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A lightweight fire detection algorithm for small targets based on YOLOv5s.

Changzhi Lv1, Haiyong Zhou2, Yu Chen1

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This study introduces an improved YOLOv5s fire detection algorithm, enhancing accuracy and small target recognition in complex environments. The lightweight model achieves high precision and real-time detection speeds.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Fire Safety Engineering

Background:

  • Current fire detection algorithms struggle with low accuracy and poor recognition of small targets in complex settings.
  • Existing methods often lack the efficiency required for real-time applications.

Purpose of the Study:

  • To develop a lightweight and accurate fire detection algorithm.
  • To improve the recognition of small fire targets in challenging environments.
  • To meet the real-time detection demands for enhanced fire safety.

Main Methods:

  • An improved YOLOv5s architecture incorporating the Contextual Transformer (CoT) and a novel CSP1_CoT module.
  • Enhancements to the Neck architecture with a dedicated small target detection layer and SE attention mechanism.
  • Implementation of the Focal-Efficient IoU (Focal-EIoU) loss function for improved convergence and precision.

Main Results:

  • The modified model achieved a mean Average Precision (mAP@.5) of 96% and an accuracy of 94.8%, an 8.8% and 8.9% improvement, respectively.
  • Reduced model parameter count by 1.1% to a compact 14.6MB size.
  • Achieved a detection speed of 85 Frames Per Second (FPS), meeting real-time requirements.

Conclusions:

  • The enhanced YOLOv5s algorithm offers superior precision and accuracy for fire detection.
  • The lightweight design and high detection speed satisfy real-time and resource-constrained application needs.
  • This improved algorithm effectively addresses the limitations of current fire detection systems.