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Photorealistic Learned Landscapes for Augmented Reality
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An Augmented Reality Periscope for Submarines with Extended Visual Classification.

André Breitinger1, Esteban Clua1, Leandro A F Fernandes1

  • 1Instituto de Computação, Universidade Federal Fluminense (UFF), Av. Gal. Milton Tavares de Souza, Niterói 24210-346, RJ, Brazil.

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Summary
This summary is machine-generated.

This study introduces a new submarine periscope system using a 360 camera and mixed reality (MR) for enhanced vessel detection. The novel approach reduces submarine exposure by allowing operations from deeper positions.

Keywords:
computer visiondeep learningmixed realityobject detectionperiscopesubmarinesynthetic datatransfer learning

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Area of Science:

  • Naval Technology
  • Computer Vision
  • Human-Computer Interaction

Background:

  • Submarine periscopes are critical for visual identification but require surfacing, increasing exposure risk.
  • Current periscope operations demand speed and agility to minimize detection during visual contact acquisition.
  • Future naval operations require advanced sensor systems for enhanced situational awareness and reduced vulnerability.

Purpose of the Study:

  • To develop and implement a novel periscope architecture for future Brazilian naval fleet submarines.
  • To enhance submarine operational capabilities by reducing exposure during visual identification.
  • To integrate advanced computer vision and mixed reality for precise vessel detection and distance estimation.

Main Methods:

  • A novel periscope system with a 360 camera probe connected to the submarine.
  • Projection of imagery inside the submarine using virtual reality/extended reality (VR/XR) devices.
  • Implementation of a computer vision-based mixed reality (MR) technique for vessel detection and distance estimation.
  • Training a vessel detection model on a custom dataset of 99,000 synthetic images.
  • Validation through a user experience experiment with 19 periscope operation experts.

Main Results:

  • Successful implementation of a novel submarine periscope system.
  • Development and validation of an efficient computer vision-based MR technique for vessel detection.
  • Creation and release of a synthetic image dataset for training vessel detection models.
  • Demonstration of reduced submarine exposure during periscope operations.
  • Positive user experience feedback from periscope operation experts.

Conclusions:

  • The proposed wired periscope system enhances submarine stealth and operational flexibility.
  • The integrated MR and AR interface provides effective and precise vessel detection and distance estimation.
  • This novel architecture represents a significant advancement for future submarine periscope technology and naval operations.