CNN Based Approach for Traffic Sign Recognition System

Abstract

Machine Learning (ML) involves making a machine able to learn and take decisions on real-life problems by working with an efficient set of algorithms. The generated ML models find application in different areas of research and management. One such field, automotive technology, employs ML enabled commercialized advanced driver assistance systems (ADAS) which include traffic sign recognition as a part. With the increasing demand for the intelligence of vehicles, and the advent of self-driving cars, it is extremely necessary to detect and recognize traffic signs automatically through computer technology. For this, neural networks can be applied for analyzing images of traffic signs for cognitive decision making by autonomous vehicles. Neural networks are the computing systems which act as a means of performing ML. In this work, a convolutional neural network (CNN) based ML model is built for recognition of traffic signs accurately for decision making, when installed in driverless vehicles.

A lightweight network architecture for traffic sign recognition based on enhanced LeNet-5 network

“…Data enhancement is an important step to solve the problem of unbalance of data sets. Various kinds of transformation processing are carried out on existing data to generate new data to expand the amount of data ( Patil et al, 2021 ; Singh and Malik, 2022 ). Translation, scaling and rotation are commonly used transformation means.…”