Tunnel crack detection and measurement method based on dual-depth learning model
The invention belongs to the technical field of tunnel detection, and particularly relates to a tunnel crack detection and measurement method based on a dual-depth learning model, which comprises thefollowing steps: acquiring a tunnel image; creating a training set of target detection; creating a tr...
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Main Authors | , , , , , , , , , , , |
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Format | Patent |
Language | Chinese English |
Published |
16.03.2021
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Subjects | |
Online Access | Get full text |
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Summary: | The invention belongs to the technical field of tunnel detection, and particularly relates to a tunnel crack detection and measurement method based on a dual-depth learning model, which comprises thefollowing steps: acquiring a tunnel image; creating a training set of target detection; creating a training set of semantic segmentation; training a target detection model; training a semantic segmentation model; detecting an input image to be detected by using the trained target detection model to judge whether a crack exists or not; inputting the image with the crack into a trained semantic segmentation model for image segmentation; predicting the type of the image, the coordinates of the crack in the image and the crack length information; and outputting and storing a model prediction result. Compared with a pure image processing technology, the invention is high in detection accuracy and high in detection speed; compared with a pure deep learning algorithm, the invention has the advantages of a deep learning a |
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Bibliography: | Application Number: CN202011506567 |