Massachusetts Institute of Technology

Daniel J Scarafoni

Michael T Chan

Jason R Thornton

Ronald Duarte

Alexander C Bockman

Luke Skelly

Systems and methods are provided for selecting an optimized data model architecture subject to resource constraints. One or more resource constraints for target deployment are identified, and random model architectures are generated from a set of model architecture production rules subject to the one or more resource constraints. Each random model architecture is defined by randomly chosen values for one or more meta parameters and one or more layer parameters. One or more of the random model architectures are adaptively refined to improve performance relative to a metric, and the refined model architecture with the best performance relative to the metric is selected.

Systems and methods for optimization of a data model network architecture for target deployment

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.

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Cambridge, MA, United States of America

Daniel J Scarafoni