CCTSDB 2021: A More Comprehensive Traffic Sign Detection Benchmark
CCTSDB 2021: A More Comprehensive Traffic Sign Detection Benchmark
复制标题
CCTSDB 2021：更全面的交通标志检测基准
DOI：
10.22967/hcis.2022.12.023
复制
发表时间：
2022-05-30
影响因子：
6.6
通讯作者：
Yu, Xiaofeng
中科院分区：
文献类型：
--
作者：
Zhang, Jianming;Zou, Xin;Yu, Xiaofeng
交通标志是引导汽车行驶的重要信息之一，交通标志的检测是自动驾驶和智能交通系统的重要组成部分。构建一个样本量大、属性类别丰富的交通标志数据集将促进交通标志检测研究的发展。本文提出了一个新的中文交通标志检测基准，在我们的CCTSDB 2017的基础上增加了4,000多幅真实的交通场景图像和相应的详细标注，并将许多原来容易检测的图像替换为困难的样本，以适应复杂多变的检测环境。由于困难样本数量的增加，新基准测试与旧版本相比，在一定程度上提高了检测网络的鲁棒性。同时，我们创建了新的专用测试集，并根据三个方面进行分类：类别含义，标志大小和天气条件。最后，我们提出了一个新的基准上的九个经典的交通标志检测算法的综合评价。我们提出的基准测试可以帮助确定未来的研究方向的算法，并开发一个更精确的交通标志检测算法具有更高的鲁棒性和实时性。
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.