Abstract
Monitoring the instantaneous and changing concrete surface condition is paramount to cost-effectively managing tunnel assets. In practice, detecting cracks efficiently and accurately is a very challenging task due to concrete blebs, stains, and illumination over the concrete surface. Unclear and tiny cracks cannot be detected effectively. In this paper, we proposed an ultra-efficient crack detection algorithm (CrackHHP) and an improved pre-extraction and second percolation process based on the percolation model to address these issues. Our contributions are shown as follows: 1) apply the overlapping grids and weight-based, redefined pixel value to obtain the candidate dark pixel image while preserving the cracks. 2) introduce the second percolation processing to generate a high-accuracy crack detection algorithm, which can connect the tiny fractures and detect the tiny cracks. 3) construct a high-efficiency and high-accuracy crack detection algorithm combining the improved pre-extraction and the second percolation process. The experimental results demonstrate that CrackHHP can significantly improve the efficiency and accuracy of crack detection.
Highlights
Concrete surface crack detection is very important for the maintenance of concrete structures [1]
We found that some unclear and tiny crack pixels by the influence of noise are not extracted as dark pixels during the pre-extraction process
The experimental results show that our method CrackHHP proposed in this paper can be effectively and fast detect concrete surface crack images
Summary
Concrete surface crack detection is very important for the maintenance of concrete structures [1]. Based on the ground, penetrating radar [10], laser [11] or remote sensing system [12] were proposed to evaluate the road quality These methods can rapidly collect information over wide areas and attain quasi-continuous crack detection results and the depth of cracks [13]. The convolution neural network (CNN) based methods [22][23] favored the local crack patches using Deep Learning They were especially strong at detecting thin cracks under lighting conditions that make detection difficult when using traditional methods. Minimum spanning trees [25] were used to describe the possible connections of sampled crack seeds These methods provided robust and precise results in a wide range of situation, but they may perceive a crack that does not exist [20]. We percolate the neighborhood pixels of dark pixels to connect the tiny fractures
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