A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Recognition of asphalt pavement crack length using deep convolutional neural networks
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.
Recognition of asphalt pavement crack length using deep convolutional neural networks
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.
Recognition of asphalt pavement crack length using deep convolutional neural networks
Tong, Zheng (author) / Gao, Jie (author) / Han, Zhenqiang (author) / Wang, Zhenjun (author)
Road Materials and Pavement Design ; 19 ; 1334-1349
2018-08-18
16 pages
Article (Journal)
Electronic Resource
English
Vision-Based Crack Detection of Asphalt Pavement Using Deep Convolutional Neural Network
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Automatic classification of pavement crack using deep convolutional neural network
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