Pavement Distress Image Automatic Classification Based on DENSITY-Based Neural Network

Abstract

This study proposes an integrated neural network-based crack imaging system to classify crack types of digital pavement images, which was named DENSITY-based neural network(DNN).The neural network was developed to classify various crack types based on the subimages (crack tiles) rather than crack pixels in digital pavement images. The spatial neural network was trained using artificially generated data following the Federal Highway Administration (FHWA) guidelines. The optimal architecture of each neural network was determined based on the testing results from different sets of the number of hidden units, and the number of training epochs. To validate the system, computer- generated data as well as the actual pavement pictures taken from pavements were used. The final result indicates that the DNN produced the best results with the accuracy of 99.50% for 1591 computer-generated data and 97.59% for 83 actual pavement pictures. The experimental results have demonstrated that DNN is quite effective in classifying crack type, which will be useful for pavement management.

This work was supported by National Basic Research Program of China (2005CB724205).