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Related Experiment Video

Updated: Jan 10, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

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Development of a deep learning-based foreign object detection algorithm for coal mine conveyor belts.

Jierui Ling1, Zhibo Fu2, Xinpeng Yuan3

  • 1School of Coal Engineering, Shanxi Datong University, Datong, 037000, China. lingjierui225@163.com.

Scientific Reports
|November 27, 2025
PubMed
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This study introduces an improved YOLOv11 algorithm for foreign object detection on coal mine conveyor belts. The enhanced model offers better accuracy and efficiency, crucial for complex underground environments.

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Mining Engineering

Background:

  • Existing foreign object detection models struggle with accuracy and efficiency in complex underground coal mine environments.
  • These models often exhibit poor recognition of slender and small foreign objects, leading to false and missed detections.
  • Current methods are computationally intensive, large in size, and difficult to deploy on edge devices, hindering real-time applications.

Purpose of the Study:

  • To develop a more accurate and efficient foreign object detection algorithm for coal mine conveyor belts.
  • To address the limitations of existing models in detecting small and slender foreign objects in challenging underground conditions.
  • To create a lightweight and fast detection model suitable for edge deployment in mining environments.
Keywords:
Edge equipmentImage segmentationLightweightReal-time detectionShared convolutional layersYOLOv11

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Last Updated: Jan 10, 2026

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Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
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Main Methods:

  • An improved YOLOv11 algorithm was proposed, incorporating an ADown downsampling module for enhanced small object detection and parameter reduction.
  • The SegNext attention mechanism was integrated to improve image segmentation performance.
  • The C3k2 module was optimized with a lightweight Context Guided mechanism, and a lightweight detection head (LSCD) was used for multi-scale feature handling.

Main Results:

  • The improved model achieved a 1.5% increase in mean Average Precision (mAP), 1.2% in Precision, and 2% in Recall compared to the original model.
  • Significant reductions were observed in model complexity: 28% fewer parameters, 33% less computational load, and 29% smaller storage size.
  • The enhanced model demonstrated improved detection performance for small and slender foreign objects and better deployment flexibility.

Conclusions:

  • The proposed improved YOLOv11 algorithm effectively enhances foreign object detection on coal mine conveyor belts.
  • The modifications lead to superior accuracy, reduced computational cost, and smaller model size, making it suitable for edge deployment.
  • This research provides a valuable solution for real-time foreign object detection in underground coal mining operations.