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The Award database is continually updated throughout the year. As a result, data for FY20 is not expected to be complete until September, 2021.

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. Fusion of VMS and Radiation Sensor Data for Real-Time Identification and Tracking

    SBC: Silverside Detectors Inc.            Topic: HSB0181010

    Traditionally, implementation of dedicated, continuous nuclear and radiological monitoring systems for identifying and tracking nuclear materials out of regulatory control is associated with high operational burdens. Although video and images from cameras are helpful in assessing threats, the raw, unstructured data require time-intensive human interaction to extract the relevant actionable informa ...

    SBIR Phase I 2018 Department of Homeland Security
  2. Tracking Nuclear Threats in Security Camera Networks (TNT-SCAN)

    SBC: Charles River Analytics, Inc.            Topic: HSB0181010

    The implementation of continuous nuclear and radiological monitoring systems enabling the automatic detection and tracking of potential nuclear threats is traditionally associated with a high operational burden. Sensors typically have to be monitored by dedicated personnel, who must investigate detection events in a timely manner; however, high nuisance alarm rates can rapidly overwhelm already-ta ...

    SBIR Phase I 2018 Department of Homeland Security
  3. Flexible, High-Frequency, High-Durability, and Multifunctional Sensor Film

    SBC: NEWPORT SENSORS INC            Topic: 18OATS001

    DHS has an unmet need for measuring ultra-high-intensity, near-field blast overpressure caused by internal blast within a commercial aircraft explosive threat environment in order to facilitate characterization of blast effects to commercial aircraft structures.The goal of this DHS SBIR OATS project is to deliver advanced thin, flexible, conformable, multi-sensing-point film sensor technology for ...

    SBIR Phase II 2018 Department of Homeland Security
  4. Generalized Change Detection to Cue Regions of Interest

    SBC: FeatureX, Inc.            Topic: NGA181006

    Generalized change detection is a critical capability to mitigate the need for massive human inspection of the rapidly expanding volume ofglobal overhead satellite imagery. Current optical change detection approaches focus on fully specified systems to detect a predefined set ofchanges, and effective approaches for generalized change detection have not yet been demonstrated. We propose to build a ...

    SBIR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
  5. PCS-Enabled Unattended Radiation Detection and Attribution System

    SBC: Physical Sciences Inc.            Topic: HSB0171009

    Physical Sciences Inc. (PSI) proposes to develop a PCS-Enabled Unattended Radiation Detection and Attribution System (PURDAS) that will be able to detect, identify, and attribute radiological sources to specific source carriers or conveyances. The PURDAS will include a COTS gamma and neutron detection capability as well as a visible camera, onboard processing, and wireless radios. PURDAS units wil ...

    SBIR Phase II 2018 Department of Homeland SecurityDomestic Nuclear Detection Office SBIR Program
  6. 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
  7. Low-Shot Detection in Remote Sensing Imagery

    SBC: TOYON RESEARCH CORPORATION            Topic: NGA181010

    The National Geospatial-Intelligence Agency (NGA) ingests and analyzes raw imagery from multiple sources to form actionable intelligenceproducts that can be disseminated across the intelligence community (IC). To effectively meet these demands NGA must continue to improveits automated and semi-automated methods for target detection and classification. Of particular concern is furthering NGA's abil ...

    SBIR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
  8. 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
  9. Generalized Change Detection to Cue Regions of Interest

    SBC: TOYON RESEARCH CORPORATION            Topic: NGA181006

    Toyon Research Corporation proposes to research and develop algorithms for generalized change detection, by leveraging and exploringexisting and proven effective traditional and deep learning methods, with a unique 3D reconstruction component. The vast majority of themassive amounts of imagery data will have small pixel level differences due to a multitude of unimportant changes: minor misregistra ...

    SBIR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
  10. 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
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