Synthetically generated image dataset for military relevant machine learning experiments

Steven D. Vanstone

doi:10.1117/12.2667569

13 June 2023 Synthetically generated image dataset for military relevant machine learning experiments

Steven D. Vanstone

Proceedings Volume 12521, Automatic Target Recognition XXXIII; 125210F (2023) https://doi.org/10.1117/12.2667569
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

The desertSim detection dataset consists of more than forty-seven thousand synthetically generated infrared images exhibiting unique characteristics not found in academic datasets typically used for machine learning research. The desertSim set of images provides realistic infrared signatures of armored vehicles under a variety of configurations, engine states, time of day, and clutter conditions. The dataset is publicly available and was created to provide academic researchers a military relevant dataset to support machine learning research. The synthetic infrared image dataset can be used in conjunction with a publicly available real infrared image dataset for experiments having a synthetic data training set and real data test set. Consistency in the nature of the two datasets make them particularly suitable for conducting academic experiments in support of machine learning research.

Conference Presentation

Citation Download Citation

Steven D. Vanstone "Synthetically generated image dataset for military relevant machine learning experiments", Proc. SPIE 12521, Automatic Target Recognition XXXIII, 125210F (13 June 2023); https://doi.org/10.1117/12.2667569

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