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Road maintenance during the Winter season is a safety critical and resourcedemanding operation. One of its key activities is determining road surfacecondition (RSC) in order to prioritize roads and allocate cleaning efforts suchas plowing or salting. Two conventional approaches for determining RSC are:visual examination of roadside camera images by trained personnel andpatrolling the roads to perform on-site inspections. However, with more than500 cameras collecting images across Ontario, visual examination becomes aresource-intensive activity, difficult to scale especially during periods ofsnowstorms. This paper presents the results of a study focused on improving theefficiency of road maintenance operations. We use multiple Deep Learning modelsto automatically determine RSC from roadside camera images and weathervariables, extending previous research where similar methods have been used todeal with the problem. The dataset we use was collected during the 2017-2018Winter season from 40 stations connected to the Ontario Road WeatherInformation System (RWIS), it includes 14.000 labeled images and 70.000 weathermeasurements. We train and evaluate the performance of seven state-of-the-artmodels from the Computer Vision literature, including the recent DenseNet,NASNet, and MobileNet. Moreover, by following systematic ablation experimentswe adapt previously published Deep Learning models and reduce their number ofparameters to about ~1.3% compared to their original parameter count, and byintegrating observations from weather variables the models are able to betterascertain RSC under poor visibility conditions.

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