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Updated: Jan 22, 2026

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Forest fire prediction using image processing.

Yingdan Li1,2, Junting Chen1, Yaxuan Zeng1

  • 1School of Electronic Information Engineering, Guiyang University, Guiyang, China.

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|January 20, 2026
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Summary
This summary is machine-generated.

Early forest fire detection is crucial for safety. A new YOLOv5-PSG model significantly improves real-time fire recognition accuracy, enhancing wildfire prevention and environmental protection.

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Area of Science:

  • Environmental Science
  • Computer Science
  • Artificial Intelligence

Background:

  • Forest fires present substantial risks to public safety and ecosystems.
  • Early detection is vital to prevent small fires from escalating into major disasters.
  • Traditional fire prediction methods lack the accuracy and real-time capabilities needed for effective intervention.

Purpose of the Study:

  • To enhance the accuracy and real-time detection capabilities of forest fire prediction systems.
  • To introduce an improved YOLOv5-PSG model for more effective early warning and prediction of wildfires.

Main Methods:

  • The study proposes an improved version of the YOLOv5 model, termed YOLOv5-PSG.
  • The model underwent 300 rounds of rigorous training and learning.

Main Results:

  • The YOLOv5-PSG model achieved an average recognition accuracy rate of 93.1% (mAP).
  • The model demonstrated an accuracy rate of approximately 0.802 and a confidence level of about 0.965 after training.
  • These results indicate a significant improvement over traditional methods.

Conclusions:

  • The enhanced YOLOv5-PSG model offers more comprehensive and effective early warning and prediction for forest fires.
  • This advancement contributes to better mitigation of wildfire impacts, protecting human life and the environment.