Abstract
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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Acknowledgements
This research was funded by Key research and development program of Shandong province (Grant Number: 2019GSF111022); National Natural Science Foundation of China (Grant Number: 51909150).
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Jiang, F., Wang, G., He, P. et al. Application of canny operator threshold adaptive segmentation algorithm combined with digital image processing in tunnel face crevice extraction. J Supercomput 78, 11601–11620 (2022). https://doi.org/10.1007/s11227-022-04330-9
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DOI: https://doi.org/10.1007/s11227-022-04330-9