Comparative Analysis of YOLOv8 and RT-DETR for Real-Time Object Detection in Advanced Driver Assistance Systems
Abstract
Object detection is critical for Advanced Driver Assistance Systems (ADAS), enhancing vehicle navigation and safety by identifying and responding to various traffic objects. This thesis evaluates the performance of YOLOv8 and Real-Time Detection Transformer (RT-DETR) models, which represent leading CNN and transformer-based architectures for real-time object detection, in the domain of ADAS. Despite RT-DETR's superior performance over YOLOv8 on the COCO dataset, this study explores its effectiveness in ADAS applications. A comprehensive analysis was conducted using seven models (five YOLOv8 and two RT-DETR variants) across five datasets, including BDD100k and four Roadlab datasets. The evaluation focused on accuracy (Mean Average Precision or mAP), inference speed (latency), and F1 scores, highlighting YOLOv8's superior performance in both accuracy and speed, making it more suitable for ADAS tasks. The study also demonstrated YOLOv8 models' faster learning dynamics and better management of class imbalance, ensuring balanced detection of critical objects like pedestrians and cyclists. The findings underscore YOLOv8's current advantage in real-time detection for ADAS, while also identifying potential areas for future research to optimize transformer-based models. This work contributes to the development of safer and more reliable autonomous vehicles.