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RT-FogNet: real-time ship detection under low-visibility conditions in inland waterways.

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RT-FogNet improves real-time ship detection in foggy inland waterways using a novel dehazing module and enhanced YOLOv10 backbone. This system offers high accuracy and speed for practical maritime surveillance.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Maritime Technology

Background:

  • Real-time ship detection in inland waterways is hindered by poor visibility conditions like fog and reflections.
  • Existing methods often struggle with the unique challenges of dynamic inland water environments.

Purpose of the Study:

  • To develop a lightweight and deployable framework for real-time ship detection in challenging inland waterway conditions.
  • To enhance image clarity and multi-scale feature representation for improved detection accuracy.

Main Methods:

  • Proposed RT-FogNet framework integrating a Water Surface Image Dehazing (WSID) module.
  • Incorporated a Dilated Shared Spatial Pyramid Pooling Fast (DS-SPPF) module into an enhanced YOLOv10 backbone.
  • Introduced the Inland Waterway Ship Dataset (IWSD) with 9,305 annotated images.

Main Results:

  • RT-FogNet achieved significant performance gains, including over 15 percentage points improvement in AP on the IWSD.
  • Demonstrated high inference speed and low parameter complexity.
  • Validated performance on COCO, SeaShips, and the newly introduced IWSD datasets.

Conclusions:

  • RT-FogNet is a practical and effective solution for real-time ship detection in low-visibility inland waterways.
  • The framework's lightweight design and performance make it suitable for deployment in vision-based ship monitoring systems.
  • The new IWSD dataset facilitates robust evaluation of ship detection algorithms in diverse inland conditions.