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The Award database is continually updated throughout the year. As a result, data for FY22 is not expected to be complete until September, 2023.

Download all SBIR.gov award data either with award abstracts (290MB) or without award abstracts (65MB). A data dictionary and additional information is located on the Data Resource Page. Files are refreshed monthly.

  1. Handheld Advanced Detection/Imaging TechNlogy System

    SBC: Luna Innovations Incorporated            Topic: DHS201006

    TeraMetrix is a wholly owned subsidiary of Luna InNvations Incorporated.Both TeraMetrix and Luna InNvations meet the SBIR eligibility requirements in size and Phase II conversion.We have attached Luna InNvations' registration SBC_000671230 because it includes ALL Luna employees and eliminates the question of does TeraMetrix as a subsidiary of Luna meet the size requirements. Luna as a whole, inclu ...

    SBIR Phase I 2020 Department of Homeland Security
  2. Learning traffic camera locations using vehicle re-identification

    SBC: Arete Associates            Topic: NGA201005

    In its effort to provide necessary intelligence and analysis, the National Geospatial-Intelligence Agency (NGA) utilizes extensive traffic camera systems. However, the large amount of data overwhelms both analysts and existing processing methods. In order to provide a better understanding and reduce the search space for common problems such as target tracking, it is necessary to extract the camera ...

    SBIR Phase I 2020 Department of DefenseNational Geospatial-Intelligence Agency
  3. Automating tilt and roll in ground-based photos and video frames

    SBC: INTERNATIONAL ASSOCIATION OF VIRTUAL ORGANIZATIONS, INCORPORATED            Topic: NGA201006

    NGA seeks an innovation to fully automate processes that recover camera orientation parameters, specifically for ground-based “photo” (aka image) and video frame use cases. The ability to use these ground-based systems represents an enhanced aspect to traditional photogrammetry, and in many regards, folding in hand-held systems, and considering the nuances associated with these collects, is ye ...

    SBIR Phase I 2020 Department of DefenseNational Geospatial-Intelligence Agency
  4. Hybrid DNN-based Transfer Learning and CNN-based Supervised Learning for Object Recognition in Multi-modal Infrared Imagery

    SBC: TOYON RESEARCH CORPORATION            Topic: 1

    On this effort Toyon Research Corp. and The Pennsylvania State University are developing deep learning-based algorithms for object recognition and new class discovery in look-down infrared (IR) imagery. Our approach involves the development of a hybrid classifier that exploits both transfer learning and semi-supervised paradigms in order to maintain good generalization accuracy, especially when li ...

    STTR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
  5. Algorithms for Look-down Infrared Target Exploitation

    SBC: Signature Research, Inc.            Topic: 1

    Signature Research, Inc. (SGR) and Michigan Technological University (MTU) propose a Phase I STTR effort to develop a learning algorithm which exploits the spatio-spectral characteristics inherent within IR imagery and motion imagery.Our archive of modelled and labeled data sets will allow our team to thoroughly capture the variable elements that will drive machine learning performance.The overall ...

    STTR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
  6. Bounding generalization risk for Deep Neural Networks

    SBC: Euler Scientific            Topic: NGA20A001

    Deep Neural Networks have become ubiquitous in the modern analysis of voluminous datasets with geometric symmetries. In the field of Particle Physics, experiments such as DUNE require the detection of particle signatures interacting within the detector, with analyses of over a billion 3D event images per channel each year; with typical setups containing over 150,000 different channels.  In an ...

    STTR Phase I 2020 Department of DefenseNational Geospatial-Intelligence Agency
  7. SHAPE-BASED GENERALIZATION BOUNDS FOR DEEP LEARNING

    SBC: GEOMETRIC DATA ANALYTICS            Topic: NGA20A001

    We propose to develop a theoretical understanding of the relationship between intrinsic geometric structure in both training and latent data and characteristics of functions learned from that data for deep neural network (DNN) architectures. Along the way we propose to also understand the structure of the neural networks that are best trained on a given data set. Both of these theories will lead t ...

    STTR Phase I 2020 Department of DefenseNational Geospatial-Intelligence Agency
  8. Variational Object Recognition and Grouping Network

    SBC: INTELLISENSE SYSTEMS INC            Topic: NGA181005

    To address the National Geospatial-Intelligence Agency (NGA) need for overhead imagery analysis algorithms that provide uncertaintymeasures for object recognition and aggregation, Intellisense Systems, Inc. (ISS) proposes to develop a new Variational Object Recognition andGrouping Network (VORGNet) system. It is based on the innovation of implementing a Bayesian convolutional neural network (CNN) ...

    SBIR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
  9. Bayesian Urban Degradation Assessment

    SBC: INTELLISENSE SYSTEMS INC            Topic: NGA181004

    To address the NGA need for algorithms that fuse observables from over-flight operations and from ground sources to automatically estimatethe degradation of urban environments due to battle damage or natural disasters, Intellisense Systems, Inc. (ISS) proposes to develop a newBayesian Urban Degradation Assessment (BUDA) software system. It is based on the integration of multiple damage assessment ...

    SBIR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
  10. Blockchain-based Anti-Spoofing and Integrity Protection

    SBC: INTELLISENSE SYSTEMS INC            Topic: DHS201002

    To address the DHS need for new remote sensor data protection and anti-spoofing techniques, Intellisense Systems, Inc. proposes to develop a new Blockchain-based Anti-Spoofing and Integrity Protection (BASIP) system. This proposed BASIP is based on redactable blockchain-based data protection and challenge-response-based spoof detection. The BASIP will offer high resilience to sensor spoofing and m ...

    SBIR Phase I 2020 Department of Homeland Security
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