An image-based system for pavement crack evaluation using transfer learning and wavelet transform

Abstract

Automatic systems for pavement inspection can significantly enhance the performance of the Pavement Management Systems (PMSs). Cracking is the most current distress in any type of pavement. Progress of various technologies leads to a lot of effort in developing an automatic system for pavement cracking inspection. In the early image-based systems, the feature extraction process for crack classification must be done by using various image processing techniques in an expert-based system. In recent years, the new machine learning techniques such as a deep convolutional neural network (DCNN) provide more efficient models with the ability of automatic feature extracting, but these models need a lot of labeled data for training. Transfer learning is a technique that solves this problem using pre-trained models. In this research, several pre-trained models (AlexNet, GoogleNet, SqueezNet, ResNet-18, ResNet-50, ResNet-101, DenseNet-201, and Inception-v3) have been used to retrain based on pavement images using transfer learning. This study aims to evaluate the efficiency of retrained DCNNs in the detection and classification of the pavement cracking. Also, it presents a more effective algorithm based on a developed wavelet transform module with more regulizer parameters for crack segmentation. The result indicated that retrained classifier models provide reliable outputs with a range of 0.94 to 0.99 in confusion matrix-based performance, but the speed of some models is significantly higher than others. Also, the results clarified that the developed wavelet module could segment crack pixels with a high level of clarity.