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Updated: Mar 11, 2026

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
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Forest fire detection and recognition method based on improved YOLOv5-ACE algorithm.

Yu Zhao1, Chao Tang1

  • 1School of Emergency Management, Chongqing Vocational Institute of Safety Technology, Wanzhou, China.

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|March 9, 2026
PubMed
Summary
This summary is machine-generated.

A new forest fire detection model improves accuracy by 11.5% using an enhanced YOLOv5-ACE algorithm. This advanced system offers faster, more precise wildfire detection, crucial for fire safety and prevention.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Environmental Science

Background:

  • Forest fires pose significant fire safety challenges.
  • Existing detection methods struggle with small targets, complex backgrounds, and edge device limitations.

Purpose of the Study:

  • To develop an optimized forest fire detection and recognition model.
  • To enhance accuracy and efficiency in identifying wildfires.

Main Methods:

  • An improved YOLOv5-ACE algorithm incorporating CBAM and ASPP modules for feature extraction.
  • Integration of ShuffleNet v2 grouped convolution and ViT for a lightweight yet robust model.
  • Enhancement of small target detection, positioning accuracy, and anti-interference capabilities.

Main Results:

  • The enhanced model achieved a detection accuracy of 92.3%, an 11.5% increase over traditional YOLOv5.
  • Recall rate improved by 6.8% to 91.6%.
  • All performance improvements were statistically significant (p < 0.05).

Conclusions:

  • The proposed model offers faster and more accurate forest fire detection.
  • This method provides significant guidance for wildfire prevention strategies.
  • The model demonstrates improved performance in challenging detection scenarios.