Traffic Sign Recognition

The German Traffic Sign Recognition Benchmark (GTSRB) contains 43 classes of traffic signs, split into 39,209 training images and 12,630 test images. The images have varying light conditions and rich backgrounds. Source: Invisible Backdoor Attacks Against Deep Neural Networks Image Source: https://www.researchgate.net/figure/An-example-of-the-43-traffic-sign-classes-of-GTSRB-dataset_fig9_311896388

Object detection benchmark for logo detection. Images are natural scenes. Each image contains multiple objects, and each image has a total of 1 logo. Logo detection & classification labels are provided.

Includes more than two million traffic sign images that are based on real-world and simulator data. Source: CURE-TSR: Challenging Unreal and Real Environments for Traffic Sign Recognition

Collected from top 10 most popular clothing/wearable brandname logos captured in rich visual context. Source: Deep Learning Logo Detection with Data Expansion by Synthesising Context

Based on simulated challenging conditions that correspond to adversaries that can occur in real-world environments and systems. Source: Traffic Sign Detection under Challenging Conditions: A Deeper Look Into Performance Variations and Spectral Characteristics

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. We call this benchmark Tsinghua-Tencent 100K. 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. Secondly, we demonstrate how a robust end-to-end convolutional neural network (CNN) can simultaneously detect and classify traffic-signs. 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 available.

BelgaLogos is a dataset for logo detection and recognition. The images of BelgaLogos dataset have been provided and are copyrighted by BELGA press agency. They are freely available for research purpose only. The dataset is composed of 10,000 images covering all aspects of life and current affairs: politics and economics, finance and social affairs, sports, culture and personalities. All images are in JPEG format and have been re-sized with a maximum value of height and width equal to 800 pixels, preserving aspect ratio. Paper: Alexis Joly and Olivier Buisson, Logo retrieval with a contrario visual query expansion, In Proceedings of the Seventeen ACM international Conference on Multimedia, 2009. Source: BelgaLogos dataset Image Source: BelgaLogos dataset