Based on an Overview of Crack Detection
DOI:
https://doi.org/10.54691/dddp5674Keywords:
Crack Detection; Digital Imaging Technology; Deep Learning; Crack Detection Algorithms.Abstract
This Crack detection, as an important part of concrete structural health monitoring, aims to reflect the stress state and damage degree of the structure. Traditional concrete crack detection mainly relies on manual visual identification, which has low detection efficiency and accuracy. In addition, manual visual detection has problems such as being greatly affected by lighting conditions, being unable to cover overhead locations such as bridge towers and high piers, and being highly subjective. In recent years, in order to solve the above problems, researchers at home and abroad have developed concrete crack detection equipment based on digital image technology, such as inspection vehicles equipped with high-resolution cameras, unmanned aerial vehicles, crawling robots, and so on. Meanwhile, efficient and accurate crack detection algorithms are the basis for realizing accurate crack identification. How to balance the detection speed and accuracy has been the focus of academic attention. This paper reviews the research progress of concrete crack detection equipment based on digital image technology at home and abroad in recent years, including camera platform, calibration method, preprocessing, traditional and deep learning algorithms, crack feature extraction, image stitching, and 3D representation and monitoring of cracks. In addition, the deficiencies in the research are summarized.
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