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Related Experiment Video

Updated: Jul 20, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
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Fire Detection in Ship Engine Rooms Based on Deep Learning.

Jinting Zhu1, Jundong Zhang1, Yongkang Wang1

  • 1College of Marine Engineering, Dalian Maritime University, Dalian 116026, China.

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|July 29, 2023
PubMed
Summary

Ship engine room fires pose significant risks. This study enhances YOLOv7-tiny deep learning for faster, more accurate fire detection, improving safety at sea.

Keywords:
SIoUYOLOv7-tinycoordinate attention mechanismdeep learningfire detectionpartial convolution

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Area of Science:

  • Maritime Safety
  • Artificial Intelligence
  • Computer Vision

Background:

  • Ship fires, particularly in engine rooms, present severe safety challenges due to remoteness and complex environments.
  • Traditional fire detection methods in ship engine rooms are limited in speed and accuracy.
  • Deep learning offers a promising approach for rapid and precise fire detection.

Purpose of the Study:

  • To enhance the YOLOv7-tiny model for improved fire detection in ship engine rooms.
  • To increase the speed and accuracy of fire detection systems on vessels.
  • To address the limitations of conventional fire detection methods in maritime settings.

Main Methods:

  • Improved YOLOv7-tiny model incorporating partial convolution (PConv) and coordinate attention (CA) mechanisms.
  • Utilized SIoU as the loss function to accelerate model convergence and boost accuracy.
  • Developed and employed a custom dataset of ship engine room fires for training and evaluation.

Main Results:

  • The enhanced YOLOv7-tiny model achieved a 2.6% increase in mAP@0.5.
  • Detection speed was improved by 10 frames per second (fps).
  • The modifications successfully enhanced feature extraction and detection performance.

Conclusions:

  • The improved YOLOv7-tiny model effectively meets the demands for rapid and accurate fire detection in ship engine rooms.
  • Integrating PConv, CA, and SIoU significantly boosts the performance of deep learning-based fire detection systems.
  • This advancement contributes to enhanced maritime safety by enabling quicker responses to engine room fires.