A Comprehensive Evaluation of State-of-the-Art Deep Learning Models for Road Surface Type Classification

Abstract

In recent years, as intelligent transportation systems (ITS) such as autonomous driving and advanced driver-assistance systems have become more popular, there has been a rise in the need for different sources of traffic situation data. The classification of the road surface type, also known as the RST, is among the most essential of these situational data and can be utilized across the entirety of the ITS domain. Recently, the benefits of deep learning (DL) approaches for sensor-based RST classification have been demonstrated by automatic feature extraction without manual methods. The ability to extract important features is vital in making RST classification more accurate. This work investigates the most recent advances in DL algorithms for sensor-based RST classification and explores appropriate feature extraction models. We used different convolutional neural networks to understand the functional architecture better; we constructed an enhanced DL model called SE-ResNet, which uses residual connections and squeeze-and-excitation modules to improve the classification performance. Comparative experiments with a publicly available benchmark dataset, the passive vehicular sensors dataset, have shown that SE-ResNet outperforms other state-of-the-art models. The proposed model achieved the highest accuracy of 98.41% and the highest F1-score of 98.19% when classifying surfaces into segments of dirt, cobblestone, or asphalt roads. Moreover, the proposed model significantly outperforms DL networks (CNN, LSTM, and CNN-LSTM). The proposed RE-ResNet achieved the classification accuracies of asphalt roads at 98.98, cobblestone roads at 97.02, and dirt roads at 99.56%, respectively.