Segmentation of Concrete Cracks by Using Fractal Dimension and UHK-Net

被引:41
作者
An, Qing [1 ]
Chen, Xijiang [1 ,2 ]
Wang, Haojun [2 ]
Yang, Huamei [1 ]
Yang, Yuanjun [1 ]
Huang, Wei [1 ]
Wang, Lei [3 ]
机构
[1] Wuchang Univ Technol, Sch Artificial Intelligence, Wuhan 430223, Peoples R China
[2] Wuhan Univ Technol, Sch Safety Sci & Emergency Management, Wuhan 430079, Peoples R China
[3] Xian Univ Architecture & Technol, Coll Mat Sci & Engn, Xian 710055, Peoples R China
关键词
fractal dimension; concrete cracks; detection; deep learning; CONVOLUTIONAL NEURAL-NETWORKS; SURFACES;
D O I
10.3390/fractalfract6020095
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
Concrete wall surfaces are prone to cracking for a long time, which affects the stability of concrete structures and may even lead to collapse accidents. In view of this, it is necessary to recognize and distinguish the concrete cracks. Then, the stability of concrete will be known. In this paper, we propose a novel approach by fusing fractal dimension and UHK-Net deep learning network to conduct the semantic recognition of concrete cracks. We first use the local fractal dimensions to study the concrete cracking and roughly determine the location of concrete crack. Then, we use the U-Net Haar-like (UHK-Net) network to construct the crack segmentation network. Ultimately, the different types of concrete crack images are used to verify the advantage of the proposed method by comparing with FCN, U-Net, YOLO v5 network. Results show that the proposed method can not only characterize the dark crack images, but also distinguish small and fine crack images. The pixel accuracy (PA), mean pixel accuracy (MPA), and mean intersection over union (MIoU) of crack segmentation determined by the proposed method are all greater than 90%.
引用
收藏
页数:18
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