This paper presents an automatic road-sign detection and recognition systems based on Convoluted Neural Networks. In automatic traffic-sign maintenance and in a visual driver-assistance system, road-sign detection and recognition are two of the most important functions. Our system is able to detect and recognize circular, triangular signs and, hence, covers all existing GTRSB traffic-sign shapes. Road signs provide drivers important information and help them to drive more safely and more easily by guiding and warning them and thus regulating their actions. The proposed recognition system is based on the generalization properties of CNNs. The system consists of three stages: 1) Preprocessing; 2)Detection of sign in video 3) traffic- sign classification using CNN;As we have used red, blue, white, or combinations of these colors, all traffic signs can be detected, and some of them can be detected by several colors. Results show a high success rate and a very low amount of false positives in the final recognition stage. From these results, we can conclude that the proposed algorithm is invariant to translation, rotation, scale, and, in many situations, event partial occlusions.
Volume 11 | 03-Special Issue
Pages: 1674-1682