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Updated: Jun 30, 2026

A Method to Estimate Cadaveric Femur Cortical Strains During Fracture Testing Using Digital Image Correlation
09:34

A Method to Estimate Cadaveric Femur Cortical Strains During Fracture Testing Using Digital Image Correlation

Published on: September 14, 2017

High-Precision Detection of Core Fractures Based on Multi-Scale Feature Fusion and Spatial Compression.

Kefeng Du1,2, Xuan Tang1, Yunjin Ge2

  • 1China University of Geoscience, Beijing, No. 29, Xueyuan Road, Haidian District, 100083 Beijing, China.

ACS Omega
|June 29, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces YOLOv11-ESA, a fast and accurate AI model for detecting natural fractures in oil and gas reservoirs. The lightweight framework significantly improves rock fracture identification for better reservoir characterization.

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Area of Science:

  • Geosciences
  • Artificial Intelligence
  • Petroleum Engineering

Background:

  • Natural fractures are crucial for reservoir properties but difficult to identify manually due to subtle morphology and scale variations.
  • Traditional fracture identification methods are inefficient and subjective, hindering accurate reservoir characterization.

Purpose of the Study:

  • To develop a lightweight, high-precision intelligent framework for automated rock-fracture detection.
  • To improve the accuracy and efficiency of identifying natural fractures in low-permeability oil and gas reservoirs.

Main Methods:

  • Proposed YOLOv11-EMBSFPN-SC-AP (YOLOv11-ESA) model incorporating AP, C2PSA, SPPF, EMBSFPN, and SC modules.
  • Utilized a dataset of 4209 core images for training and evaluating the rock-fracture detection model.
  • Enhanced backbone, neck, and detection head for improved feature extraction, fusion, and multiscale prediction.

Main Results:

  • YOLOv11-ESA achieved mAP00.5 of 82.6% and mAP00.5:0.95 of 65.1%, with a recall of 75.0%.
  • The model demonstrated a high inference speed of 1096.5 FPS, 98.6% faster than the baseline YOLOv11.
  • Maintained a compact model size (4.38 MB) and low parameter count (2.05 M), balancing accuracy and efficiency.

Conclusions:

  • The YOLOv11-ESA model offers an optimal balance of detection accuracy, real-time performance, and lightweight design for automated fracture detection.
  • Provides a robust technical foundation for intelligent core analysis and fine reservoir characterization in oil and gas exploration.