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Rock Crack Recognition Technology Based on Deep Learning.

Jinbei Li1, Yu Tian2, Juan Chen2

  • 1School of Hydraulic Engineering, Dalian University of Technology, Dalian 116024, China.

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|July 8, 2023
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Summary
This summary is machine-generated.

This study introduces a deep learning method for detecting rock cracks using drone imagery, improving geological disaster prediction. The enhanced YOLOv7 model with SimAM attention achieves 100% precision, offering rapid and accurate early warnings for landslides and collapses.

Keywords:
YOLOv7attentioncrackdisasterobject detection

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Area of Science:

  • Geological Engineering
  • Computer Science
  • Remote Sensing

Background:

  • Rock surface cracks are critical early indicators of geological disasters like landslides and collapses.
  • Accurate and swift crack detection is essential for effective geological disaster monitoring and mitigation.
  • Drone videography offers a terrain-independent solution for capturing surface crack data.

Purpose of the Study:

  • To develop and evaluate a deep learning-based rock crack recognition technology for early geological disaster detection.
  • To enhance the YOLOv7 model with attention mechanisms for improved crack identification accuracy and efficiency.
  • To establish a novel approach for precise and rapid analysis of rock surface cracks using drone imagery.

Main Methods:

  • Drone-acquired rock surface images were segmented into 640x640 pixel patches.
  • A VOC dataset was created using data augmentation and Labelimg for crack object detection.
  • The YOLOv7 model was modified by integrating various attention mechanisms, including SimAM.
  • Data was partitioned into training (80%) and testing (20%) sets for model evaluation.

Main Results:

  • The improved YOLOv7 model with the SimAM attention mechanism achieved 100% precision, 75% recall, and 96.89% AP.
  • This optimized model processed 100 images in just 10 seconds, outperforming five other models.
  • Compared to the original YOLOv7, the enhanced model showed a 1.67% increase in precision, 1.25% in recall, and 1.45% in AP without compromising speed.

Conclusions:

  • Deep learning-based rock crack recognition technology enables rapid and precise identification of geological hazard precursors.
  • The integration of YOLOv7 with the SimAM attention mechanism provides a highly effective solution for rock crack detection.
  • This research offers a new direction for early warning systems for geological disasters, enhancing public safety.