Intelligent Traffic Sign Detection and Voice Alerts for Safer Roads

Abstract

Enhancing road safety remains a critical concern, encompassing the well-being of drivers, pedestrians, and cyclists alike. A promising approach to elevate road safety involves the implementation of intelligent systems capable of real-time detection and identification of traffic signs, accompanied by voice-based alerts to drivers. In this research, we introduce a system designed for the recognition of traffic signboards. This system leverages Convolutional Neural Networks (CNNs) to process and classify images, resulting in precise identification of traffic signs, such as speed limits, stop signs, and warning indicators, among others. Furthermore, our proposed system integrates a voice-based alerting mechanism, ensuring drivers receive timely notifications aligned with the recognized traffic signboards. The voice-based alerting system takes on the responsibility of notifying drivers about impending alterations in speed limits, approaching stop signs, pedestrian crossings, and other pivotal traffic indicators. Rigorous experimentation demonstrates the system's impressive capability to swiftly and accurately detect and categorize traffic signs in real-time. Importantly, the voice-based alerts serve as a proactive measure to diminish accidents and elevate road safety levels. This innovative solution offers an effective, efficient approach to real-time traffic sign recognition and voice-based alerts, promising substantial contributions to road safety and accident reduction.