Design of Efficient Deep Learning models for Determining Road Surface Condition

Design of Efficient Deep Learning models for Determining Road Surface Condition from Roadside Camera Images and Weather Data

Road maintenance during the Winter season is a safety critical and resource demanding operation. One of its key activities is determining road surface condition (RSC) in order to prioritize roads and allocate cleaning efforts such as plowing or salting.Two conventional approaches for determining RSC are: visual examination of roadside camera images by trained personnel and patrolling the roads to perform on-site inspections. However, with more than 500 cameras collecting images across Ontario, visual examination becomes a resource-intensive activity, difficult to scale especially during periods of snowstorms. This paper presents the results of a study focused on improving the efficiency of road maintenance operations. We use multiple Deep Learning models to automatically determine RSC from roadside camera images and weather variables, extending previous research where similar methods have been used to deal with the problem. The dataset we use was collected during the 2017-2018 Winter season from 40 stations connected to the Ontario Road Weather Information System (RWIS), it includes 14.000 labeled images and 70.000 weather measurements. We train and evaluate the performance of seven state-of-the-art models from the Computer Vision literature, including the recent DenseNet, NASNet, and MobileNet. Moreover, by following systematic ablation experiments we adapt previously published Deep Learning models and reduce their number of parameters to about ~1.3% compared to their original parameter count, and by integrating observations from weather variables the models are able to better ascertain RSC under poor visibility conditions.

从路边摄像头图像和天气数据确定路面状况的高效深度学习模型的设计

冬季的道路维护是一项至关重要的安全工作，需要大量资源。它的主要活动之一是确定路面状况（RSC），以便对道路进行优先排序并分配耕作工作，例如耕作或撒盐。.. 确定RSC的两种常规方法是：由经过培训的人员对路边的摄像机图像进行目视检查，并巡逻道路以进行现场检查。但是，随着超过500台摄像机在安大略省收集图像，视觉检查成为一项资源密集型活动，很难扩展，尤其是在暴风雪期间。本文介绍了专注于提高道路养护运营效率的研究结果。我们使用多个深度学习模型从路边的摄像机图像和天气变量中自动确定RSC，从而扩展了以前的研究，其中已经使用类似的方法来解决该问题。我们使用的数据集是在2017-2018年冬季期间从40个连接到安大略路天气信息系统（RWIS）的站点收集的，其中包括14个。000张带标签的图像和70.000个天气测量值。我们根据计算机视觉文献（包括最近的DenseNet，NASNet和MobileNet）训练和评估了七个最新模型的性能。此外，通过遵循系统的消融实验，我们采用了以前发布的深度学习模型，并将其参数数量与原始参数数量相比减少了约1.3％，并且通过整合来自天气变量的观测值，该模型能够更好地确定可见性差的RSC条件。 （阅读更多）