Real-time small traffic sign detection with revised faster-RCNN

Abstract

Traffic sign detection is a crucial step for automatic driving and Intelligent Transportation. Promising results have been achieved in the area of traffic sign detection, but most of them are limited to ideal environment, where the traffic signs are very clear and large. Actually, traffic sign detection is always realized based on object detection methods. However, existing object detection methods failed to detect most of the traffic signs, especially in surveillance videos or driving recorder videos. In fact, traffic signs, i.e. traffic lights, or distant road signs in driving recorded video, always cover less than 5% of the whole image in the view of camera. Therefore, in this paper, we dedicate an effort to propose a real-time small traffic sign detection approach based on revised Faster-RCNN. More specifically, firstly, we use a small region proposal generator to extract the characteristics of small traffic signs. That is to say, considering that the stride of generator is too large, we remove the pool4 layer of VGG-16 and adopt dilation for ResNet. Secondly, we combine the revised architecture of Faster-RCNN with Online Hard Examples Mining (OHEM) to make the system more robust to locate the region of small traffic signs. Finally, we conduct extensive experiments and empirical evaluations on several different videos to demonstrate the satisfying performance of our approach. i.e., the experimental results show our approach improve the mean average precision by 12.1% over the original object detection algorithm.