US12307641 - Half-cast mark identification and damaged flatness evaluation and classification method for blastholes in tunnel blasting

The patent describes a method for identifying half-cast marks and evaluating damage in tunnel blasting through a series of image processing steps, including denoising, gray-scale processing, and binary processing. It employs a naive Bayes classifier to classify the blasting effects and assess the flatness of surrounding rock surfaces based on extracted multi-dimensional digital features from the images.

Claim 1

1. A half-cast mark identification and damaged flatness evaluation and classification method for blastholes in tunnel blasting, comprising the following specific steps: S1: photographing standard contrast images, a size of each of the standard contrast images being comprehensively determined by an analysis scale, and the standard contrast images comprising a first contrast image of a surrounding rock surface with an ideal half-cast mark and a second contrast image that is unacceptable with excessive backbreak or overbreak; S2: acquiring a half-cast mark image after actual blasting to serve as a third analysis image, a size and a photographing environment of the analysis image being consistent with those of the above contrast images; S3: denoising the above three images by a two-dimensional (2D) Gaussian algorithm according to a characteristic that Gaussian noise in an acquired image obeys a normal distribution; S4: performing gray-scale processing on three denoised images according to a preset threshold of an image histogram to reduce an original data volume in each of the images; S5: performing binary processing on gray-scale processed images, and setting an optimal binary threshold by maximum entropy thresholding; S6: identifying a boundary and a related region of a half-hole mark in each of the images with a gradient vector flow (GVF)-Snake and active contour model (ACM) (GVF-Snake-ACM); S7: importing each of three half-cast mark identified images to ImageJ software, and determining a ratio of an area of a half-cast mark region to a total area of the image as a flatness damage variable; S8: normalizing an interval for an area ratio of a half-cast mark in each of the first contrast image and the second contrast image, and establishing a quantitative relation ω=(D) β between a damage degree and a fractal dimension D, thereby determining a quantitative relation among the area ratio of the half-cast mark region, the damage variable and the fractal dimension; S9: determining a damage value of the third analysis image through linear interpolation calculation on an area ratio of a half-cast mark; S10: extracting, for each of a plurality of half-hole mark identified images with a gray-level co-occurrence matrix (GLCM) in 0°, 45°, 90°, and 135° directions or 180°, 225°, 270°, and 315° directions, five eigenvalues comprising an energy mean, an entropy mean, a contrast mean, a correlation mean, and a uniformity mean, thereby forming a five-dimensional (5D) eigenvector; S11: respectively photographing 90 blasting images for a tunnel excavation surface in three blasting plans, which specifically comprise conventional blasting, presplit blasting and smooth blasting and employ a same explosive charge during tunneling, to form a set C={c 1 , c 2 , c 3 }, analyzing target matrices corresponding to the conventional blasting, the presplit blasting and the smooth blasting, and obtaining 5D eigenvectors F={f 1 , f 2 , f 3 , f 4 , f 5 } in different blasting plans, wherein a feature attribute of each of the images is composed of a 5D eigenvector, comprising an energy mean, an entropy mean, a contrast mean, a correlation mean, and a uniformity mean, thereby obtaining a multi-dimensional digital information feature of the image; S12: randomly selecting 5D eigenvectors of 60 images from the image set in the three different blasting plans as training data to input to a naive Bayes classifier (NBC), and calculating, with five eigenvalues f i (i=1,2,3,4,5) of each of the images, a probability that a training image falls into a category c j (j=1,2,3): S1: photographing standard contrast images, a size of each of the standard contrast images being comprehensively determined by an analysis scale, and the standard contrast images comprising a first contrast image of a surrounding rock surface with an ideal half-cast mark and a second contrast image that is unacceptable with excessive backbreak or overbreak; S2: acquiring a half-cast mark image after actual blasting to serve as a third analysis image, a size and a photographing environment of the analysis image being consistent with those of the above contrast images; S3: denoising the above three images by a two-dimensional (2D) Gaussian algorithm according to a characteristic that Gaussian noise in an acquired image obeys a normal distribution; S4: performing gray-scale processing on three denoised images according to a preset threshold of an image histogram to reduce an original data volume in each of the images; S5: performing binary processing on gray-scale processed images, and setting an optimal binary threshold by maximum entropy thresholding; S6: identifying a boundary and a related region of a half-hole mark in each of the images with a gradient vector flow (GVF)-Snake and active contour model (ACM) (GVF-Snake-ACM); S7: importing each of three half-cast mark identified images to ImageJ software, and determining a ratio of an area of a half-cast mark region to a total area of the image as a flatness damage variable; S8: normalizing an interval for an area ratio of a half-cast mark in each of the first contrast image and the second contrast image, and establishing a quantitative relation ω=(D) β between a damage degree and a fractal dimension D, thereby determining a quantitative relation among the area ratio of the half-cast mark region, the damage variable and the fractal dimension; S9: determining a damage value of the third analysis image through linear interpolation calculation on an area ratio of a half-cast mark; S10: extracting, for each of a plurality of half-hole mark identified images with a gray-level co-occurrence matrix (GLCM) in 0°, 45°, 90°, and 135° directions or 180°, 225°, 270°, and 315° directions, five eigenvalues comprising an energy mean, an entropy mean, a contrast mean, a correlation mean, and a uniformity mean, thereby forming a five-dimensional (5D) eigenvector; S11: respectively photographing 90 blasting images for a tunnel excavation surface in three blasting plans, which specifically comprise conventional blasting, presplit blasting and smooth blasting and employ a same explosive charge during tunneling, to form a set C={c 1 , c 2 , c 3 }, analyzing target matrices corresponding to the conventional blasting, the presplit blasting and the smooth blasting, and obtaining 5D eigenvectors F={f 1 , f 2 , f 3 , f 4 , f 5 } in different blasting plans, wherein a feature attribute of each of the images is composed of a 5D eigenvector, comprising an energy mean, an entropy mean, a contrast mean, a correlation mean, and a uniformity mean, thereby obtaining a multi-dimensional digital information feature of the image; S12: randomly selecting 5D eigenvectors of 60 images from the image set in the three different blasting plans as training data to input to a naive Bayes classifier (NBC), and calculating, with five eigenvalues f i (i=1,2,3,4,5) of each of the images, a probability that a training image falls into a category c j (j=1,2,3): P ⁡ ( c j / F ) = P ⁡ ( c j ) ⁢ P ⁡ ( c j / F ) P ⁡ ( F ) = P ⁡ ( c j ) ⁢ P ⁡ ( f 1 , f 2 , f 3 , f 4 , f 5 / c j ) P ⁡ ( F ) , wherein for an eigenvector F, a larger posterior probability P(c j /F) indicates a higher probability that the F falls into c j , and the category into which the F falls is calculated by: wherein for an eigenvector F, a larger posterior probability P(c j /F) indicates a higher probability that the F falls into c j , and the category into which the F falls is calculated by: c ⁡ ( F ) = arg ⁢ max ⁢ P ⁡ ( c j ) ⁢ ∏ j = 1 3 P ⁡ ( f i | c j ) , and after P(c j ) and P(c j /F) are calculated, a category with a maximum posterior probability is returned, thereby obtaining the category of the F; and S13: inputting eigenvectors of remaining 30 images in the image set in the different blasting plans to a well-trained NBC, and determining a state with a maximum probability in different given categories c j (j=1,2,3) based on a maximum a posteriori (MAP) principle of the NBC to take as a final result for evaluation and classification of the blasting plans, thereby implementing classification on blasting effects of the conventional blasting, the presplit blasting and the smooth blasting and on damaged flatness of surrounding rock surfaces. and after P(c j ) and P(c j /F) are calculated, a category with a maximum posterior probability is returned, thereby obtaining the category of the F; and S13: inputting eigenvectors of remaining 30 images in the image set in the different blasting plans to a well-trained NBC, and determining a state with a maximum probability in different given categories c j (j=1,2,3) based on a maximum a posteriori (MAP) principle of the NBC to take as a final result for evaluation and classification of the blasting plans, thereby implementing classification on blasting effects of the conventional blasting, the presplit blasting and the smooth blasting and on damaged flatness of surrounding rock surfaces.

Google Patents

https://patents.google.com/patent/US12307641

USPTO PDF

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