Enhancing road signs segmentation using photometric invariants

TL;DR

This paper presents a robust road sign segmentation method using photometric invariants that effectively handles illumination changes, improving detection accuracy in natural scenes for intelligent transport systems.

Contribution

The paper introduces a novel segmentation approach based on photometric invariants in the l Theta Phi color space, demonstrating superior robustness over existing methods.

Findings

High segmentation accuracy under varying illumination conditions

Robustness demonstrated through comparative experiments

Significant improvement over traditional segmentation techniques

Abstract

Road signs detection and recognition in natural scenes is one of the most important tasksin the design of Intelligent Transport Systems (ITS). However, illumination changes remain a major problem. In this paper, an efficient ap-proach of road signs segmentation based on photometric invariants is proposed. This method is based on color in-formation using a hybrid distance, by exploiting the chro-matic distance and the red and blue ratio, on l Theta Phi color space which is invariant to highlight, shading and shadow changes. A comparative study is performed to demonstrate the robustness of this approach over the most frequently used methods for road sign segmentation. The experimental results and the detailed analysis show the high performance of the algorithm described in this paper.