Traffic-Sign Detection and Classification in the Wild

• Published in: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) pp. 2110 - 2118
• Main Authors: Zhe Zhu, Dun Liang, Songhai Zhang, Xiaolei Huang, Baoli Li, Shimin Hu
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.06.2016
• Subjects: Benchmark testing; Feature extraction; Neural networks; Object detection; Proposals; Robustness; Urban areas
• ISSN: 1063-6919
• DOI: 10.1109/CVPR.2016.232

Although promising results have been achieved in the areas of traffic-sign detection and classification, few works have provided simultaneous solutions to these two tasks for realistic real world images. We make two contributions to this problem. Firstly, we have created a large traffic-sign benchmark from 100000 Tencent Street View panoramas, going beyond previous benchmarks. It provides 100000 images containing 30000 traffic-sign instances. These images cover large variations in illuminance and weather conditions. Each traffic-sign in the benchmark is annotated with a class label, its bounding box and pixel mask. We call this benchmark Tsinghua-Tencent 100K. Secondly, we demonstrate how a robust end-to-end convolutional neural network (CNN) can simultaneously detect and classify trafficsigns. Most previous CNN image processing solutions target objects that occupy a large proportion of an image, and such networks do not work well for target objects occupying only a small fraction of an image like the traffic-signs here. Experimental results show the robustness of our network and its superiority to alternatives. The benchmark, source code and the CNN model introduced in this paper is publicly available1.