Recognition of asphalt pavement crack length using deep convolutional neural networks

Abstract

Crack length measurement is an important part of asphalt pavement detection. However, some crack measurement techniques cannot satisfy the needs of accuracy and efficiency. This study discusses application of deep convolutional neural networks (DCNN) in automatic recognition of pavement crack length in batches. Original red, green and blue images were transformed to grey-scale images to calculate their threshold and pre-extract cracks’ properties by k-means clustering analysis. Then the pre-extracted crack images were used as both training and testing samples. The process of accomplishing DCNN to recognise the crack length included the structure designing, training, and testing of the networks. The output results of well-trained DCNN were compared with those of the actual measurement to verify the accuracy of the networks. The result indicates that the training strategy including two processes overcomes the lack of crack labelled images and improves the accuracy of the network, combining with quadrature encoding and stochastic gradient descent. Recognition accuracy of DCNN is 94.36%, maximum length error is 1 cm and mean squared error is 0.2377. The error rates of length ranges 6–7 cm and 7–8 cm are bigger than other ranges Therefore, the networks can be adopted to measure the crack length accurately, but more 6–8 cm crack images should be used to improve the accuracy of the networks in future.

Keywords:

• asphalt pavement crack
• deep convolutional neural networks (DCNN)
• deep learning
• picture processing
• stochastic gradient descent
• k-means clustering analysis

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The authors thank the supports from the Fundamental Research Funds for the Central Universities of China [No. 310831153504 and 310831163113] and National and Local Joint Engineering Materials Laboratory of Traffic Engineering and Civil Engineering, Chongqing Jiaotong University [No: LHSYS-2016-002].