Abstract
This research paper delves into the realm of road sign detection and classification using deep Convolutional Neural Networks (CNNs). Leveraging the robust capabilities of CNNs, the study comprehensively investigates the intricate landscape of computer vision techniques applied to the task of road sign recognition. The focal point of this research centers on the design and evaluation of a CNN-based architecture trained on the German Traffic Sign Recognition Benchmark (GTSRB) dataset, comprising over 50,000 annotated road sign images across 43 distinct classes. Through a rigorous examination of theoretical foundations and practical implementation, the study elucidates the pivotal role of CNNs in enhancing the accuracy and efficiency of road sign detection systems. The research evaluates the model's performance using precision, recall, and F1-score metrics, consistently demonstrating the model's adeptness in minimizing false positives while effectively capturing pertinent road sign instances. Furthermore, this study underscores the significance of meticulous dataset curation, model optimization, and training procedure refinement in augmenting the efficacy of road sign detection systems. The outcomes of this research have far-reaching implications for intelligent transportation systems, autonomous vehicles, and road safety, offering a promising trajectory towards the advancement of responsive and reliable road sign recognition technologies.