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Proceedings Paper

Image quality and super resolution effects on object recognition using deep neural networks
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Paper Abstract

Real-time object recognition systems are critical for several UAV applications since they provide fundamental semantic information of the aerial scene. In this study, we describe how image quality limits object detection frame-works such as YOLO which can distinguish 80 different object classes. This paper will focus on vehicles such as cars, trucks and buses. Pristine high-resolution images are degraded using different blurring functions, spatial resolution, reduced image contrast, additive noise and lossy compression. Object recognition results are significantly better after applying an image super-resolution algorithm to realistically simulated under-sampled imagery.

Paper Details

Date Published: 10 May 2019
PDF: 9 pages
Proc. SPIE 11006, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, 110061M (10 May 2019); doi: 10.1117/12.2518524
Show Author Affiliations
Christoph Borel-Donohue, U.S. Army Research Lab. (United States)
S. Susan Young, U.S. Army Research Lab. (United States)

Published in SPIE Proceedings Vol. 11006:
Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications
Tien Pham, Editor(s)

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