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Neural Net Based Machine Learning Algorithms for Automated NDT of Military and Other Hardware

Award Information
Agency: Department of Defense
Branch: Army
Contract: W15QKN-20-P-0049
Agency Tracking Number: A192-126-0035
Amount: $109,910.40
Phase: Phase I
Program: SBIR
Solicitation Topic Code: A19-126
Solicitation Number: 19.2
Solicitation Year: 2019
Award Year: 2020
Award Start Date (Proposal Award Date): 2019-10-30
Award End Date (Contract End Date): 2020-11-09
Small Business Information
2800 Shirlington Road Suite 801
Arlington, VA 22206
United States
DUNS: 080912968
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Amit Mehra
 Managing Partner
 (202) 213-2846
Business Contact
 Amit Mehra
Phone: (202) 213-2846
Research Institution

NOVI proposes to develop a prototype system capable of automating NDT of munitions using deep learning techniques. The system will be designed to process a stack of radiography scans for a 6.5” diameter, 36” tall munition with 0.002” – 0.020” defects in under one minute, and largely utilize Government-provided images (appropriately annotated) to create the necessary training data set. Specific activities will include: 1. Developing a unique set of training data with labeled defects in radiographic scans of munitions; 2. Customizing and training a LinkNet Convolutional Neural Net (CNN) model for the defect detection task; 3. Developing a framework for assembling 2D results into a volumetric 3D output for end-user analysis; 4. Testing the trained model on out-of-sample scans, and providing qualitative and quantitative performance analysis for detection accuracy and speed; and 5. Providing recommendations for continued enhancement of the approach, along with a preliminary plan for integration into production-class radiography inspection systems.

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

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