Operationally Relevant Artificial Training for Machine Learning: Improving the Performance of Automated Target Recognition Systems

Abstract

We set out to demonstrate that an automated target recognition (ATR) system could be built using nothing but commercial off-the-shelf artificial intelligence/machine learning (AI/ML) algorithms and artificially rendered imagery to train thembut we did not succeed. What the AI/ML algorithms learned about detecting artificial objects in artificial images failed to transfer meaningfully toward the task of detecting real objects in real images. However, to our surprise, we discovered that a hybrid training set consisting of both real and artificial images together produced a more robust ATR system than a training set of real images alone. In other words, we were able to boost the performance of the ATR system consistently by adding artificially generated images of the target to the original training set.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2020
Accession Number
AD1115673

Entities

People

  • Damien Baveye
  • Dara Gold
  • Gavin S. Hartnett
  • Jasmin Leveille
  • Jeff Hagen
  • Jia Xu
  • Lance Menthe
  • Li A. Zhang

Organizations

  • RAND Corporation

Tags

Communities of Interest

  • Autonomy
  • Cyber
  • Human Systems
  • Weapons Technologies

DTIC Thesaurus Topics

  • Air Force
  • Artificial Intelligence
  • Artificial Intelligence Software
  • Automata Theory
  • Automated Target Recognition
  • Computational Science
  • Computer Vision
  • Computers
  • Drone Targeting
  • Information Systems
  • Machine Learning
  • Neural Networks
  • Pattern Recognition
  • Target Recognition
  • Three Dimensional
  • Video Games
  • Warfare

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Sensor Fusion and Tracking Systems.

Technology Areas

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