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

Christoph Borel-Donohue; S. Susan Young

doi:10.1117/12.2518524

10 May 2019 Image quality and super resolution effects on object recognition using deep neural networks

Proceedings Volume 11006, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications; 110061M (2019) https://doi.org/10.1117/12.2518524
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

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.

Citation Download Citation

Christoph Borel-Donohue and S. Susan Young "Image quality and super resolution effects on object recognition using deep neural networks", Proc. SPIE 11006, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, 110061M (10 May 2019); https://doi.org/10.1117/12.2518524

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