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GGSYOLOv5: Flame recognition method in complex scenes based on deep learning.

Fucai Sun1, Liping Du1, Yantao Dai1

  • 1School of Mechatronic Engineering, Harbin Vocational & Technical University, Harbin, Heilongjiang, People's Republic of China.

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Summary
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This study introduces GGSYOLOv5, a deep learning flame recognition system for enhanced fire safety. The novel method improves detection accuracy and enables real-time fire monitoring, crucial for protecting lives and property.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Fire Safety Engineering

Background:

  • Fire incidents pose significant risks to public safety and property.
  • Existing flame detection methods struggle with complex environmental conditions.
  • Artificial intelligence offers potential for advanced surveillance and protection.

Purpose of the Study:

  • To develop a robust deep learning-based flame recognition method for complex scenes.
  • To enhance the accuracy and real-time performance of flame detection systems.
  • To deploy an effective flame detection system on an embedded platform.

Main Methods:

  • A modified YOLOv5 architecture named GGSYOLOv5 was proposed.
  • Integration of a Global Attention Mechanism (GAM) into the CSP1 module.
  • Inclusion of a parameterless attention mechanism in feature fusion and replacement of original convolution with packet random convolution (GSConv).

Main Results:

  • The GGSYOLOv5 algorithm demonstrated a 4.46% increase in detection accuracy compared to the original YOLOv5.
  • Achieved a Frames Per Second (FPS) rate of 64.3, meeting real-time detection requirements.
  • Successful deployment of the flame detection system on a Jetson Nano embedded development board.

Conclusions:

  • The GGSYOLOv5 algorithm provides a significant improvement in flame recognition accuracy and speed for complex environments.
  • The developed system is suitable for real-time fire detection and monitoring applications.
  • The integration of advanced deep learning techniques enhances public safety through improved fire detection capabilities.