Abstract
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%.
Highlights
Concrete is the most widely used construction material in the world [1]
In order to evaluate the performance of the proposed method, we conduct the segmentation of concrete cracks under complex background
We use the fractal dimension to conduct the initial recognition of concrete cracks, and obtain some images which includes cracks
Summary
Concrete is the most widely used construction material in the world [1]. Some researchers have used the fractal dimension to analyze the characteristics of concrete [2,3].Concrete often has some cracks. Concrete is the most widely used construction material in the world [1]. Some researchers have used the fractal dimension to analyze the characteristics of concrete [2,3]. It is very important to detect the concrete cracks because the damage of cracked concrete will control the action of the concrete structure. The most common detection method is still manual detection, which is inefficient and has low accuracy, and presents safety problems to personnel. With the development of computer vision technology, crack detection based on digital image processing method has become a hot research topic in concrete surface crack detection
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