Attention-Based Neural Network For Traffic Sign Detection

Existing object detection pipelines can show superior performance for large objects with high resolution but fail to detect very small objects such as traffic signs. So, detecting traffic signs is a proverbially challenging problem. In this paper, we propose a novel end-to-end architecture that improves small object detection by combining Faster R-CNN with the attention mechanism. Specifically, we focus on channel-wise features and utilize the attention mechanism to enhance the feature responses by explicitly modeling the interdependencies between channel-wise features. Finally, the regression of bounding boxes and the classification of traffic signs are generated after selecting the discriminative features by the attention mechanism. Extensive evaluations of the largest traffic sign dataset demonstrate that the attention mechanism improves the performance of detecting objects, especially the small targets. For traffic sign detection task, our method achieves better performance compared with many state-of-the-art approaches on the largest traffic sign detection dataset, Tsinghua-Tencent 100K.