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<p>This paper presents the development and evaluation of a deep neural network model for the detection of naval surface vessel using laboratory-generated datasets. By employing chroma-key technology, images of a scale model naval vessel were superimposed onto realistic maritime backgrounds to create a diverse training dataset. Fine-tuned with these datasets and evaluated using the YOLOv8 framework, the model achieved high precision and recall in identifying the naval surface vessel despite data limitations. This zero-shot learning approach, validated through extensive testing, supports visual navigation and target identification in GPS/RF-denied environments, advancing autonomous maritime operations and aligning with the United States Navy strategy to leverage AI/ML for military enhancement.</p>

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Exploring deep neural networks for real-world ship detection using scaled model images and chroma key technology

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月24日

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  • Sean McCormick,
  • Evan Adams,
  • Adrien Richez,
  • Violet Mwaffo,
  • Donald Costello

摘要

This paper presents the development and evaluation of a deep neural network model for the detection of naval surface vessel using laboratory-generated datasets. By employing chroma-key technology, images of a scale model naval vessel were superimposed onto realistic maritime backgrounds to create a diverse training dataset. Fine-tuned with these datasets and evaluated using the YOLOv8 framework, the model achieved high precision and recall in identifying the naval surface vessel despite data limitations. This zero-shot learning approach, validated through extensive testing, supports visual navigation and target identification in GPS/RF-denied environments, advancing autonomous maritime operations and aligning with the United States Navy strategy to leverage AI/ML for military enhancement.