Journal
INTERNATIONAL JOURNAL OF PAVEMENT ENGINEERING
Volume 21, Issue 4, Pages 457-463Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/10298436.2018.1485917
Keywords
Pavement; crack classification; deep learning; convolutional neural network; receptive field
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Funding
- National Natural Science Foundation of China [U153420027, 51478398]
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The classification of pavement crack heavily relies on the engineers' experience or the hand-crafted algorithms. Convolutional Neural Network (CNN) has demonstrated to be useful for image classification, which provides an alternative to traditional imaging classification algorithms. This paper proposes a novel method using deep CNN to automatically classify image patches cropped from 3D pavement images. In all, four supervised CNNs with different sizes of receptive field are successfully trained. The experimental results demonstrate that all the proposed CNNs can perform the classification with a high accuracy. Overall classification accuracy of each proposed CNN is above 94%. Upon the evaluation of these neural networks with respect to accuracy and training time, we find that the size of receptive field has a slight effect on the classification accuracy. However, the CNNs with smaller size of receptive field require more training times than others.
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