Fog augmentation of road images for performance analysis of traffic sign detection algorithms

Abstract

This paper studies the influence of fog on traffic sign detection algorithms used in intelligent driver assistance systems. Previous studies are all based on synthetic images. In this work we use instead reallife photos of different road situations for fog augmentation to investigate the performance of five detection methods. To obtain depth information about the scene a depth map is first estimated for every source image of the dataset. Different visibility distances are then simulated with Koschmieder’s fog model and the implemented algorithms are applied on the resulting images. Among others, the analysis of the results shows that in foggy situations the performance of a HSI-based algorithm is not always better than that of a RGB-based method.