Traffic-Sign Detection and Classification in the Wild
Citations Over TimeTop 1% of 2016 papers
Abstract
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.
Related Papers
- → Syncretic-NMS: A Merging Non-Maximum Suppression Algorithm for Instance Segmentation(2020)30 cited
- → Medical Image Segmentation with Imperfect 3D Bounding Boxes(2021)1 cited
- → Bounding-box Centralization for Improving SiamFC++(2021)1 cited
- A Method to Generate the Minimum Bounding Boxes for Shape-Arbitrary Objects(2010)