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September 28, 20250 citationsOpen Access

Intelligent Identification of Overburden Fractures Using Deep Learning and Data Fusion Techniques

Intelligent Identification of Overburden Fractures in Physical Simulation Experiments Based on Improved U-Net Deep Learning and Multimodal Data Fusion

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Authors

PZPei ZhangYWYujie WeiLLLiang Li

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Overview

Automatic fracture identification enhances recognition accuracy in physical simulations, suggesting improved mining practices.

Key Points

  • The intelligent recognition system achieves 200 frames per second in real-time processing, enhancing operational efficiency.
  • Experimental results show a Dice coefficient of 0.91, indicating significantly better performance compared to traditional methods.
  • The approach incorporates multimodal data types, including RGB imagery and FMI resistivity data, for comprehensive fracture analysis.
  • Integration of attention gating mechanisms and ResNet-34 improves the model's ability to handle complex backgrounds effectively.

Cite This Study

Zhang et al. (2025) studied this question.

synapsesocial.com/papers/68d90a0f41e1c178a14f6aeehttps://doi.org/10.21203/rs.3.rs-7281191/v1
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