Mr. Joseph A. Jennings Profile

Joseph A. Jennings

at NAVFAC EXWC

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Proceedings Article | 27 May 2022 Presentation + Paper

Comparison of onboard processors for rapid target identification in unmanned aircraft systems

Joseph Jennings, Jean Pan, Lucas Overbey, Jamie Lyle

Proceedings Volume 12102, 121020D (2022) https://doi.org/10.1117/12.2618949

KEYWORDS: Performance modeling, Image processing, Machine learning, Image classification, Image segmentation, Data modeling, Target recognition, Data processing, Operating systems, Data storage

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The real-time identification of targets on small unmanned aircraft systems (UAS) is a challenging task. One approach to achieving this task is the use of image recognition in deep learning networks on embedded processors. While it has been well established that the use of deep learning networks can help increase the reliability of image recognition applications, less research has been performed on the requirements needed for selecting an appropriate embedded processor that can meet the speed and efficiency needs for real-time target identification. The embedded processor must fit within the size, weight, and power (SWaP) constraints of small UAS, while still meeting the computational and memory requirements of the detection algorithms. To determine whether embedded processors meet these form factor requirements and other performance considerations, we evaluated and compared several commercially available embedded processors based on their physical specifications, performance using lightweight benchmark machine learning models developed for commercial use, and performance using a Navy-developed deep convolutional neural network (CNN) used for identifying the California Least Tern. This evaluation will provide information on the necessary hardware and software requirements for performing complex computing tasks on a UAS in real-time using image recognition deep learning networks on embedded processors.

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