RCNet: road classification convolutional neural networks for intelligent vehicle system

Vision-based techniques for intelligent vehicles in heterogeneous road environments are gaining significant attention from researchers and industrialists. Unfortunately, the mechanisms in this domain suffer from limited performance due to scene complexity, varying road structure, and improper illumination conditions. These challenging situations may lead an intelligent vehicle into dangerous situations such as collisions or road accidents and may cause higher mortality. The application of intelligent methods and other machine learning techniques for road surface classification is little explored in the existing literature. Thus, we propose a convolutional neural network-based road classification network (RCNet) for the accurate classification of road surfaces. This procedure includes the classification of five major categories of road surfaces: curvy, dry, ice, rough, and wet roads. The experimental results reveal the behavior of the proposed RCNet under various optimizer techniques. The standard performance evaluation measures have been used to test and validate the proposed method on the Oxford RobotCar dataset. RCNet achieves classification accuracy, precision, and sensitivity of 99.90%, and 99.97% of specificity. Results of implemented work are significantly higher than available state-of-the-art techniques and show accurate and effective performance in the complex road environment.