Innovative Road Object Detection and Distance Estimation Framework Using Monocular Cameras for Advanced Driver Assistance Systems

Advanced Driver Assistance Systems (ADAS) rely on accurate road object detection, tracking, and distance estimation to enhance road safety.This paper presents ROD-YOLOv8, an innovative framework that leverages a monocular camera for these tasks within ADAS.By integrating the YOLOv8 model with transfer learning, the framework achieves high accuracy in road object detection.The model was retrained on the BDD100k dataset and further validated using COCO, KITTI, and PASCAL VOC datasets for comparison with existing models, achieving a mean average precision (mAP) of 0.782 and an F1 score of 0.874 on the COCO dataset.Object tracking is maintained through the Bot-Sort algorithm, ensuring consistent tracking of detected objects and providing continuous monitoring and future location prediction.Advanced camera calibration techniques enable accurate distance estimation between the camera and the detected objects, resulting in a mean absolute distance error of just 2.4 meters.Operating at an impressive 83 frames per second (FPS), ROD-YOLOv8 showcases its real-time capabilities, contributing to safer autonomous and assisted driving experiences.The proposed object detection model demonstrates improved performance compared to existing methods, particularly outperforming other methods like Mobile NET and earlier versions of YOLO in terms of precision, recall, and speed, measured in Frames Per Second (FPS), making it highly suitable for integration into ADAS applications.