Deep Learning Platform for High Performance Machine Learning for Automatic Target Detection and Recognition

Abstract

Major Goals: This project funded the acquisition of a Deep Learning Platform for Big Data (DLP-BD) with high performance data storage, networking, and processing capabilities to enable machine learning and specifically Deep Learning for ATR. The acquisition responds to the need for an in-house instrument enabling permanent storage of sensitive data that are in the tens of terabyte-range, and providing efficient big data processing through a non-volatile memory enabled hierarchical storage system, high speed networking interconnects, and multiple processors in parallel. Accomplishments: During the past year, the acquired Deep Learning Platform for Big Data (DLP-BD) has proved crucial to our DoD project that involved developing algorithms for the detection, recognition, and tracking of targets (vehicles and dismounts) using InfraRed (IR) imagery. The main objective of this project is to support the US Army Advanced Targeting and Lethality Automated System , ATLAS, program. ATLAS is a joint program, between the US Army C5ISR center and the Armament center. It aims to develop autonomous target acquisition technology, that will be integrated with fire control technology, aimed at providing ground combat vehicles with the capability to acquire, identify, and engage targets at least 3X faster than the current manual process. Our work in this area is in collaboration with the Army's Night Vision and Electronic Sensors Directorate, and more specifically the ATLAS team. The acquired DLP-BD have enabled local storage of large ITAR data and the adaptation, training, optimization, and analysis of various Deep Network architectures. Consequently, we have developed robust target detection and identification algorithms that continue to have excellent performance in various blind testing experiments. The acquired DLP-BD is also currently being used to develop, train, and analyze a deep learning algorithm for door detection in an indoor setting.

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Document Details

Document Type
Technical Report
Publication Date
Jul 31, 2021
Accession Number
AD1199850

Entities

People

  • Hichem Frigui

Organizations

  • University of Louisville

Tags

Communities of Interest

  • Autonomy
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Acquisition
  • Algorithms
  • Artificial Intelligence Software
  • Big Data
  • Combat Vehicles
  • Control Systems
  • Convolutional Neural Networks
  • Data Processing
  • Data Storage Systems
  • Deep Learning
  • Detection
  • Machine Learning
  • Neural Networks
  • Recognition
  • Recurrent Neural Networks
  • Students
  • Target Acquisition
  • Target Detection

Readers

  • Neural Network Machine Learning.
  • Research Science/Academic Research
  • Sensor Fusion and Tracking Systems.

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy
  • AI & ML - Neural Networks
  • Microelectronics