Infrared collects of scale models for automatic target recognition

Jacob Ross; Rajith Weerasinghe; Justin Lastrapes; Ryan Shaver; Paul Sotirelis

doi:10.1117/12.3013548

7 June 2024 Infrared collects of scale models for automatic target recognition

Jacob Ross, Rajith Weerasinghe, Justin Lastrapes, Ryan Shaver, Paul Sotirelis

Proceedings Volume 13039, Automatic Target Recognition XXXIV; 130390O (2024) https://doi.org/10.1117/12.3013548
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

Traditional data collects of high priority targets require immense planning and resources. When novel operating conditions (OCs) or imaging parameters need to be explored, typically synthetic simulations are leveraged. While synthetic data can be used to assess automatic target recognitions (ATR) algorithms; some simulation environments may inaccurately represent sensor phenomenology. To levitate this issue, a scale model approach is utilized to provide accurate data in a laboratory setting. This work demonstrates the effectiveness of a resource cognizant approach for collecting IR imagery suitable to assessing ATR algorithms. A target of is interest is 3D printed at 1/60th scale with a commercial printer and readily available materials. The printed models are imaged with a commercially available IR camera in a simple laboratory setup. The collected imagery is used to test ATR algorithms when trained on a standard IR ATR dataset; the publicly available ARL Comanche FLIR dataset. The performance of the selected ATR algorithms when given sampled of scale model data is compared to the performance of the same algorithms when using the provided measured data.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jacob Ross, Rajith Weerasinghe, Justin Lastrapes, Ryan Shaver, and Paul Sotirelis "Infrared collects of scale models for automatic target recognition", Proc. SPIE 13039, Automatic Target Recognition XXXIV, 130390O (7 June 2024); https://doi.org/10.1117/12.3013548

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