Abstract
In recent years, a plethora of systems have emerged for recognizing traffic signs. This paper offers a comprehensive overview of the latest and most effective approaches in detecting and categorizing traffic signs. The primary goal of detection techniques is to pinpoint the precise areas containing traffic signs, which are classified into three main categories: color-based, shape-based, and learning-based methods of Alex net, Desnse net, and Mobil net (ADM) models. Moreover, methods of classification are divided into two groups; those relying on manually crafted features such as HOG, LBP, SIFT, SURF, BRISK, and those leveraging deep learning. The paper summarizes various detection and classification methods, along with the datasets utilized, for quick reference. Additionally, it provides suggestions for future research directions and recommendations to enhance traffic sign recognition performance..
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