Monitoring states of road surfaces provides valuable information for the planning and controlling vehicles and active vehicle control systems. Classical road monitoring methods are expensive and unsystematic because they require time for measurements. This article proposes an real time system based on weather conditional data and road surface condition data. For this purpose, we collected data with a mobile phone camera on the roads around the campus of the Karlsruhe Institute of Technology. We tested a large number of different image-based deep learning algorithms for road classification. In addition, we used road acceleration data along with road image data for training by using them as images. We compared the performances of acceleration-based and camera image-based approaches. The performances of the simple Alexnet, LeNet, VGG, and Resnet algorithms were compared as deep learning algorithms. For road condition classification, 5 classes were considered: asphalt, damaged asphalt, gravel road, damaged gravel road, pavement road and over 95% accuracy performance was achieved. It is also proposed to use the acceleration or the camera image to classify the road surface according to the weather and the time of day using fuzzy logic.

翻译：监测路面状态为车辆规划控制及主动车辆控制系统提供了宝贵信息。传统道路监测方法因需耗费测量时间而成本高昂且缺乏系统性。本文提出一种基于天气条件数据与路面状态数据的实时监测系统。为此，我们使用手机摄像头在卡尔斯鲁厄理工学院校园周边道路采集数据，测试了大量基于图像的深度学习算法进行路面分类。此外，我们将道路加速度数据与道路图像数据共同转换为图像格式进行训练，并比较了基于加速度与基于摄像头图像两种方法的性能。在深度学习算法中，我们对比了基础Alexnet、LeNet、VGG及Resnet等模型的性能。针对路面状态分类，本研究设定了5个类别：沥青路面、破损沥青路面、砂石路、破损砂石路及铺砌路面，最终实现了超过95%的准确率。同时提出采用模糊逻辑方法，结合加速度或摄像头图像数据，根据天气条件与昼夜时段对路面类型进行动态分类。