SBIR/STTR Research Award
Low-shot Algorithm for Mine Data Augmentation (LAMDA)
Award amount
$239,997Program and phase
SBIR · Phase IAward year
2024Award details
- Awarding agency
- Department of Defense
- Branch
- Navy
- Recipient company
- IN-DEPTH ENGINEERING CORPORATION
- Proposal award date
- 2024-09-09
- Contract end date
- 2025-11-12
- UEI
- GC7MKAN5DG35
- Solicitation number
- 24.1
- Topic code
- N241-025
Research abstract
Advanced Artificial Intelligence/Machine Learning Techniques for Automated Target Recognition (ATR) Using Small/Reduced Data Sets: Develop an innovative automatic target recognition (ATR) capability that leverages state-of-the-art Artificial Intelligence/Machine Learning (AI/ML) technology to support naval mine countermeasure (MCM) operations using reduced data sets.
Technology keywords
Not reported
Other awards by this recipient
Verified by the same UEI. These are other public SBIR/STTR award pages, not a merged company record.
- High-velocity Adaptive electronic Warfare Kit
- Navy Artificial Intelligence Logistics Support for Aegis (NAILS-A)
- Wideband High-frequency Aquatic Life Echolocation System, Deployable
- Mission Planner Adaptive Trainer (MPAT)
- Transformer with Reinforcement and Actor- Critic for Edge-vectorization (TRACE)
Awards with the same topic code
Verified by an exact topic code match. This is not a text-similarity match.
- Sonar Adapted Low-shot Model for Ordnance Neutralization
- AAI/MLT for ATR USing small/reduced Data sets
- MARINE MINED: FEW-SHOT LEARNING FOR UNDERSEA MINE COUNTERMEASURES (MCM)
- MineDL: Efficient Mine Detection and Localization Using Advanced YOLO and LSTM with Few-Shot Learning
Research funding history
50 SBIR/STTR records share this UEI (2011–2025). Reported award amounts total $35,435,692. Reported amounts are not federal obligations.
Phases: Phase II, Phase I
Agencies: Department of Defense