Classification of Road Scenes Based on Heterogeneous Features and Machine Learning

Abstract

There is a rapid advancement in Artificial intelligence (AI) and Machine Learning (ML) that has extensively improved the object detection capabilities of smart vehicles today. Convolutional Neural Networks (CNNs) based on small, medium, and large networks have made significant contributions to in-vehicle navigation. Simultaneously, achieving higher level accuracies and faster response in autonomous vehicles is still a challenge and needs special care and attention and must be addressed for human safety. Hence, this article proposes a heterogeneous features-based machine learning framework to distinguish road scenes. The model incorporates object-based, image-based, and diverse conventional features from the road scene images generated from four distinct datasets. Object-based features are acquired using the YOLOv5m model and modified VGG19 networks, whereas image-based features are extracted using the modified VGG19 network. Conventional features are added to the object-based and blind features by applying a variety of descriptors that include Matched filters, Wavelets, Gray Level Occurrence Matrix (GLCM), Linear Binary Pattern (LBP), and Histogram of Gaussian (HOG). The descriptors are used to extract fine and course features to enhance the capabilities of the classifier. Experiments show that the proposed road scene classification framework performed better in classifying two scene categories, including crosswalks, parking, roads under bridges/tunnels, and highways achieving an average classification accuracy of 97.62% and the highest of 99.85% between crosswalks and Parking. A marginal improvement of approximately 1% is seen when all four categories were considered for evaluation using a multiclass SVM compared to other competing models.