Enhancing Road Safety: Convolutional Neural Networks in Traffic Sign Recognition

Traffic sign recognition (TSR) is an essential part of systems that allow autonomous driving and intelligent driver assistance technologies, directly contributing to enhanced road safety. This research presents a comprehensive approach to TSR using the German Traffic Sign Recognition Benchmark (GTSRB) dataset, a convolutional neural network (CNN) was trained. Our system integrates a robust frontend and API to streamline real-world deployment. Preprocessing strategies and a multi-step learning rate schedule were employed to optimize model performance. Extensive evaluations demonstrate the system’s effectiveness in accurately classifying traffic signs, contributing to safer driving environments. Potential advancements are discussed, including real-world condition adaptation and novel CNN architectures, to further enhance road safety through advanced TSR.