Image processing-based classification of pavement fatigue severity using extremely randomized trees, deep neural network, and convolutional neural network

Fatigue failure is a major structural defect found in the asphalt pavement subjected to repeated traffic loadings. In order to establish cost-effective maintenance plans, timely detection of pavement fatigue and classification of its severity are crucial. This study aims at developing an advanced image processing method based on Gaussian steerable filters, projection integrals, and texture descriptors for automating the tasks of interest. The extremely randomized trees (ERT) and deep neural network (DNN) are used to analyze the features extracted from the aforementioned image processing methods. The performance of ERT and DNN is also benchmarked against that of the convolutional neural network. A dataset consisting of 6000 samples has been collected in Da Nang city (Vietnam) to construct and verify the proposed computer vision approaches. Experimental results supported by Wilcoxon signed-rank tests confirm that the ERT-based method has achieved the most desired classification performance with an accuracy rate > 0.93.