Review. Machine learning techniques for traffic sign detection

TL;DR

This review paper discusses various machine learning and neural network methods for automatic road sign detection, comparing their features and addressing key challenges in the field.

Contribution

It categorizes existing methods into two groups, reviews their approaches, and provides a comparative analysis of their features for improved road sign detection.

Findings

Neural networks are a prominent approach in road sign detection.

Feature extraction plays a crucial role in detection accuracy.

Comparison highlights strengths and limitations of different methods.

Abstract

An automatic road sign detection system localizes road signs from within images captured by an on-board camera of a vehicle, and support the driver to properly ride the vehicle. Most existing algorithms include a preprocessing step, feature extraction and detection step. This paper arranges the methods applied to road sign detection into two groups: general machine learning, neural networks. In this review, the issues related to automatic road sign detection are addressed, the popular existing methods developed to tackle the road sign detection problem are reviewed, and a comparison of the features of these methods is composed.