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Ship-Fire Net: An Improved YOLOv8 Algorithm for Ship Fire Detection.

Ziyang Zhang1, Lingye Tan1, Robert Lee Kong Tiong1

  • 1School of Civil and Environmental Engineering, Nanyang Technological University, Singapore 639798, Singapore.

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PubMed
Summary
This summary is machine-generated.

This study introduces Ship-Fire Net, a lightweight deep learning algorithm for real-time ship fire detection. It achieves high accuracy and speed, improving maritime safety by enabling prompt fire identification.

Keywords:
GhostnetV2ODConvSCConvYOLOv8ndeep learningship-fire detection

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Area of Science:

  • Maritime Safety
  • Artificial Intelligence
  • Computer Vision

Background:

  • Ship fires cause significant economic losses and structural damage.
  • Conventional detection systems struggle with distance and ship motion.
  • Deep learning offers solutions but faces computational complexity challenges.

Purpose of the Study:

  • To develop a lightweight and accurate ship fire detection algorithm.
  • To address the limitations of existing detection methods for maritime environments.
  • To enable real-time monitoring and prompt response to ship fires.

Main Methods:

  • A custom dataset of over 4000 ship fire images was created.
  • YOLOv8n was selected as the base model for its performance and speed.
  • GhostnetV2-C2F and spatial and channel reconstruction convolution (SCConv) were integrated.
  • Omni-dimensional dynamic convolution was employed in the neck for attention.

Main Results:

  • The proposed Ship-Fire Net achieved precision and recall exceeding 0.93 for fire and smoke detection.
  • Mean Average Precision (mAP@0.5) reached approximately 0.9.
  • Ship-Fire Net demonstrated fewer parameters and lower FLOPs than the original YOLOv8n.
  • The algorithm achieved a detection speed of 286 frames per second (FPS).

Conclusions:

  • Ship-Fire Net offers a lightweight and accurate solution for real-time ship fire detection.
  • The enhanced algorithm improves maritime safety through faster and more reliable fire identification.
  • The method effectively balances accuracy with reduced computational cost for practical applications.