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Related Experiment Video

Updated: Sep 15, 2025

Detection and Quantification of Tunneling Nanotubes Using 3D Volume View Images
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Research on lightweight tunnel cable fire recognition algorithm based on multi-scale features.

Zimeng Liu1,2, Lei Zhang3, Huiqiang Ma4,5

  • 1College of Environmental and Safety Engineering, Liaoning Petrochemical University, Fushun, 113001, Liaoning, China. ameng679@163.com.

Scientific Reports
|July 16, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a faster, more accurate YOLO-v5 based fire detection system for tunnels using lightweight networks and attention mechanisms. The enhanced algorithm significantly improves detection speed and precision for tunnel cable fires.

Keywords:
Cable fireDeep learningImage recognitionMulti-scale feature fusionYOLO-v5

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Fire Safety Engineering

Background:

  • Tunnel fire detection systems often suffer from slow response times and high false alarm rates.
  • Intelligent fire detection using computer vision is a growing area of research for improved tunnel safety.

Purpose of the Study:

  • To develop a lightweight and efficient YOLO-v5 algorithm for tunnel cable fire recognition.
  • To enhance the speed and accuracy of fire detection in tunnel environments.

Main Methods:

  • Proposed a lightweight YOLO-v5 algorithm by replacing Darknet53 with Mobilenetv3-small and integrating the SimAM attention mechanism.
  • Implemented a Bi-directional Feature Pyramid Network (BiFPN) for feature fusion and utilized the GIou_Loss function.
  • Created a tunnel cable fire image database under various wind conditions for model validation.

Main Results:

  • The modified YOLO-v5 network achieved a 0.4% increase in mean Average Precision (mAP) to 99%.
  • The Frames Per Second (FPS) improved by 46.7% to 179, indicating enhanced detection speed.
  • Experimental validation confirmed the model's accuracy and feasibility for tunnel fire detection.

Conclusions:

  • The proposed lightweight YOLO-v5 algorithm effectively addresses the limitations of traditional tunnel fire detection.
  • This approach offers significant improvements in precision and speed, crucial for effective tunnel fire management and loss prevention.