Automated Traffic Sign Recognition via CNN Deep Learning

The rapid development of the road traffic systems is a very important component of the nation’s infrastructure, and that is reflected in growing importance of the traffic safety. Traffic Sign Recognition (TSR) is an important field of research because traffic offenses, particularly the disregard for traffic signs, are a major contributor to accidents. This paper gives a comprehensive review of the latest developments in the area of traffic sign detection and recognition techniques with attention to the applications of CNNs. This paper discusses the need for traffic signs to be detected under complex conditions and proposes an architecture based on modified CNN that helps reduce the processing time and increase accuracy. Improving recognition accuracy in realistic environments is the motivation behind the CNN model, which has been architected for real-time training as well as target identification. Experiments suggest that this solution outperforms present intelligent driving systems and the state-of-the-art performance achieved by the image processing algorithms currently in use and traffic sign datasets.