MPC Research Reports
Report Details

Title:	Winter Safety Improvement with Computer Vision and Transfer Learning
Authors:	Moein Ramezanpourkami and Xuan Zhu
University:	University of Utah
Publication Date:	Oct 2024
Report #:	MPC-24-572
Project #:	MPC-698

Abstract

The preservation of road safety in snowy regions during the winter season is of paramount significance due to the presence of perilous meteorological circumstances, such as snowstorms, which can result in escalated vehicular collisions and subsequent roadway closure. In the present investigation, our primary objective is to devise a novel methodology aimed at tackling the aforementioned obstacle. This is achieved through the utilization of a hybridized system that incorporates both thermal and optical imagery to identify snow accumulation on road surfaces. By employing transfer learning techniques in conjunction with the U-Net architecture implemented in the Keras framework, our approach demonstrates notable efficacy in attaining precise outcomes, even when confronted with the limitations imposed by a restricted dataset. Our results demonstrate notable mean pixel accuracy (MPA) scores of 88% for roadway snow detection based on optical images captured during daytime and 94% based on thermal images acquired during nighttime. The encouraging results observed in this study underscore the potential of our dual-spectrum imaging technique to greatly improve road safety and reduce the number of collisions in winter conditions.

How to Cite

Ramezanpourkami, Moein, and Xuan Zhu. Winter Safety Improvement with Computer Vision and Transfer Learning, MPC-24-572. North Dakota State University - Upper Great Plains Transportation Institute, Fargo: Mountain-Plains Consortium, 2024.