A Traffic Sign Detection System Linking Hypothesis Tests and Deep Learning Networks

Abstract

In this paper, we propose a computationally efficient method for traffic sign detection. Our methodology uses deep learning networks introduced for semantic image segmentation and classification of region of interests generated by image segmentation. The region of interests are detected by segmenting the road scene image dataset and hypothesis tests are applied to accept or reject the probability of being a traffic sign in the detected region of interest. For hypothesis tests, color feature, and the location of the traffic sign with respect to the segmented road information is used. The main contribution of our method is the introduction of hypothesis tests that mutually couple two deep learning networks trained by well-known datasets publicly available for benchmarking purposes. To test and evaluate our traffic sign detection system, we use the German traffic sign detection benchmark dataset with a large set of traffic signs and during its training, The cityscapes dataset offering labeled urban scenes is also leveraged by the traffic sign detection system. Our experimental results illustrate the effectiveness performance metrics are reached at 90.81%, 94.76% and 92.74% in precision, recall and F-measure, respectively. The runtime cost is around 0.4[Formula: see text]s for an image on an ordinary laptop computer.