DCL System Using Deep Learning Approaches for Land-based or Ship-based Real-time Recognition and Localization of Marine Mammals

Lead PI: Drs. Peter Dugan & Christopher Clark, Cornell University
Start Year: 2011 | Duration: 3 years
Partners: New York University, Cornell University, Pacific Northwest National Laboratory, Lockheed Martin, & Hydroscience Technologies, Inc.

The primary goals of the proposed collaborative project are to: (1) to apply and study advanced new approaches in machine learning (i.e., the convolution neural net technology, ConvNet) to bring detection and classification of marine mammals to an entirely new level of performance, (2) leverage past experiences and advanced computing to develop enhanced real-time algorithms for locating marine mammals, (3) overcome the limitations of current state-of-the-art technologies by applying, evaluating and studying a systems approach that includes the integration of the ConvNet technology with Cornell’s acoustic array processing system, Minatour, and converts these into an open and extensible real-time DCL system that can be used in a variety of environments, and (4) demonstrate the accuracy and precision of the DCL system by merging this system with a state-of-the-art towed beamforming array.



Annual PI Reports:
FY 2011 PI Report
FY 2012 PI Report
FY 2013 PI Report
FY 2014 PI Report
FY 2015 PI Report