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 c
2 PAPERS • 1 BENCHMARK
Based on simulated challenging conditions that correspond to adversaries that can occur in real-world environments and systems.
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Includes more than two million traffic sign images that are based on real-world and simulator data.
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A video dataset for recognising traffic signs hosted with the first IEEE Video and Image Processing (VIP) Cup within the IEEE Signal Processing Society.
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Trainging and testing data: The original training set includes 6105 images, and the original testing set includes 3071 images.