Machine and Deep Learning Techniques for Daytime Fog Detection in Real Time with In-Vehicle Vision Systems Using the SHRP 2 Naturalistic Driving Study Data

Abstract

The main focus of this study is to develop a system that can accurately detect the presence of fog in real time at a trajectory level. This study leveraged video data from the SHRP 2 Naturalistic Driving Study (NDS). Extensive data reduction steps were taken to classify various levels of foggy weather conditions from the video data to form two unique image data sets. Afterward, features based on the gray level co-occurrence matrix (GLCM) were extracted from the images and used as classification parameters for training support vector machine (SVM) and K-nearest neighbor (K-NN) algorithms. In addition, a convolutional neural network (CNN) was also examined to improve the detection performance. Although the analysis was done initially on a data set consisting of two weather conditions, clear and fog, it has been extended to include different levels of fog, that is, near fog and distant fog. While the accuracy of the first analysis with two categories was approximately 92% and 91% for SVM and K-NN classifiers, respectively, the CNN produced much greater accuracy of 99%. As expected, the accuracy of the second analysis, with more refined weather categories, was relatively lower than the first analysis where CNN, SVM, and K-NN models produced an accuracy of about 98%, 89%, and 88%, respectively. With the rapid advances in connectivity and affordable cameras, the proposed detection models could be integrated into the smartphones of regular road users, creating an effective way to collect real-time road weather information that could be used to improve weather-based variable speed limit (VSL) systems.