CEIT-Basque Research and Technology Alliance (BRTA), Manuel Lardizabal 15, 20018 Donostia/San Sebastián, Spain.
Universidad de Navarra, Tecnun, Manuel Lardizabal 13, 20018 Donostia/San Sebastián, Spain.
Sensors (Basel). 2021 Feb 10;21(4):1254. doi: 10.3390/s21041254.
European road safety has improved greatly in recent decades. However, the current numbers are still far away to reach the European Commission's road safety targets. In this context, Cooperative Intelligent Transport Systems (C-ITS) are expected to significantly improve road safety, traffic efficiency and comfort of driving, by helping the driver to make better decisions and adapt to the traffic situation. This paper puts forward two vision-based applications for traffic sign recognition (TSR) and real-time weather alerts, such as for fog-banks. These modules will support operators in road infrastructure maintenance tasks as well as drivers, giving them valuable information via C-ITS messages. Different state-of-the-art methods are analysed using both publicly available datasets (GTSB) as well as our own image databases (Ceit-TSR and Ceit-Foggy). The selected models for TSR implementation are based on Aggregated Chanel Features (ACF) and Convolutional Neural Networks (CNN) that reach more than 90% accuracy in real time. Regarding fog detection, an image feature extraction method on different colour spaces is proposed to differentiate sunny, cloudy and foggy scenes, as well as its visibility level. Both applications are already running in an onboard probe vehicle system.
欧洲的道路安全在近几十年得到了极大的改善。然而，目前的数字距离欧盟委员会的道路安全目标还有很大的差距。在这种情况下，协同智能交通系统（C-ITS）有望通过帮助驾驶员做出更好的决策并适应交通状况，显著提高道路安全、交通效率和驾驶舒适度。本文提出了两个基于视觉的应用程序，用于交通标志识别（TSR）和实时天气警报，例如雾区。这些模块将支持道路基础设施维护人员和驾驶员，通过 C-ITS 消息为他们提供有价值的信息。使用公开数据集（GTSB）和我们自己的图像数据库（Ceit-TSR 和 Ceit-Foggy）分析了不同的最先进方法。用于 TSR 实现的选定模型基于聚合通道特征（ACF）和卷积神经网络（CNN），可在实时达到 90%以上的准确率。关于雾检测，提出了一种在不同颜色空间上的图像特征提取方法，用于区分晴天、多云和雾天以及其可见度水平。这两个应用程序已经在车载探头系统中运行。
