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Mask R-CNN-based reinforced concrete crack identification and measurement method and storage medium

A technology for reinforced concrete and crack identification, applied in image data processing, instruments, calculations, etc., can solve the problems of low efficiency and detection accuracy of crack detection technology, less algorithm research, etc., and achieve the effect of accurate extraction and high efficiency

Inactive Publication Date: 2020-01-31
FUJIAN CHUANZHENG COMM COLLEGE
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Problems solved by technology

[0005] To this end, it is necessary to provide a reinforced concrete crack identification and measurement method and storage medium based on Mask R-CNN, to solve the problem of low efficiency and detection accuracy in existing crack detection technology, and less research on the algorithm combining crack detection and size measurement The problem

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  • Mask R-CNN-based reinforced concrete crack identification and measurement method and storage medium
  • Mask R-CNN-based reinforced concrete crack identification and measurement method and storage medium
  • Mask R-CNN-based reinforced concrete crack identification and measurement method and storage medium

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[0048] In order to explain in detail the technical content, structural features, achieved goals and effects of the technical solution, the following will be described in detail in conjunction with specific embodiments and accompanying drawings.

[0049] see figure 1 , the present embodiment provides a method for identifying and measuring cracks in reinforced concrete based on Mask R-CNN, comprising the following steps:

[0050]Step S110: Obtain a picture to be detected, the picture to be detected is a picture with concrete cracks;

[0051] Step S120: Search and locate the crack area on the picture to be detected based on Mask R-CNN, and segment the crack target in the picture to be detected;

[0052] Step S130: combining the Sobel edge filter operator, the Laplacian filter operator and the Gaussian smoothing filter to perform edge detection on the crack target to generate a crack mask;

[0053] Step S140: According to the pole coordinates of the generated crack mask and the ...

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Abstract

The invention relates to a Mask R-CNN-based reinforced concrete crack identification and measurement method, which comprises the following steps of obtaining a to-be-detected picture which is a picture with a concrete crack; searching and positioning a crack region on the to-be-detected picture based on Mask R-CNN, and segmenting a crack target in the to-be-detected picture; carrying out edge detection on the crack target through combination of a Sobel edge filtering operator, a Laplacian filtering operator and a gaussian smoothing filter, and generating a crack mask; and calculating the length and width of the crack and the crack according to the pole coordinates of the generated crack mask and the minimum bounding rectangle of the mask. Compared with other technologies, the method has relatively high efficiency in crack measurement, has more accurate extraction of crack pixels, and realizes crack detection and identification and calculation of crack length, width and area.

Description

technical field [0001] The invention relates to the technical field of concrete crack identification, in particular to a method for identifying and measuring reinforced concrete cracks based on Mask R-CNN and a storage medium. Background technique [0002] Cracks in reinforced concrete structures greatly affect the structural load transfer capability and durability, posing a significant risk to human safety. Therefore, crack detection is crucial in ensuring the safety of reinforced concrete structures, and in view of this, automatic image-based crack detection as a technique to overcome the safety inspection scheme of reinforced concrete structures has attracted extensive research interest in recent years. [0003] In 2019, Zhong Qu et al. proposed a genetic algorithm based on genetic programming (GP) and seepage model. This method includes three steps: first, pre-extract cracks through the image processing model of GP. Second, the crack front is calculated after extracting...

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Application Information

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IPC IPC(8): G06T7/00G06T7/13G06T7/62
CPCG06T7/0004G06T7/13G06T7/62G06T2207/20084
Inventor 林少丹
Owner FUJIAN CHUANZHENG COMM COLLEGE
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