Accurate and Efficient Traffic Sign Detection with a Guided Region Enlarging Algorithm

Abstract

Traffic signs detection is a hot and important topic in the research field of computer vision and lots of applications, e.g., driver assistance system and autonomous vehicle, etc. Thanks to the development of the convolutional neural network, object detection has achieved promising results in recent years. Nevertheless, traffic sign detection still faces two difficulties due to the characteristics of traffic signs. The first difficulty is the detection of small and blurred traffic signs. The second difficulty is that training samples are sparse and imbalanced. For tackling these two difficulties, Guided Region Enlarging Algorithm (GREA) is proposed in the paper. The GREA consists of two sub-modules, Potential region estimation network (PREN) and NMS-Cropping. The PREN obtains a potential object region of each input image first. Then, the potential object region is enlarged by the NMS-Cropping algorithm for subsequent augmenting training. The GREA makes use of features of a small and blurred traffic sign to enhance the performance of a traffic sign detector. To comprehensively evaluate the proposed approach, two traffic sign datasets include the Swedish traffic sign data set and the Tsinghua-Tencent 100k data set are utilized in the experiment. Experimental results demonstrate the efficiency of the proposed GREA, and our approach is comparable to the state-of-the-art approaches.