Real-time Road Signs Detection and Recognition for Enhanced Road Safety

• Main Authors: Teklesenbet, Hermon B., Demoz, Nahom H., Jabiro, Igor H., Tesfay, Yonatan R., Badidi, Elarbi
• Format: Conference Proceeding
• Language: English
• Subjects: Computational modeling; Computer vision; Deep Learning; Driver Assistance System; Mobile Application; Mobile applications; Real-time systems; Roads; Traffic Sign Recognition; Visualization; YOLO
• ISSN: 2473-2052
• DOI: 10.1109/IIT59782.2023.10366480

Road sign detection and recognition play a critical role in improving driver safety and awareness in the modern traffic era. This paper describes the development and evaluation of a Road Sign Detection and Recognition System (RSDRS). Our system leverages computer vision techniques and mobile application technology to provide drivers with real-time visual and auditory feedback based on detected traffic signs. The implementation of RSDRS involves two basic phases: developing a robust recognition model and creating an Android application. The model, trained with the German Traffic Sign Recognition Benchmark (GTSRB) dataset and the YOLOv5s object detection algorithm, serves as the core component for accurate traffic sign recognition. The Android application captures live video frames, integrates seamlessly with the recognition model, and provides intuitive feedback to the driver. The performance evaluation reveals the exceptional capabilities of our system, with an average accuracy of 99.3% and a fast response time of 0.73 milliseconds. Metrics such as recall, precision, and F1 score highlight the model's ability to maintain accuracy while minimizing false positives. Real-world applicability is paramount, and our system excels in a variety of environments and lighting conditions, as evidenced by rigorous testing.