Application of canny operator threshold adaptive segmentation algorithm combined with digital image processing in tunnel face crevice extraction

The present work aims to reduce tunnel construction accidents to personnel. The threshold adaptive segmentation algorithm combined with the Canny operator is employed to extract and detect the cracks on the rock mass of the tunnel face from digital images of the tunnel face. Firstly, the gray change...

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Vydáno v:The Journal of supercomputing Ročník 78; číslo 9; s. 11601 - 11620
Hlavní autoři: Jiang, Feng, Wang, Gang, He, Peng, Zheng, Chengcheng, Xiao, Zhiyong, Wu, Yue
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.06.2022
Springer Nature B.V
Témata:
Adaptive algorithms
Algorithms
Compilers
Computer Science
Construction site accidents
Cracks
Data Mining for IoT in Mobile Edge computing
Digital imaging
Disclaimers
Equalization
Error detection
Flaw detection
Histograms
Image classification
Image contrast
Image enhancement
Image processing
Image segmentation
Interpreters
Processor Architectures
Programming Languages
Rock masses
Tunnel construction
Canny operator
Crevice extraction
Threshold adaptive segmentation
Digital image processing
ISSN:0920-8542, 1573-0484
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Shrnutí:The present work aims to reduce tunnel construction accidents to personnel. The threshold adaptive segmentation algorithm combined with the Canny operator is employed to extract and detect the cracks on the rock mass of the tunnel face from digital images of the tunnel face. Firstly, the gray change processing and histogram equalization technology of the image processing algorithm enhance the contrast of the digital image of rock mass on the tunnel face. Then, the Canny operator and OTSU method construct a threshold adaptive segmentation algorithm to segment the rock mass crevice image after increasing the contrast and to classify crevices on the tunnel face into streak cracks and irregular cracks. Secondly, the segmented image is corrupted, extended, and refined; meanwhile, boundary fitting, separation, merging, and filtering are carried out to form a relatively complete rock boundary recognition result. Finally, the streak crevices and irregular crevices are detected according to the crevice geometry and pixel distribution characteristics to determine the crack direction. The experimental results show that this method can extract complete rock cracks with less than a 2% extraction error rate. Besides, the detection rates of the algorithm for the streak crevices and irregular crevices are 97% and 94%, respectively, and the detection accuracy of the crevice direction is 98%. This indicates that the algorithm proposed here is applicable to geological sketch and provides a reference for the classification of surrounding rock on the tunnel face.
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ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-022-04330-9