ROAD SIGN RECOGNITION USING A HYBRID EVOLUTIONARY ALGORITHM AND PRIMITIVE FUSION

We present an algorithm to detect and recognize road signs from embedded terrestrial images. We argue that the most important process in such algorithms is the fine detection which isolates object shape in images. After this step, recognition can be processed by simple normalized template matching. We then use a hybrid evolutionary algorithm capable of performing the fusion between colour and edges detection. This hybrid approach associates a stochastic process and a local deterministic error minimization to increase the precision and improve the repeatability of the convergence by eliminating certain unpredictable processes such as mutation. Primitive fusion brings precision and decreases the necessary number of iterations (about 5 times faster) required to optimize the influence of every primitive during the algorithm execution. We present this algorithm and show that we can use a final template matching in a simple way.