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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 snow storms. This paper presents the preliminary results of an ongoing 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 labelled images and 70.000 weather measurements. In particular, 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. Also, by integrating observations from weather variables, the models are able to better ascertain RSC under poor visibility conditions.

Author

Carrillo, J., Crowley, M., Pan, G., Fu, L.

Session title

Artificial Intelligence and Machine Learning for Smart Mobility (S)

Organizers

Education & Human Resources Development Council

Category

Smart Cities & Big Data

Year

2019

Format

Paper

File