Detection and classification of traffic signs in natural environments

A novel approach is proposed for the detection of traffic signs in natural environments. Each RGB image is converted into HSV color space, and segmented by the hue and saturation thresholds. A symmetrical detector of local binary features and a set of fuzzy rules are used to determine the shape of region of interests (ROI) in the detection algorithm. For the traffic signs classification, a classification module based on decision trees is designed, and PNN is adopted for the further classification which incorporates the J-means algorithm and Particle Swarm Optimization to optimize the networks. Experiments were conducted for the detection and classification of traffic signs, involved in 3000 images under sunny, cloudy and rainy weather conditions. Results demonstrate that the proposed detection algorithm is capable of achieving the hit-rate of 93.28%, 90.25% and 88.97% respectively, and the classification module has simple structure as well as high accuracy.