The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
May. 20, 2025

Filed:

Oct. 11, 2022
Applicants:

Huaqiao University, Quanzhou, CN;

Sanming University, Sanming, CN;

Inventors:

Jin Yu, Xiamen, CN;

Xiaoqiang Fu, Xiamen, CN;

Wei Yao, Xiamen, CN;

Yanyan Cai, Xiamen, CN;

Shiyu Liu, Xiamen, CN;

Assignees:

Huaqiao University, Quanzhou, CN;

Sanming University, Sanming, CN;

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06T 7/136 (2017.01); E21D 9/00 (2006.01); G06T 3/40 (2006.01); G06T 5/20 (2006.01); G06T 5/40 (2006.01); G06T 5/70 (2024.01); G06T 7/00 (2017.01); G06T 7/13 (2017.01); G06T 7/149 (2017.01); G06T 7/194 (2017.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01); G06V 10/776 (2022.01); F42D 3/04 (2006.01);
U.S. Cl.
CPC ...
G06T 7/0002 (2013.01); E21D 9/003 (2013.01); E21D 9/006 (2013.01); G06T 3/40 (2013.01); G06T 5/20 (2013.01); G06T 5/40 (2013.01); G06T 5/70 (2024.01); G06T 7/13 (2017.01); G06T 7/136 (2017.01); G06T 7/149 (2017.01); G06T 7/194 (2017.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01); G06V 10/776 (2022.01); F42D 3/04 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20116 (2013.01); G06T 2207/30181 (2013.01); G06T 2207/30204 (2013.01);
Abstract

The present disclosure relates to a half-cast mark identification and damaged flatness evaluation and classification method for blastholes in tunnel blasting, including the following steps: S1-2: photographing first and second contrast images as well as a half-cast mark image after blasting; S3-6: performing denoising, gray-scale processing and binary processing on the above images, and identifying a boundary of a half-cast mark in each of the images; S7-9: determining a flatness damage variable, a quantitative relation among an area of a half-cast mark region, the damage variable and a fractal dimension, and a damage value of the half-cast mark image; S10-11: forming five-dimensional (5D) eigenvectors to obtain multi-dimensional digital information features of the images; and S12-13: selecting eigenvectors of 60 images as training data to input to a naive Bayes classifier (NBC), and taking eigenvectors of remaining 30 images as classification data to input the above well-trained NBC for classification.


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