Traffic Sign Recognition and Speed Alert System Using Deep Learning

Abstract

Road accidents caused by over-speeding and missed traffic signs have become a major concern in recent years. This paper proposes a real-time Traffic Sign Detection and Speed Alert System using deep learning techniques to enhance road safety. The system utilizes a Convolutional Neural Network (CNN)-based EfficientNetB0 model for accurate traffic sign recognition. It integrates sign detection with vehicle speed monitoring to generate real-time alerts when speed limits are exceeded. The proposed model achieves a validation accuracy of 96% with reliable precision and recall values. The system is designed for real-time deployment and provides both visual and audio alerts to assist drivers. Experimental results demonstrate that the proposed approach improves driver awareness and has the potential to reduce road accidents significantly.The system is optimized for low latency, enabling efficient performance on mobile and embedded devices. It is capable of handling variations in lighting conditions and partial occlusions in real-world scenarios. Furthermore, the integration of detection and alert mechanisms makes it suitable for advanced driver assistance applications