Roadway Snow Detection Using Dual-Spectrum Camera Images and Computer Vision

Unfavorable roadway conditions, such as slippery roads, can negatively affect the safety of highway transportation. We aimed to develop a convenient tool capable of evaluating multi-lane road slippery conditions in winter seasons. In this work, field data collection using a dual-spectrum camera was first performed at a field site in the state of Utah, U.S. We analyzed optical and infrared images covering a field of view over three lanes through two snowstorms. Image processing techniques, including image registration, morphological operation, and segmentation, were implemented on both types of images collected under different illumination and temperature conditions. Moreover, the ratio of snow-covered pixels was computed to quantify the snow coverage rate of individual lanes. Finally, we verified the system performance by comparing our estimation with the ground truth via a confusion matrix. The high accuracy, precision, true positive rate, and true negative rate suggest the developed approach can support satisfactory performance for roadway snow detection. Besides, the performance of the unsupervised k-means clustering algorithm and supervised support vector machine (SVM) were evaluated on a dataset of 22 optical images and 19 infrared images. Both the k-means clustering and SVM can support a reasonable image segmentation for roadway snow coverage estimation. Thus, the developed technique offers the potential to facilitate local agencies' decision-making on snow-plowing resource planning and performance evaluation and support winter safety for connected vehicles.