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.

在异类道路环境中用于智能车辆的基于视觉的技术正受到研究人员和工业家的极大关注。不幸的是，由于场景复杂性，变化的道路结构和不适当的照明条件，该领域的机制受性能限制。这些具有挑战性的情况可能会使智能车辆进入危险情况，例如碰撞或交通事故，并可能导致更高的死亡率。在现有文献中很少探讨智能方法和其他机器学习技术在路面分类中的应用。因此，我们提出了基于卷积神经网络的道路分类网络（RCNet），以对路面进行精确分类。此过程包括对路面的五种主要类别的分类：弯曲，干燥，冰冻，崎，和潮湿的道路。实验结果揭示了所提出的RCNet在各种优化程序技术下的行为。标准性能评估方法已用于在牛津RobotCar数据集上测试和验证所提出的方法。RCNet可以实现99.90％的分类准确度，精确度和灵敏度，以及99.97％的特异性。实施工作的结果大大高于可用的最新技术，并且在复杂的道路环境中显示出准确有效的性能。RCNet可以实现99.90％的分类准确度，精确度和灵敏度，以及99.97％的特异性。实施工作的结果大大高于可用的最新技术，并且在复杂的道路环境中显示出准确有效的性能。RCNet可以实现99.90％的分类准确度，精确度和灵敏度，以及99.97％的特异性。实施工作的结果大大高于可用的最新技术，并且在复杂的道路环境中显示出准确有效的性能。