A novel saliency computation model for traffic sign detection
Zhang Fan, Ruirui Ji, Jiao Shangbin, Qi Kaijie · 2017
In this paper, a new method of saliency-based traffic sign detection is presented. On the basis of the visual attention mechanism model, edge and color information are extracted as early visual features, and each feature is computed and normalized to obtain feature maps, conspicuity maps and the saliency map. Then the candidate regions containing traffic signs are determined with self-organizing map neural network and k-means algorithm. The experimental results show that the method could highlight salient targets and suppress the background effectively, and detect the traffic signs in natural scenes accurately.