Abstract
The identification and measurement of building cracks can reduce the harm caused by accidents to people’s lives. Reducing manual work can improve the efficiency of crack inspection and reduce inspection errors. In this paper, (1) Machine vision with high measurement accuracy and fast response speed is used, the crack contour is extracted by OpenCV splicing, segmentation, morphological processing, and wall cracks can be accurately detected. (2) The crack size is calculated using the pixel ratio algorithm, the image pixel ratio is calculated by the reference object method, and the size of the wall crack is measured. (3) The measurement error is small. In comparing the experimental results and manual measurement, the width measurement error rate is the highest, 0.314%, and the height error rate is the highest, 2.9%. (4) Size information can be displayed to prevent the hazard caused by cracks in advance. So, the system has a good wall crack identification and measurement effect, which reduces labor costs.
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