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Related Experiment Video

Updated: Jun 25, 2026

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine
08:27

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

Research on Coal Gangue Image Recognition Method Based on EMAM-YOLO.

Ying Jia1,2,3,4, Baoshan Li5, Yongxing Du5

  • 1School of Mining and Coal, Inner Mongolia University of Science and Technology, No.7 Arding Street, Baotou 014010, China.

ACS Omega
|May 25, 2026
PubMed
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Intelligent Coal-Gangue Sorting Method Based on CCD and DE-XRT Image Fusion.

ACS omega·2026

A new EMAM-YOLO model improves coal-gangue detection in mines, achieving 81.17% mAP with high speed and fewer parameters. This robust model enhances accuracy in challenging underground conditions.

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Mining Engineering

Background:

  • Underground coal mines present complex environments with challenges like uneven illumination, coal dust, and varied coal-gangue shapes.
  • Existing detection models often suffer from low accuracy and poor robustness in these conditions.

Purpose of the Study:

  • To develop a real-time coal-gangue image detection model (EMAM-YOLO) that addresses the limitations of current methods in complex mining environments.
  • To enhance detection accuracy, robustness, and real-time performance for intelligent coal washing applications.

Main Methods:

  • Utilized YOLOv12n as the baseline and integrated EfficientNetV1 for backbone reconstruction, reducing parameters while maintaining feature representation.
  • Introduced a Multiscale Attention Feature Pyramid Network (MAFPN) for improved cross-scale feature interaction.

Related Experiment Videos

Last Updated: Jun 25, 2026

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine
08:27

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

  • Designed an Adaptive Spatial Feature Fusion detection head (Detect_ASFF) for optimized multiscale feature fusion and enhanced localization accuracy.
  • Incorporated a multiscale channel attention (MCA) mechanism to focus on critical feature channels.

    • Main Results:

    • The EMAM-YOLO model achieved a mean average precision (mAP50-95) of 81.17%, a 5.04% improvement over the baseline YOLOv12n.
    • The model has 2.59 million parameters and a detection speed of 69.89 FPS, demonstrating a balance between accuracy and real-time performance.
    • Outperformed Faster R-CNN, SSD, YOLOv8n, and YOLOv10n in robustness and detection accuracy under simulated complex conditions.

    Conclusions:

    • The proposed EMAM-YOLO model offers significant improvements in coal-gangue detection accuracy and robustness for underground mining environments.
    • The novel Detect_ASFF head and the synergistic combination of modules provide effective technical support for intelligent coal washing.
    • The model's performance highlights its potential for practical application in enhancing mining automation and efficiency.