Review of a Comparative Survey on the Detection and Classification of Traffic Signs

Abstract

Intelligent transportation systems have recently gotten a lot of interest because of their vast range of applications. The detection and identification of traffic signs system is an important part of Intelligent Transportation Systems. It enhances driver safety by notifying them of the current condition of traffic signs and providing useful information on safety considerations. This work presents a review of image and video-based traffic sign detection and recognition systems. Our primary goal is to discuss current issues and trends in the area of building an effective traffic signs detection and recognition system, then present a detailed comparison of numerous renowned methodologies applied by different researchers.Additionally, a real-time Traffic Sign Recognition system is presented in this paper. The major goal is to classify the traffic signs with a small-scale CNN into subclasses. The entire process is assessed using the German traffic sign recognition benchmark (GTRDB) dataset. The achieved identification rate is comparable to those mentioned in the literature with significantly less complexity, according to the experimental results. Moreover, its appropriateness for real-time processing applications is demonstrated by the average processing time.