The slipperiness of the surface is essential for road safety. The growing number of CCTV cameras opens the possibility of using them to automatically detect the slippery surface and inform road users about it. This paper presents a system of developed intelligent road signs, including a detector based on convolutional neural networks (CNNs) and the transfer-learning method employed to the processing of images acquired with video cameras. Based on photos taken in different light conditions by CCTV cameras located at the roadsides in Poland, four network topologies have been trained and tested: Resnet50 v2, Resnet152 v2, Vgg19, and Densenet201. The last-mentioned network has proved to give the best result with 98.34% accuracy of classification dry, wet, and snowy roads.

中文翻译：

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基于闭路电视摄像机和卷积神经网络的道路湿滑监测系统

路面的光滑度对道路安全至关重要。越来越多的闭路电视摄像机开启了使用它们自动检测光滑表面并通知道路使用者的可能性。本文介绍了一种开发的智能路标系统，包括基于卷积神经网络 (CNN) 的检测器和用于处理摄像机获取的图像的迁移学习方法。根据波兰路边闭路电视摄像机在不同光照条件下拍摄的照片，训练和测试了四种网络拓扑：Resnet50 v2、Resnet152 v2、Vgg19和Densenet201。最后提到的网络已被证明以 98.34% 的干燥、潮湿和雪路分类准确率给出了最好的结果。