SBIR-STTR Award

Object Detection and Recognition in Degraded Visual Environment (DAR-DVE)
Award last edited on: 9/9/2023

Sponsored Program
SBIR
Awarding Agency
DOD : AF
Total Award Amount
$799,640
Award Phase
2
Solicitation Topic Code
AF211-CSO1
Principal Investigator
Besma R Abidi

Company Information

Phelps2020 Inc

301 South Gallaher View Road Suite #110
Knoxville, TN 37923
   (865) 705-5171
   info@phelps2020.com
   www.phelps2020.com
Location: Single
Congr. District: 02
County: Knox

Phase I

Contract Number: FA8649-21-P-1240
Start Date: 4/13/2021    Completed: 7/12/2021
Phase I year
2021
Phase I Amount
$49,956
Phelps2020 will customize our SOCOM developed SAM real-time Artificial Intelligence (AI) solution that performs accurate Electro-Optical Infra-Red (EO/IR) persistent surveillance and automatic detection of threats (people and vehicles) in the presence of atmosphere turbulence. Our solution performs automatic and concurrent mitigation of several environmental degradations, including low light, fog, haze, blur, high dynamic range, backlighting, low resolution, and atmospheric distortions, which were shown to highly improve the accuracy of threat detection and reduce false alarms. Neural networks-based Object detection (OD), the second component of DAR-DVE, has been traditionally used with EO sensors for lack of data in the IR. PhelpsÂ’ base product Crystal already extends EO-based OD to the majority of low light situations (dusk, dawn, and many nighttime situations). We will retrain our existing AI OD algorithms for Geospatial AI to detect new classes potentially including surface to air missile systems or other peer Integrated Air Defense Systems (IADS) in the visible and infrared.

Phase II

Contract Number: FA8649-22-P-0676
Start Date: 3/10/2022    Completed: 6/12/2023
Phase II year
2022
Phase II Amount
$749,684
Phelps2020 will adapt its USSOCOM developed SAM real-time Artificial Intelligence (AI) solution that performs accurate Electro-Optical persistent surveillance and automatic detection of threats (people and vehicles) in the presence of jitter and atmosphere turbulence. Our solution performs automatic and concurrent mitigation of several environmental degradations, including low light, fog, haze, blur, high dynamic range, backlighting, low resolution, and atmospheric distortions, which were shown to highly improve the accuracy of threat detection and reduce false alarms. Convolutional Neural networks-based Object detection (OD), the second component of DAR-DVE, has been traditionally used with EO sensors for lack of data in the IR. PhelpsÂ’ base product Crystal already extends EO-based OD to the majority of low light situations (dusk, dawn, and many nighttime situations). We will extend AI to the infrared bands for moonless nights, and retrain our existing AI OD algorithms for Geospatial AI to detect new classes, potentially including weapons, surface to air missile systems or other peer Integrated Air Defense Systems (IADS) in the visible and in the infrared, while associating the same objects in both bands for increased accuracy and improved intelligence.