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Incremental Online Learning for Target Recognition

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-23-C-0648
Agency Tracking Number: N231-035-1217
Amount: $139,837.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: N231-035
Solicitation Number: 23.1
Timeline
Solicitation Year: 2023
Award Year: 2023
Award Start Date (Proposal Award Date): 2023-08-07
Award End Date (Contract End Date): 2024-02-05
Small Business Information
41-305 Kalanianaole Hwy
Waimanalo, HI 96795-1111
United States
DUNS: 066271768
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Greg Rocheleau
 (808) 259-8871
 greg.rocheleau@makai.com
Business Contact
 Michael Nedbal
Phone: (808) 259-8871
Email: michael.nedbal@makai.com
Research Institution
N/A
Abstract

Undersea target recognition from unmanned underwater vehicles (UUVs) plays a critical role in the US Naval strategies and mission capabilities. The current Automated Target Recognition (ATR) solutions remain limited to homogenous and similarly non-complex seabed environments. ATR in cluttered environments poses significant challenges, with automated solutions resulting in too many false alarms. Makai will develop a continuous learning model and framework using convolution neural network models to provide ATR with pixel-level classification. As with any statistics-based learning model, machine learning performance is best when used on targets with characteristics to the training data. Future implementation requires continuous improvement and tuning as additional datasets become available over time. Makai will utilize the convolution neural networks with online machine learning to provide a step-change improvement in automated target recognition, producing higher performance and lower Probability of False Alarm in complex and cluttered environments; maintaining processing applicable for both onboard and post-mission analysis.

* Information listed above is at the time of submission. *

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