Automatic Classification of Pavement Type and Service Age Benchmarked with Standard Texture Databases Using the Machine Learning Method: A Pilot Study

Abstract

Pavement intelligent management systems have attracted considerable interest from researchers. However, various service conditions of pavement surface concerning the pavement type, texture service age, and so forth, inhibit a universal algorithm that is feasible for all cases. In this regard, the automatic classification of pavement type and service age is an essential premise to unblock the bottleneck stated above. Based on the surface texture data, a pilot study of the automatic classification approach to identify pavement surface textures using convolutional neural networks (CNNs) is presented. For comparison, the efficiency of the support vector machine (SVM) is also investigated. In total, three cases, (i) pavement types, (ii) texture service ages, and (iii) a combination of (i) and (ii), are involved in the automatic classification. The results indicate that the CNN outperforms the SVM, and the CNN models show a favorable classification accuracy for the above three cases with 93.0%, 81.1%, and 83.8%, respectively. In conclusion, the CNN demonstrates a high capability in expressing the pavement texture features and achieves satisfactory identification results for pavement surface types, but is inferior for texture service age. It is promising that the presented results could serve as a foundational exploration in the automatic identification of texture service conditions benchmarked with standard texture databases to facilitate pavement management systems.