Ground fracture detection method based on remote sensing image visual analysis

By constructing a dynamic monitoring model for ground fissures and using remote sensing image visual analysis and generative adversarial networks to generate virtual samples, the problems of insufficient efficiency and accuracy in ground fissure identification in existing technologies were solved, and automated detection and efficient identification of ground fissures in drone images were achieved.

CN120689784APending Publication Date: 2025-09-23中煤能源研究院有限责任公司
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Patent Information

Application Number
CN202510849991.9
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-06-24
Publication Date
2025-09-23

AI Technical Summary

Technical Problem

Existing technologies require a large number of image samples for training in the process of constructing a dynamic monitoring model for ground fissures, and the inspection results are not accurate enough, making it difficult to achieve efficient and automated identification of ground fissures in mining areas.

Method used

A ground fissure detection method based on visual analysis of remote sensing images is adopted. By constructing a dynamic ground fissure monitoring model, Faster R-CNN and LSTM networks are used to detect and predict crack areas. The crack paths are displayed on a GIS platform, and virtual samples are generated through a generative adversarial network. This optimizes the model training process and improves the recognition accuracy and robustness of the model.

Benefits of technology

It has realized the automated detection of ground fissures in drone images, significantly improved the efficiency and accuracy of ground fissure identification, replaced traditional manual inspections, supported wider-area, higher-frequency, and lower-cost mining area inspections, and provided technical support for environmental protection and restoration management.

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Abstract

The invention discloses a ground fracture detection method based on remote sensing image visual analysis, which comprises the following steps of: sending a remote sensing image to a trained ground fracture dynamic monitoring model, outputting a fracture path by the ground fracture dynamic monitoring model, outputting a vector element graph layer containing a detected fracture position, and identifying a section of path identified as a fracture corresponding to each element, displaying through a GIS platform; the construction and training process of the ground fracture dynamic monitoring model comprises the following steps: S1, constructing the ground fracture dynamic monitoring model, and manually marking the accuracy of a detection result of a DOM image sample; s2, improving the recognition precision of the fine-line object target in the model structure; s3, optimizing the extraction efficiency of the interference features; s4, obtaining landform parameters of a to-be-monitored area, and introducing a high-speed interpretation image sample; and S5, obtaining a trained ground fracture dynamic monitoring model. The invention belongs to the technical field of geological image processing, and significantly improves the efficiency and accuracy of ground fracture recognition.
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