Evaluation of the required optical resolution for deep learning-based long-range UAV detection

Denis Ojdanić; Niklas Paternoster; Christopher Naverschnigg; Andreas Sinn; Georg Schitter

doi:10.1117/12.3013251

7 June 2024 Evaluation of the required optical resolution for deep learning-based long-range UAV detection

Denis Ojdanić, Niklas Paternoster, Christopher Naverschnigg, Andreas Sinn, Georg Schitter

Proceedings Volume 13040, Pattern Recognition and Prediction XXXV; 130400A (2024) https://doi.org/10.1117/12.3013251
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

This paper evaluates the required resolution of a telescope system experimentally to enable a reliable deep learning-based long-range UAV detection. FRCNN, a state-of-the-art deep learning object detector is fine-tuned for UAV detection with a custom dataset. A test dataset has been created of a small UAV in front of a clear and complex background at distances ranging from 500m up to 2500m using a telescope with a focal length of 1325mm and an aperture of 102 mm. At each distance the resolution is measured with a modified version of the US Air Force resolution chart. The results show that a small UAV is detected with a mAP(0.5) of above 90% in front of a complex background up to a distance of 1167m given a minimum resolution of 9:3mm or 8μrad and up to 2222m in front of a clear background given a minimum resolution of 38mm or 17:1μrad.

Conference Presentation

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

Citation Download Citation

Denis Ojdanić, Niklas Paternoster, Christopher Naverschnigg, Andreas Sinn, and Georg Schitter "Evaluation of the required optical resolution for deep learning-based long-range UAV detection", Proc. SPIE 13040, Pattern Recognition and Prediction XXXV, 130400A (7 June 2024); https://doi.org/10.1117/12.3013251

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