Abstract
The detection of cracks is a crucial task in monitoring structural health and ensuring structural safety. The manual process of crack detection is painstakingly time-consuming and suffers from subjective judgments of inspectors. This study establishes an intelligent model based on image processing techniques for automatic crack recognition and analyses. In the new model, a gray intensity adjustment method, called Min-Max Gray Level Discrimination (M2GLD), is proposed to preprocess the image thresholded by the Otsu method. The goal of this gray intensity adjustment method is to meliorate the accuracy of the crack detection results. Experimental results point out that the integration of M2GLD and the Otsu method, followed by other shape analysis algorithms, can successfully detect crack defects in digital images. Therefore, the constructed model can be a useful tool for building management agencies and construction engineers in the task of structure maintenance.
Highlights
Cracks are of major concern for ensuring the safety, durability, and serviceability of structures [1]. e reason is that when cracks are developed and propagate, they tend to cause the reduction in the effective loading area which brings about the increase of stress and subsequently failure of the concrete or other structures [2]
Manual visual inspection is inefficient in terms of both cost and accuracy because it involves the subjective judgments of inspectors
Image binarization, which is widely employed for text recognition and medical image processing [13, 14], is very suitable to be used for crack detection
Summary
Cracks are of major concern for ensuring the safety, durability, and serviceability of structures [1]. e reason is that when cracks are developed and propagate, they tend to cause the reduction in the effective loading area which brings about the increase of stress and subsequently failure of the concrete or other structures [2]. Image binarization, which is widely employed for text recognition and medical image processing [13, 14], is very suitable to be used for crack detection. An image processing model that automatically detects and analyzes cracks on the surfaces of building elements in the digital image is established. E experimental results show that the crack on various structure surfaces can be accurately recognized and analyzed using the proposed image processing model. E paper is organized as follows: the section reviews previous works pertinent to the current study; the third section describes the improved Otsu method based on the M2GLD, followed by the proposed image processing model for the detection of surface crack; the model experimental results are reported in the fifth section; and the final section provides some conclusions of the study At the center of the proposed model, an image enhancement algorithm called Min-Max Gray Level Discrimination (M2GLD) is put forward as a preprocessing step to improve the Otsu binarization approach, followed by shape analyses for meliorating the crack detection performance. e crack detected by the proposed approach was compared with that acquired by the conventional technique. e experimental results show that the crack on various structure surfaces can be accurately recognized and analyzed using the proposed image processing model. e paper is organized as follows: the section reviews previous works pertinent to the current study; the third section describes the improved Otsu method based on the M2GLD, followed by the proposed image processing model for the detection of surface crack; the model experimental results are reported in the fifth section; and the final section provides some conclusions of the study
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