Traffic Sign Detection

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

A video dataset for recognising traffic signs hosted with the first IEEE Video and Image Processing (VIP) Cup within the IEEE Signal Processing Society. Source: Challenging Environments for Traffic Sign Detection: Reliability Assessment under Inclement Conditions

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

Trainging and testing data: The original training set includes 6105 images, and the original testing set includes 3071 images. Description: 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.

Traffic signs are one of the most important information that guide cars to travel, and the detection of traffic signs is an important component of autonomous driving and intelligent transportation systems. Constructing a traffic sign dataset with many samples and sufficient attribute categories will promote the development of traffic sign detection research. In this paper, we propose a new Chinese traffic sign detection benchmark, which adds more than 4,000 real traffic scene images and corresponding detailed annotations based on our CCTSDB 2017, and replaces many original easily-detected images with difficult samples to adapt to the complex and changing detection environment. Due to the increase of the number of difficult samples, the new benchmark can improve the robustness of the detection network to some extent compared to the old version. At the same time, we create new dedicated test sets and categorize them according to three aspects: category meanings, sign sizes, and weather conditions. Finally, we present a comprehensive evaluation of nine classic traffic sign detection algorithms on the new benchmark. Our proposed benchmark can help determine the future research direction of the algorithm and develop a more precise traffic sign detection algorithm with higher robustness and real-time performance. Source: CCTSDB 2021: A more comprehensive traffic sign detection benchmark

A large-scale traffic sign and traffic light dataset with accurate 3D positioning and temporally consistent 3D bounding boxes of traffic management objects from up to 200 meters away. The dataset contains additional attributes such as traffic light state, traffic light mask type, traffic sign type, and occlusion. The application areas are 3D traffic lights and sign detection for autonomous driving.

The CSUST Chinese Traffic Sign Detection Benchmark (CCTSDB) is an existing dataset for traffic sign detection. It consists of nearly 20,000 images of Chinese road traffic scenes, including around 40,000 annotated images of traffic signs. While most scenes in the dataset are captured under natural weather conditions, challenges include foggy, rainy, and blurry perspectives. To facilitate our research, we created a dataset called CCTSDB-AUG based on CCTSDB. This augmented dataset includes images with foggy, rainy, and blurry perspectives. We applied random haze, raindrop, and motion blur effects to generate these augmented images, simulating real-world extreme conditions. Image augmentation was performed using the Albumentations library in Python, allowing us to construct images with various weather effects. The CCTSDB-AUG dataset contains images with different extreme conditions,. These extreme conditions are proportionally represented throughout the dataset.