Traffic Sign Detection and Recognition using Neural Networks and Histogram based Selection of Segmentation Method

Speed limit traffic sign detection is realized in two basic parts. The first part is the detection of traffic sign edge which includes the segmentation by means of a large set of different thresholds in different colour spaces. For each input image a convenient threshold and colour component from one of colour spaces is selected by the observation of the histogram of the whole image. Specially trained artificial neural network decides about the selection. In this way a system that is adaptable to different lighting conditions is obtained so that the segmentation is successfully carried out even in night lighting conditions where red colours prevail. After the segmentation in the first part the detection by means of the circular Hough transform is carried out in larger radius range and that is why the system is adaptable to the different sizes of the traffic sign. The second part is the recognition where the most important thing is the selection of real patterns and features. The first part, i.e. the detection module, results in one or two best graded circles in the segmented image of the reduced resolution. Now, the suitable frame is within the original image and it is reduced to a certain dimension and is transferred into the grey image. The system automatically prepares the inputs for NN in accordance with so obtained interior of the traffic sign. The method is experimentally verified using an image database of traffic signs. The initial results are encouraging.