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Updated: Jul 1, 2026

Computer Vision-Based Biomass Estimation for Invasive Plants
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Computer Vision-Based Biomass Estimation for Invasive Plants

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LM-YOLO: A Lightweight Multi-Scale Enhanced Model for Forest Smoke Detection Using Unmanned Aerial Vehicles.

Enliang Zhu1, Xin Chen1, Yaolin Zhu1

  • 1School of Electronics and Information, Xi'an Polytechnic University, Xi'an, Shaanxi, China.

Annals of the New York Academy of Sciences
|June 30, 2026
PubMed
Summary
This summary is machine-generated.

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A new lightweight multi-scale You Only Look Once (LM-YOLO) model improves forest smoke detection by enhancing accuracy and efficiency for edge devices. This advanced model offers better real-time performance and supports early forest fire warnings.

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Forestry and Environmental Science

Background:

  • Existing forest smoke detection models struggle with small targets, real-time performance, and edge device efficiency.
  • Limitations hinder effective early forest fire detection and warning systems.

Purpose of the Study:

  • To develop a novel lightweight multi-scale You Only Look Once (LM-YOLO) model for enhanced forest smoke detection.
  • To improve accuracy, real-time capability, and computational efficiency on edge devices.

Main Methods:

  • The LM-YOLO model integrates YOLOv12n's feature extraction and multi-scale fusion.
  • Introduced the RFMBlock for enhanced feature representation and a two-path downsampling module for efficient spatial information preservation.
  • Employed the Shape-IoU loss function to improve localization of small and blurred smoke regions.
Keywords:
RFMBlockSmoke detectionTDownYOLOv12nloss function

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Main Results:

  • LM-YOLO reduced parameters by 34.5% and computational cost by 30.2% compared to the baseline YOLOv12n.
  • Achieved a 4.7% improvement in detection precision on the try123-v4 dataset.
  • Demonstrated 92.7% precision on the public try7 dataset, outperforming existing methods.

Conclusions:

  • The LM-YOLO model offers superior performance in forest smoke detection, particularly for small targets and edge deployments.
  • Its efficiency and accuracy provide reliable technical support for early forest fire warning systems using unmanned aerial vehicles.