Detection of Traffic Signs using feature based on of Speed Up Robust method

Abstract

Variations in perspective, illumination, occlusion, motion blur, and weatherworn degeneration of signs could all be crucial in identifying road signs. The goal of this project is to evaluate the image processing technique's performance in detecting and recognizing road signs, as well as determine the optimum threshold value range for doing so. The Speed Up Robust Features (SURF) detector was tested in the current project to detect and recognize road signs through Bagdad’s streets under various speeds and threshold values. The importance of the threshold’s value was highlighted here to occupy an accurate detection and hence recognize road sign at final. The optimum threshold value for best detection resulted usually in the range (20-25) for all speed signs. The latter recorded its highest precision value at five threshold value while the highest precision value (i.e. 0.5) resulted for speed sign 40 followed by 60 and 80-speed signs