Enhancing Traffic Sign Recognition Through Daubechies Discret Wavelet Transform and Convolutional Neural Networks

Abstract

Traffic sign recognition (TSR) is an important task in the field of autonomous vehicles and intelligent transportation systems. In this paper, we propose a novel approach to TSR using a combination of Discrete Daubechies Wavelet Transform (DDWT) and Convolutional Neural Networks (CNN). The approach transforms the input image into a wavelet representation using the DDWT and then uses a CNN to classify the traffic sign based on the representation. The proposed TSR method, combining DDWT and CNN, demonstrates high accuracy and efficiency, making it suitable for real-time applications.