Traffic sign detection method based on faster R-CNN

Abstract

Traffic sign detection is the pivotal technology of the traffic sign recognition system. In this article, a traffic sign detection method comes up based on Faster R-CNN deep learning framework. In this method, a convolution neural network is devoted to extract traffic sign image features automatically, and the extracted convolution feature map is sent into a Region Proposal Network (RPN) for foreground objects filtration and regression of bounding boxes. Then the proposed regions are mapped to the feature map, and the fixed-size proposal boxes via Region of Interest pooling layer (RoI). After that, we use the classification network to perform specific classification tasks and further compute the bounding box regression. The experiments performs on the German Traffic Sign Detection Benchmark (GTSDB) and the experimental results show that the method has effectiveness and robustness to different light, block, and motion.