Automatic detection and recognition of humans with YOLO-based deep learning using MuSES-generated EO/IR synthetic imagery

Mark D. Klein; Zachary J. Edel; Corey D. Packard; Jacob N. Hendrickson; Audrey Levanen; Peter L. Rynes

doi:10.1117/12.3013911

7 June 2024 Automatic detection and recognition of humans with YOLO-based deep learning using MuSES-generated EO/IR synthetic imagery

Mark D. Klein, Zachary J. Edel, Corey D. Packard, Jacob N. Hendrickson, Audrey Levanen, Peter L. Rynes

Author Affiliations +

Proceedings Volume 13035, Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications II; 130350B (2024) https://doi.org/10.1117/12.3013911
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

Machine learning algorithms are capable of processing image-based scenes, detecting and recognizing embedded targets. This has been demonstrated by data scientists and computer vision engineers, but performant algorithms must be robustly trained to successfully complete such a complex task. This typically requires a large set of training data on which the algorithm can base statistical predictions. Electro-optical infrared (EO/IR) remote sensing applications necessitate a substantial image database with suitable variation for adept learning to occur. For human detection/recognition applications diversity in clothing ensembles, pose, season, times of day, sensor platform perspectives, scene backgrounds and weather conditions can be included in training image sets to ensure sufficient input variety. However, acquiring such a diverse image set from measured sources can be a challenge, especially in thermal infrared wavebands (e.g., MWIR and LWIR). Alternatively, generating synthetic imagery with appropriate features is possible and has been shown to perform well, but a careful methodology must be followed if robust training is to be accomplished. In this work, MuSES and CoTherm are used to generate synthetic EO/IR remote sensing imagery of various human dismounts with a range of clothing, poses and environmental factors. The performance of a YOLO (“you only look once”) deep learning algorithm is studied, and sensitivity conclusions are discussed.

Conference Presentation

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

Citation Download Citation

Mark D. Klein, Zachary J. Edel, Corey D. Packard, Jacob N. Hendrickson, Audrey Levanen, and Peter L. Rynes "Automatic detection and recognition of humans with YOLO-based deep learning using MuSES-generated EO/IR synthetic imagery", Proc. SPIE 13035, Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications II, 130350B (7 June 2024); https://doi.org/10.1117/12.3013911

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available