Two-Stage Road Sign Detection and Recognition

We propose a road sign detection and recognition method using two-stage classification strategy. In the detection phase, geometric characters of road traffic signs, Hough transformation, corner detection, and projection are used to detect the exact position of the road sign in the image under noisy and complicated environment. In the recognition phase, convolution, radial basis function (RBF) neural network and K-d tree are used to recognize the road signs in two stages. Experimental results show that most road signs can be correctly detected and recognized by our proposed method with the accuracy of 95.5%. Moreover, the method is robust against the major difficulties of road sign detection and recognition. The proposed approach would be helpful for the development of intelligent driver support system and provide effective driving assistance message.