Classification of road conditions from images with deep learning frameworks********

点击翻译按钮获取中文摘要

This research project with Weatherlogics seeks to automate the process of observing highway conditions by using machine learning to automatically report road conditions using images taken by highway cameras. Specifically, Deep Convolutional Neural Networks (DCNNs) will be employed to classify images taken by highway cameras through the use of transfer learning. Modern DCNN architectures have attained remarkable success in image classification tasks. The deep learning algorithm will be able to take any static image of a highway and determine the road condition that is present (e.g. dry, wet, snow-covered, slush-covered, or ice-covered).****This project would have tremendous positive benefits for Canada. By more accurately observing road conditions, especially in winter, this technology would have both economic and social benefits. Due to the high skill involved in producing and running a machine learning model, the commercialization of this technology would provide high-quality models. Furthermore, the data from this model would be used to improve driver safety and reduce transportation impacts due to weather. Given the movement toward automation in vehicles, and intelligent transportation systems, the ability to receive real-time road condition information across the entire highway network would put Canada on the leading edge of these advances in transportation. Road weather forecasts and observations are a unique service that could be used to position Weatherlogics as a leading data provider to consumers, governments, and transportation/logistics companies that are impacted by adverse road weather****The significance of project is that this technology would give Weatherlogics a unique competitive advantage over traditional weather prediction companies.**