Pavement crack detection using convolutional neural network

Publication Type:
Conference Proceeding
Citation:
ACM International Conference Proceeding Series, 2018, pp. 251 - 256
Issue Date:
2018-12-06
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soict_Nhung_Final_V3.pdfAccepted Manuscript version2.34 MB
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© 2018 Association for Computing Machinery. Pavement crack detection is an important problem in road maintenance. There are many processing methods, including traditional and modern methods, solving this issue. Traditional methods use edge detection or some other digital image processing for crack detection, but these approaches are sensitive to many types of noise and unwanted objects on the road. For the purpose of increasing accuracy, image pre-processing methods are required for many of these techniques. Recently, some techniques that utilize deep learning to detect cracks in images have achieved high accuracy, without pre-processing. However, some of them are very complicated, some make use of manually collected data and some methods still need some form of pre-processing. In this paper, we propose a method that applies a convolutional neural networks to detect cracks in pavement images. Our research uses two data sets, one public data set and the other collected by ourselves. We also experimentally compare our method with some exiting methods and the experiments show that the proposed approach achieves high accuracy and generates stable models.
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