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Related Experiment Video

Updated: Aug 8, 2025

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging
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Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Published on: April 18, 2025

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Enhancement of Marine Lantern's Visibility under High Haze Using AI Camera and Sensor-Based Control System.

Jehong An1, Kwonwook Son2, Kwanghyun Jung1

  • 1Lighting & Energy Research Division, Korea Photonics Technology Institute 9, 500-460 Cheomdan Venture-ro 108beon-gil, Buk-gu, Gwangju 61007, Republic of Korea.

Micromachines
|February 25, 2023
PubMed
Summary
This summary is machine-generated.

This study developed an AI system to automatically adjust marine lantern brightness based on sea fog density, enhancing maritime safety. The intelligent system improves visibility and optimizes power usage in challenging marine conditions.

Keywords:
LED marine lanternartificial intelligencecomputer visiondeep learningdehazeembedded systemsea fogserial communication

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Area of Science:

  • Maritime Safety
  • Artificial Intelligence
  • Image Processing

Background:

  • Maritime safety is often compromised by poor visibility due to sea fog and haze.
  • Current marine lanterns have manual brightness adjustments, which are inefficient in dynamic weather conditions.

Purpose of the Study:

  • To develop an intelligent system for automatic marine lantern brightness control.
  • To enhance navigational sign visibility during sea fog and haze events.
  • To improve power efficiency and safety in maritime navigation.

Main Methods:

  • Utilized artificial intelligence, camera sensors, and an embedded board for real-time fog analysis.
  • Developed a deep learning-based dehaze model using marine image datasets.
  • Derived sea fog concentration using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) values.
  • Implemented serial communication to control LED marine lantern brightness based on fog density.

Main Results:

  • Successfully generated clear images from hazy ones using the deep learning model.
  • Quantified sea fog concentration by comparing original and dehazed images via PSNR and SSIM.
  • Achieved autonomous control of marine lantern brightness correlating with fog density.
  • Demonstrated enhanced visibility and efficient power consumption compared to manual systems.

Conclusions:

  • The developed system enables autonomous, fog-density-based brightness control for marine lanterns.
  • This technology offers significant improvements in maritime safety and energy efficiency.
  • The fog concentration estimation method is adaptable for various applications, including local weather forecasting and autonomous marine navigation.