Traffic Sign Detection and Recognition Using Deep Learning

Abstract

Automatic traffic sign detection and recognition is a crucial part of traffic management. It offers a precise and efficient method for managing traffic sign with the least amount of human effort. Incorrect direction is causing an increasing number of accidents in today's world. These accidents can be decreased with automated traffic sign detection. A vast majority of existing approaches perform well on traffic signs needed for advanced driverassistance and autonomous systems but they don't work with Bangladesh and they don't work with different environments . In our country there are hundreds of traffic signs. I work on 15 signs that are very essential for advanced driving systems and the vehicle driver. For building our model to detect object traffic signs I use YOLOv8 (You Only Look Once), VGG16 (Visual Geometry Group), Inception v3, With YOLO's assistance, machines can quickly recognize objects and forecast the traffic sign automatically. In this research best performing model is YOLOv8 and the accuracy is 96%. The main objective of this report is to show drivers how to drive safely by using the machine's instructions to anticipate all traffic regulations and directives.