Wicker scale model lab: rapid generation of ATR training data from 2 years to 3 days

Rachel Kinard; Igor Ternovskiy; Robert Schueler; Brandon Kinard; James Graham; Matthew Rustad

doi:10.1117/12.3014722

10 June 2024 Wicker scale model lab: rapid generation of ATR training data from 2 years to 3 days

Rachel Kinard, Igor Ternovskiy, Robert Schueler, Brandon Kinard, James Graham, Matthew Rustad

Proceedings Volume 13035, Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications II; 1303506 (2024) https://doi.org/10.1117/12.3014722
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

In Machine Learning (ML) based autonomous technology research (ATR), it is crucial to have large and reliable data sets to train deep learning-based classifiers and implement object detection methods. For air-to-ground ATR, the gold standard, obtained by limited and expensive controlled field collections, is measured data. However, carefully curated research data intended to test or isolate specific qualities of object detection (low-light, heavy shadow, cloud cover, obscurations, and other operational use cases) is still difficult to obtain. For advanced research problems, synthetic data generated in simulated environments meets both quantity and quality requirements. Most synthetic data is generated in a software simulated environment using various rendering techniques, limited by available computational resources. Among the many types of synthetic data is scale model data, generated by 3D printing and imaging the same 3D Computer-Aided Design (CAD) models at a reduced scale (1:285 or 1:125) on a turntable in controlled environmental conditions. We present a workflow for the rapid generation of ATR Training Data customized to isolate and identify features of interest in advanced research problems. Publicly accessible data is available upon request to lead author.

Conference Presentation

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

Citation Download Citation

Rachel Kinard, Igor Ternovskiy, Robert Schueler, Brandon Kinard, James Graham, and Matthew Rustad "Wicker scale model lab: rapid generation of ATR training data from 2 years to 3 days", Proc. SPIE 13035, Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications II, 1303506 (10 June 2024); https://doi.org/10.1117/12.3014722

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