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Fire Detection and Notification Method in Ship Areas Using Deep Learning and Computer Vision Approaches.

Kuldoshbay Avazov1, Muhammad Kafeel Jamil1, Bahodir Muminov2

  • 1Department of Computer Engineering, Gachon University, Seongnam-si 461-701, Republic of Korea.

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Summary

This study introduces an advanced fire detection system for ships using YOLOv7 deep learning, achieving 93% accuracy. The technology enhances maritime safety by enabling real-time fire recognition and mitigation.

Keywords:
E-ELANYOLOv7deep learningfireflame detectionships

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Area of Science:

  • Maritime Safety
  • Artificial Intelligence
  • Computer Vision

Background:

  • Ship fires pose severe risks to crew, cargo, and the environment.
  • Timely fire detection is crucial for effective mitigation and response.

Purpose of the Study:

  • To develop and evaluate a novel fire detection system for maritime environments.
  • To leverage deep learning, specifically YOLOv7, for enhanced fire recognition capabilities.

Main Methods:

  • Utilized YOLOv7 with an improved E-ELAN backbone for fire detection.
  • Trained the model on 4622 augmented images of ship scenarios.
  • Evaluated model performance using standard metrics and compared with existing methods.

Main Results:

  • Achieved a 93% accuracy rate in fire detection.
  • Demonstrated superior feature fusion and recognition capabilities compared to predecessors.
  • Showcased potential for real-time detection in challenging maritime conditions.

Conclusions:

  • The proposed YOLOv7-based system significantly enhances maritime fire safety.
  • The model is effective for real-time fire detection and small object recognition in ship environments.
  • This deep learning approach offers a promising solution for ship protection and port fire monitoring.