Methods for Detecting Objects-of-Interest in Underwater Environments

Surveillance systems and methods taught herein provide automated detection and classification of objects of interest in a submerged or underwater environment such as a body of water. The sonar systems and methods taught herein can detect and classify a variety of objects in echograms without feedback or instructions from a human operator. The sonar systems and methods taught herein include a data model that is partially trained using non-echogram image data and can associate geolocation information with detected objects of interest.

Researchers

Michael Chan / Daniel Scarafoni / Alexander Bockman

Departments: Lincoln Laboratory
Technology Areas: Artificial Intelligence (AI) and Machine Learning (ML) / Sensing & Imaging: Acoustics, Imaging

  • systems and methods for detecting objects in underwater environments
    United States of America | Granted | 10,809,376

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