In order to extract meaningful observations from an image, it is essential to first convert it into a digital format and then apply a particular processing methodology. In the domain of image processing, it is a prevalent convention to consider all images as signals that are two-dimensional in nature, while utilizing conventional signal processing methodologies. The existence of surface fissures in concrete acts as an initial indication of probable structural deterioration. The utilization of image-based automated fracture identification is proposed as a viable alternative in situations where a human replacement is unavailable. This paper provides a critical review of crack detection using image processing. The scholarly literature encompasses a range of image processing techniques that can be employed for the automated identification of fractures and their respective depths. The present research involves a comprehensive examination with the objective of discerning the existing obstacles and past accomplishments within this area of investigation. A total of 24 publications related to the detection of Ato cracks have been selected for the purpose of conducting a comprehensive review. Following the review, a comprehensive analysis is performed on various image processing techniques, encompassing their respective objectives, degrees of accuracy and inaccuracy, as well as the datasets of images utilized. This study also presents future research efforts in identifying and resolving the problem of crack detection.
Image Processing, Image Analysis, Image Compression, Crack Detection, Big Data Technology, Artificial Neural Network.
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The authors would like to thank to the reviewers for nice comments on the manuscript.
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Department of Modern Mechanics, University of Science and Technology of China, Hefei, Anhui province, China.
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Cite this article
Zhu Jiping, “A Critical Review of Crack Detection Based on Image Processing”, Journal of Computing and Natural Science, vol.3, no.4, pp. 204-215, October 2023. doi: 10.53759//181X/JCNS/202303019.