Comparative Analysis of YOLO and Faster R-CNN Models for Detecting Traffic Object

The identification of traffic objects is a basic aspect of autonomous vehicle systems. It allows vehicles to detect different traffic entities such as cars, pedestrians, cyclists, and trucks in real-time. The accuracy and efficiency of object detection are crucial in ensuring the safety and reliability of autonomous vehicles. The focus of this work is a comparative analysis of two object detection models: YOLO (You Only Look Once) and Faster R-CNN (Region-based Convolutional Neural Networks) using the KITTI dataset. The KITTI dataset is a widely accepted reference dataset for work in autonomous vehicles. The evaluation included the performance of YOLOv3, YOLOv5, and Faster R-CNN on three established levels of difficulty. The three levels of difficulty range from Easy, Moderate, to Hard based on object exposure, lighting, and the existence of obstacles. The results of the work show that Faster R-CNN achieves maximum precision in detection of pedestrians and cyclists, while YOLOv5 has a good balance of speed and precision. As a result, YOLOv5 is found to be highly suitable for applications in real-time. In this aspect, YOLOv3 shows computational efficacy but displayed poor performance in more demanding scenarios. The work presents useful insights into the strength and limitation of these models. The results help in improving more resilient and efficient systems of detection of traffic objects, hence advancing the construction of more secure and reliable self-driving cars. Moreover, this study provides a comparative analysis of YOLO and Faster R-CNN models, highlighting key trade-offs and identifying YOLOv5 as a strong real-time candidate while emphasizing Faster R-CNN’s precision in challenging conditions.