Road crack detection and quantification based on segmentation network using architecture of matrix
ISSN: 0264-4401
Article publication date: 1 July 2021
Issue publication date: 8 February 2022
Abstract
Purpose
Periodic inspection and maintenance are essential for effective pavement preservation. Cracks not only affect the appearance of the road and reduce the levelness, but also shorten the life of road. However, traditional road crack detection methods based on manual investigations and image processing are costly, inefficiency and unreliable. The research aims to replace the traditional road crack detection method and further improve the detection effect.
Design/methodology/approach
In this paper, a crack detection method based on matrix network fusing corner-based detection and segmentation network is proposed to effectively identify cracks. The method combines ResNet 152 with matrix network as the backbone network to achieve feature reuse of the crack. The crack region is identified by corners, and segmentation network is constructed to extract the crack. Finally, parameters such as the length and width of the cracks were calculated from the geometric characteristics of the cracks and the relative errors with the actual values were 4.23 and 6.98% respectively.
Findings
To improve the accuracy of crack detection, the model was optimized with the Adam algorithm and mixed with two publicly available datasets for model training and testing and compared with various methods. The results show that the detection performance of our method is better than many excellent algorithms, and the anti-interference ability is strong.
Originality/value
This paper proposed a new type of road crack detection method. The detection effect is better than a variety of detection algorithms and has strong anti-interference ability, which can completely replace traditional crack detection methods and meet engineering needs.
Keywords
Acknowledgements
The research is jointly supported by the Key Research and Development Program of Shaanxi (2020ZDLGY09-03), the Key Research and Development Program of Guangxi (GK-AB20159032), the Fund of National Engineering and Research Center for Mountainous Highways (GSGZJ-2020-08), the Science and Technology Bureau of Xi'an project (2020KJRC0130) and the Open Fund of the Inner Mongolia Transportation Development Research Center (2019KFJJ-006).
Data availability statement: The data in this paper are made up of public datasets and private data, and the raw data needed to reproduce these findings cannot be shared at this time because it is also part of ongoing research.
Citation
Li, G., Chen, Y., Zhou, J., Zheng, X. and Li, X. (2022), "Road crack detection and quantification based on segmentation network using architecture of matrix", Engineering Computations, Vol. 39 No. 2, pp. 693-721. https://doi.org/10.1108/EC-01-2021-0043
Publisher
:Emerald Publishing Limited
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