Generating synthetic IR imagery to train modern deep learning detection and recognition algorithms to perform on similar real IR data

Jamie C. Perez; Steven D. Vanstone; Santiago Andres Acevedo; Karina Narotam

doi:10.1117/12.2663879

13 June 2023 Generating synthetic IR imagery to train modern deep learning detection and recognition algorithms to perform on similar real IR data

Jamie C. Perez, Steven D. Vanstone, Santiago Andres Acevedo, Karina Narotam

Author Affiliations +

Proceedings Volume 12529, Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications; 1252903 (2023) https://doi.org/10.1117/12.2663879
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

Deep learning neural networks require large amounts of data to properly train for detection and recognition. Adequate infrared training data is not available especially for military specific applications. This paper describes the Collaborative Research and Development Agreement (CRADA) between U.S. Army Combat Capabilities Development Command Aviation & Missile Center (DEVCOM AvMC) and Lockheed Martin Missiles and Fire Control (LM-MFC) effected since early 2020. The purpose is to collaboratively research and develop a practical understanding of the value in using synthetically generated IR imagery for training modern deep learning algorithms for military applications to perform on field-collected real IR data. This paper details the effort to generate synthetic IR data for algorithm training, the networks and real testing data used for evaluation, and the detection and recognition results achieved. Particular attention was given to creating a set of synthetic IR images having some degree of similarity with a set of real sensor images without purposely trying to synthesize the real sensor images. To that end, target and background models derived or selected based on general thermal conditions present during the real sensor data collections and an empirically derived sensor model was used when creating the synthetic IR images. The synthetic IR image set has been approved for public release and the real IR image set is part of the Defense Systems Information Analysis Center (DSIAC) Automatic Target Recognition Algorithm Development Image Database (ATR ADID).

Conference Presentation

Citation Download Citation

Jamie C. Perez, Steven D. Vanstone, Santiago Andres Acevedo, and Karina Narotam "Generating synthetic IR imagery to train modern deep learning detection and recognition algorithms to perform on similar real IR data", Proc. SPIE 12529, Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications, 1252903 (13 June 2023); https://doi.org/10.1117/12.2663879

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