A CASE STUDY ON AUTOMATIC TRAFFIC SIGN RECOGNITION USING DEEP LEARNING

Abstract

In our work, we have reviewed algorithms such as SVM, CNN, Adaboost, etc. based on Deep learning. In Traffic sign recognition, the aim is to remind or warn drivers about the restrictions, dangers or other information imparted by traffic signs, beforehand. Since the existing signs are designed to draw drivers’ attention through their colors and shapes, processing these features is one of the crucial parts of these systems. In this study a Traffic Sign Recognition System, having the ability to the classification of traffic signs even with bad visual artifacts that originate from some weather conditions or other circumstances, is developed. Through our experiment using the CNN algorithm traffic sign recognition worked with 97% accuracy by using the German Traffic Sign Recognition Benchmark(GTSRB) dataset.