the application of the YOLO (You Only Look Once) object detection algorithm within the perception system of self-driving cars. YOLO is a deep learning- based approach that performs object detection as a single regression problem, allowing for high-speed and accurate identification of multiple objects, such as vehicles, pedestrians, traffic lights, and road signs, in a single frame. By integrating YOLO into the vehicle’s vision system, the autonomous system can make timely decisions to ensure safe navigation and obstacle avoidance. This study discusses the architecture of YOLO, its real-time performance capabilities, and its effectiveness in dynamic driving environments. Experimental results demonstrate that YOLO provides a practical solution for real-time object detection in autonomous driving systems, offering a balanced trade-off between speed and accuracy necessary for safe and efficient operation.