A method and system for automatically identifying linear structural surfaces in complex rock outcrops

By combining the DeepLabV3+ model with the greedy algorithm, automatic identification of linear structural surfaces in complex rock outcrops is achieved, solving the identification difficulties in traditional methods and improving recognition accuracy and efficiency.

CN120495762BActive Publication Date: 2025-10-03CHINA HYDROELECTRIC ENGINEERING CONSULTING GROUP CHENGDU RESEARCH HYDROELECTRIC INVESTIGATION DESIGN AND INSTITUTE +1
2 Cites 0 Cited by

Patent Information

Application Number
CN202510590685.8
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-05-08
Publication Date
2025-10-03
Estimated Expiration
2045-05-08

AI Technical Summary

Technical Problem

Traditional methods have difficulty in accurately identifying linear structural surfaces in complex rock outcrops, especially in achieving structured extraction of structural surface trace data in three-dimensional space, and existing image processing methods lack targeted segmentation and classification mechanisms.

Method used

The DeepLabV3+ model is used for semantic segmentation. Combined with 3D point cloud data, the mapping relationship between the image coordinate system and the point cloud is established. The greedy algorithm is used for 3D line segment extraction and global polyline connection, and the iterative Hough transform algorithm is combined for accurate recognition.

Benefits of technology

It achieves rapid and accurate identification of linear structural surfaces in complex rock outcrops, improves identification accuracy and efficiency, reduces calculation time, and breaks through the limitations of manual measurement.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The present invention provides a method and system for automatically identifying linear structural surfaces in complex rock outcrops, belonging to the field of rock engineering. The method involves using a DeepLabV3+ model to semantically segment the input image of a rock outcrop, outputting a binary mask of structural surface trace pixels and performing topological reconstruction; establishing a mapping relationship between the image coordinate system and a three-dimensional point cloud; calculating the spatial angle between the direction vector of the trace and the average attitude of the structural surface group; classifying the three-dimensional point cloud data by structural surface group based on an angle threshold; and extracting three-dimensional line segments using an improved iterative Hough transform algorithm. Finally, a greedy algorithm is used to implement global multi-segment connectivity and trace length calculation for discrete line segments. This method overcomes the topographical limitations of manual measurement when obtaining rock structural surface data, improving the accuracy and efficiency of structural surface extraction in complex rock masses.
Need to check novelty before this filing date? Find Prior Art