Abstract:
Traffic Sign Recognition is the process of automatically detecting and classifying traffic
signs along the road, including speed limit signs, yield signs, merge signs etc. It can
subsequently be used in ADAS (advanced driver-assistance systems), or for help in the
navigation of self-driving cars. It is a technology that allows users to recognize traffic signs
in real-time, typically in videos, or sometimes just in photos. It is a realistic task that is full
of constraints, such as visual environment, physical damages, and partial occasions, etc.
In order to deal with the constrains, convolutional neural networks (CNN) are
accommodated to extract visual features of traffic signs and classify them into
corresponding classes.
Our final-year project automatically and in real time uses Deep Convolutional Neural
Networks (CNNs) to achieve the task of automated traffic sign detection and classification.
We initially used a benchmark (GTSRB) for the traffic-sign recognition. In order to
determine which deep learning models are the most suitable one for the TSR (Traffic Sign
Recognition), we chose three kinds of models to conduct deep learning computations:
LENET, SSD MobileNet and YOLOv4. According to the scores of various metrics, we
summarized the pros and cons of the picked models for the TSR task. We have used the
Python programming language, OpenCV library and Linux OS. The NVIDIA Jetson Nano
single-board computer is used for hardware deployment.
