Traffic Sign Detection and Recognition System using CNN

Abstract: Traffic signs play a crucial role in providing drivers with critical information. Therefore, motorists must possess the ability to read and understand traffic signals in order to drive safely. There has been a lot of research on traffic sign detection during the past many decades. There is a severe lack of real-time and precise detections in the present state of the art of traffic sign detection, which is preventing it from being really useful. This study details an approach to traffic sign recognition that operates in real-time and provides drivers with voice assistance. This system is comprised of two subsystems. Locating and recognising traffic lights is the first step in using a trained Convolutional Neural Network (CNN). A text-to-speech engine will then narrate the traffic sign to the driver after it has been detected. Using Deep Learning techniques, we construct a very efficient CNN model using a benchmark dataset for real-time detection and identification. One benefit of this technology is its ability to recognise and elucidate traffic signs for drivers, regardless of their level of attentiveness or comprehension. This sort of technology is equally important for developing autonomous automobiles