Traffic sign detection from video for autonomous vehicles - Wong, Song Wang (2025)
sign detection from video plays a vital role in enhancing the safety and decision-making capabilities of autonomous vehicles and Advanced Driver Assistance Systems (ADAS). This project focuses on the development of a robust deep learning-based detection system utilizing the latest YOLO11 model to identify and classify traffic signs from recorded video feeds. The system was trained using a carefully prepared dataset consisting of 21,688 images across 18 traffic sign classes, collected under various real-world conditions such as illumination changes and occlusions. The YOLO11 model was fine-tuned through data augmentation and hyperparameter optimization to maximize detection accuracy and model generalization. The final model demonstrated strong performance, achieving a precision of 96.8%, recall of 97.3%, mAP@50 of 98.7%, and mAP@50–95 of 90.8%. The project concludes with the successful implementation of an efficient and scalable traffic sign detection framework that supports high reliability. The findings contribute to the field of computer vision and intelligent transportation by demonstrating the effectiveness of the YOLO11 model in detecting traffic signs under challenging conditions. This work serves as a foundation for further enhancements in autonomous navigation and real-world deployment of intelligent perception systems.