Robust Computer Vision for Better Object Detection with Limited Training Data - Topic A244-033

The Robust Computer Vision for Better Object Detection with Limited Training Data (Topic A244-033) is a grant from the Department of Defense Army SBIR program that funds innovative AI and machine learning research for automated object identification in multi-modal imagery data.

This solicitation seeks solutions for object detection in electro-optical/infrared, synthetic aperture radar, and full-motion video imagery that do not rely on extensive labeled training datasets. Applications are accepted as Direct to Phase II proposals only, with contracts worth up to $2,000,000 over an 18-month performance period.

Eligible applicants are small businesses that can substantiate scientific and technical merit equivalent to a Phase I project. The application deadline was July 30, 2024.

Robust Computer Vision for Better Object Detection with Limited Training Data – Army SBIR|STTR Program Artificial Intelligence/Machine Learning, Army SBIR, Direct to Phase II Robust Computer Vision for Better Object Detection with Limited Training Data Application Due Date: 07/30/2024 Duration: Up to 18 months Language Computer Corporation Boston Fusion Corporation CoVar Applied Technologies Inc. The U.S. Army seeks to experiment with innovative artificial intelligence and machine learning approaches for object identification and imagery scene analysis.

With the increasing availability of digital imagery, including satellite data for electro-optical/infrared, synthetic aperture radar and full-motion video, there is a growing need for automated methods to efficiently process and analyze vast amounts of multi-modal data. One critical application is the identification of objects of interest within imagery data or the scene generated by the imagery.

This can provide valuable insights and facilitate decision-making processes in various fields such as military intelligence, environmental monitoring, transportation management and security surveillance. The Army will only accept Direct to Phase II proposals for contracts worth up to $2,000,000 over an 18-month performance period.

Proposers interested in submitting a DP2 proposal must provide documentation to substantiate that the technology meets the scientific, technical merit and feasibility equivalent to a Phase I project. Documentation can include data, reports, specific measurements and the success criteria of a prototype.

This SBIR solicitation will explore robust AI/ML object detection techniques for computer vision that do not rely on the extensive availability of labeled training data. Foundational knowledge and methods already exist, making feasibility studies unnecessary.

Computer vision algorithms using handcrafted mathematical features, which include edge detection and scale-invariant feature transform, are still effective for certain tasks while offering faster run times.

Evolutionary algorithms, such as Neuroevolutionary of Augmenting Topologies , can optimize the parameters of a computer vision system and combine with other methods such as handcrafted features and various neural networks architectures. These help to form hybrid approaches with less dependence on extensive training data.

Newer techniques based on transformers, and referred to as foundational models, have shown extraordinary ability to generalize to new tasks without requiring use case specific training data. All these computer vision technologies can function within academic and industrial settings, even reaching sufficient maturity for deployment in commercial products such as level-two self-driving cars and vision language models like Google’s Gemini.

Vendors can leverage these foundational technologies for the SBIR solicitation and adapt them for Department of Defense and Army use cases without requiring a feasibility study. During the DP2, firms should develop and implement novel or hybrid AI/ML models for object detection that do not rely on extensive training data.

Vendors should also develop training models in Project Linchpin’s AI Unclassified Operations Environment using Linchpin data for DoD use cases. Autonomy: Detecting objects and obstacles for self-driving cars, robots and drone delivery initiatives . Retail: Analyze shopping behavior in store to gain insights into product interactions and contactless checkout .

Public safety: Detection of unauthorized objects or individuals in manufacturing, logistics and construction sectors . Traffic management: Monitor roads to optimize traffic flow and reduce congestion . Enhanced security: Improving security systems for access control and surveillance purposes.

Agriculture: Computer vision can help prediction and plant monitoring to detect diseases. Computer vision solutions in the private sector encompass a wide range of applications, from object detection and recognition to healthcare and agriculture. Companies such as Amazon, Google and Microsoft offer cloud-based object detection and recognition services.

Meanwhile Face++, Kairos and NEC provide facial recognition solutions. Additionally, companies like IBM, Cisco, and Huawei offer video analytics solutions while ABB, Kuka, and FANUC provide vision-guided robotics and automation solutions . All eligible businesses must submit proposals by noon, ET.

To view the full solicitation details, click here . For more information, and to submit your full proposal package, visit the DSIP Portal . Applied SBIR Help Desk: usarmy.

pentagon. hqda-asa-alt. mbx.

army-applied-sbir-program@army. mil B. Amjoud and M.

Amrouch, “Object Detection Using Deep Learning, CNNs and Vision Transformers: A Review,” in IEEE Access, vol. 11, pp. 35479-35516, 2023, doi: 10.

1109/ACCESS. 2023. 3266093.

L. Jiao et al. , “New Generation Deep Learning for Video Object Detection: A Survey,” in IEEE Transactions on Neural Networks and Learning Systems, vol.

33, no. 8, pp. 3195-3215, Aug. 2022, doi: 10.

1109/TNNLS. 2021. 3053249.

Y. Bi, B. Xue, P.

Mesejo, S. Cagnoni and M. Zhang, “A Survey on Evolutionary Computation for Computer Vision and Image Analysis: Past, Present, and Future Trends,” in IEEE Transactions on Evolutionary Computation, vol.

27, no. 1. Digital Imagery; Objects of Interest; Sensor Data; AI/ML; Scale-Invariant Feature Transform; Neuroevolutionary of Augmenting Topologies Language Computer Corporation Boston Fusion Corporation CoVar Applied Technologies Inc. The U.S. Army seeks to experiment with innovative artificial intelligence and machine learning approaches for object identification and imagery scene analysis.

With the increasing availability of digital imagery, including satellite data for electro-optical/infrared, synthetic aperture radar and full-motion video, there is a growing need for automated methods to efficiently process and analyze vast amounts of multi-modal data. One critical application is the identification of objects of interest within imagery data or the scene generated by the imagery.

This can provide valuable insights and facilitate decision-making processes in various fields such as military intelligence, environmental monitoring, transportation management and security surveillance. The Army will only accept Direct to Phase II proposals for contracts worth up to $2,000,000 over an 18-month performance period.

Proposers interested in submitting a DP2 proposal must provide documentation to substantiate that the technology meets the scientific, technical merit and feasibility equivalent to a Phase I project. Documentation can include data, reports, specific measurements and the success criteria of a prototype.

This SBIR solicitation will explore robust AI/ML object detection techniques for computer vision that do not rely on the extensive availability of labeled training data. Foundational knowledge and methods already exist, making feasibility studies unnecessary.

Computer vision algorithms using handcrafted mathematical features, which include edge detection and scale-invariant feature transform, are still effective for certain tasks while offering faster run times.

Evolutionary algorithms, such as Neuroevolutionary of Augmenting Topologies , can optimize the parameters of a computer vision system and combine with other methods such as handcrafted features and various neural networks architectures. These help to form hybrid approaches with less dependence on extensive training data.

Newer techniques based on transformers, and referred to as foundational models, have shown extraordinary ability to generalize to new tasks without requiring use case specific training data. All these computer vision technologies can function within academic and industrial settings, even reaching sufficient maturity for deployment in commercial products such as level-two self-driving cars and vision language models like Google’s Gemini.

Vendors can leverage these foundational technologies for the SBIR solicitation and adapt them for Department of Defense and Army use cases without requiring a feasibility study. During the DP2, firms should develop and implement novel or hybrid AI/ML models for object detection that do not rely on extensive training data.

Vendors should also develop training models in Project Linchpin’s AI Unclassified Operations Environment using Linchpin data for DoD use cases. Autonomy: Detecting objects and obstacles for self-driving cars, robots and drone delivery initiatives . Retail: Analyze shopping behavior in store to gain insights into product interactions and contactless checkout .

Public safety: Detection of unauthorized objects or individuals in manufacturing, logistics and construction sectors . Traffic management: Monitor roads to optimize traffic flow and reduce congestion . Enhanced security: Improving security systems for access control and surveillance purposes.

Agriculture: Computer vision can help prediction and plant monitoring to detect diseases. Computer vision solutions in the private sector encompass a wide range of applications, from object detection and recognition to healthcare and agriculture. Companies such as Amazon, Google and Microsoft offer cloud-based object detection and recognition services.

Meanwhile Face++, Kairos and NEC provide facial recognition solutions. Additionally, companies like IBM, Cisco, and Huawei offer video analytics solutions while ABB, Kuka, and FANUC provide vision-guided robotics and automation solutions . All eligible businesses must submit proposals by noon, ET.

To view the full solicitation details, click here . For more information, and to submit your full proposal package, visit the DSIP Portal . Applied SBIR Help Desk: usarmy.

pentagon. hqda-asa-alt. mbx.

army-applied-sbir-program@army. mil B. Amjoud and M.

Amrouch, “Object Detection Using Deep Learning, CNNs and Vision Transformers: A Review,” in IEEE Access, vol. 11, pp. 35479-35516, 2023, doi: 10.

1109/ACCESS. 2023. 3266093.

L. Jiao et al. , “New Generation Deep Learning for Video Object Detection: A Survey,” in IEEE Transactions on Neural Networks and Learning Systems, vol.

33, no. 8, pp. 3195-3215, Aug. 2022, doi: 10.

1109/TNNLS. 2021. 3053249.

Y. Bi, B. Xue, P.

Mesejo, S. Cagnoni and M. Zhang, “A Survey on Evolutionary Computation for Computer Vision and Image Analysis: Past, Present, and Future Trends,” in IEEE Transactions on Evolutionary Computation, vol.

27, no. 1. Digital Imagery; Objects of Interest; Sensor Data; AI/ML; Scale-Invariant Feature Transform; Neuroevolutionary of Augmenting Topologies Assistant Secretary of the Army for Acquisition, Logistics, and Technology ASA(ALT) releases contract opportunities on an ad-hoc basis to meet Army research and development needs.

Army Futures Command (AFC) releases topics during three specific solicitation periods throughout the fiscal year to address the Army’s current and anticipated war-fighting technology needs. Army STTR follows AFC’s topic release schedule but partners with a university, federally funded research and development center, or a qualified non-profit research institution as part of their contract.

Is the opportunity to establish the scientific, technical, commercial merit and feasibility of your proposed innovation. Is focused on the development, demonstration and delivery of your innovation from Phase I. Represents the commercialization phase of the program in which the company can market their products or services developed in Phase II, either to the government or in the commercial sector.

Allows small businesses to submit to Direct to Phase II applications if they performed the Phase I research through other funding sources. Provides funding to projects that require additional funding during their open Phase II contract. A Phase II Awardee may receive one additional, sequential Phase II award to continue the work of an initial Phase II award.

The sequential Phase II award has the same guideline amounts and limits as an initial Phase II award.

Artificial Intelligence/Machine Learning (supply chain management, logistics coordination, target identifications and simulation) Advanced Materials and Manufacturing (additive manufacturing) Autonomy (unmanned systems, drones, ground vehicle capabilities) Chemical and Biological (detection, defense) Cyber (biometric authentication, secure communications) Electronics (microelectronics, Very-Large-Scale Integration (VLSI)) Electronic Warfare (jamming, spoofing) Human Performance (wearables) Immersive (augmented reality, virtual reality, mixed reality) Network Technologies (antennas, radio frequency, communications systems) Position, Navigation, and Timing (GPS) Power (batteries, generators) Software Modernization (high performance computing, data management and visualization) Sensors (infrared sensing) Weapons Systems (hypersonics, munitions and projectiles, directed energy)