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Related Experiment Videos

DLR-YOLO: A High-Accuracy Lightweight Object Detector for Complex Underground Coal Mine Environments.

Xiaohang Cai1, Ruimin Wang2, Jianhui Zhang1,3

  • 1School of Cyberspace Security, Zhengzhou University, Zhengzhou 450002, China.

Sensors (Basel, Switzerland)
|May 27, 2026
PubMed
Summary
This summary is machine-generated.

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This study introduces DLR-YOLO, an improved object detection system for underground coal mines. It significantly enhances detection accuracy and confidence in challenging low-light and dusty conditions.

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Mining Engineering

Background:

  • Object detection in underground coal mines faces challenges like low illumination, dust, occlusion, and scale variation.
  • These issues lead to missed detections and low confidence, impacting safety monitoring.

Purpose of the Study:

  • To develop a high-performance, lightweight object detector for underground coal mines.
  • To address limitations of existing methods in low-light and high-noise environments.

Main Methods:

  • Proposed DLR-YOLO, a lightweight object detector based on YOLOv11n.
  • Integrated Dynamic Multi-scale Global Perception Enhancement Module (DMGPEM) for adaptive feature extraction.
  • Incorporated Lightweight Cross-Attention (LCA) module for feature fusion and noise suppression.
Keywords:
DLR-YOLOYOLOv11nfeature fusionlightweight object detectormulti-scale feature extractionunderground coal mine object detection

Related Experiment Videos

  • Developed Reparameterized stem (RepStem) module for efficient initial feature extraction.

    • Main Results:

    • DLR-YOLO achieved 94.4% mAP@50 and 66.7% mAP@50-95 on an in-house dataset, outperforming the YOLOv11n baseline.
    • Demonstrated effectiveness of individual modules through ablation studies.
    • Maintained a lightweight architecture (2.7M parameters, 6.6 GFLOPs) with high inference speed (157.1 FPS).
    • Outperformed YOLOv12, YOLOv13, and RT-DETR in detection performance and speed.

    Conclusions:

    • DLR-YOLO offers a robust and efficient solution for object detection in complex underground coal mine environments.
    • The proposed modules effectively enhance feature extraction, fusion, and noise suppression.
    • Provides a strong technical foundation for real-time safety monitoring systems.