Enhancing road signs detection rate using Multi-Scale Retinex

In this paper, a detailed conception of an embedded system of road sign recognition algorithms based on color segmentation, shape analysis and template matching has been made. These techniques are poorly adapted to the Arab context, since some signs are written with Arabic letters. The illumination changes are the greatest obstacle in our work. Therefore, in the first module of the system there is a pre-processing of the image which uses Multi-Scale Retinex and a color model based on normalized RGB color space due to its superior performance in illumination changes such as cloud, fog and dark. The regions of interest are detected using color segmentation. This preprocessing is followed by a Hough transform in order to detect the existing forms. In the second module, the output of the last processing is compared, using the normalized cross correlation function, with our reference database. Experimental results show that the proposed algorithm increases the detection rate of traffic signs.