Accelerating Deep Learning Applications in Space

Lofqvist, M. and Cano, J. (2020) Accelerating Deep Learning Applications in Space. 34th Annual Small Satellite Conference, Virtual Conference, 2020.

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Abstract

Computing at the edge offers intriguing possibilities for the development of autonomy and artificial intelligence. The advancements in autonomous technologies and the resurgence of computer vision have led to a rise in demand for fast and reliable deep learning applications. In recent years, the industry has introduced devices with impressive processing power to perform various object detection tasks. However, with real-time detection, devices are constrained in memory, computational capacity, and power, which may compromise the overall performance. This could be solved either by optimizing the object detector or modifying the images. In this paper, we investigate the performance of CNN-based object detectors on constrained devices when applying different image compression techniques. We examine the capabilities of a NVIDIA Jetson Nano; a low-power, high-performance computer, with an integrated GPU, small enough to fit on-board a CubeSat. We take a closer look at the Single Shot MultiBox Detector (SSD) and Region-based Fully Convolutional Network (R-FCN) that are pre-trained on DOTA -- a Large Scale Dataset for Object Detection in Aerial Images. The performance is measured in terms of inference time, memory consumption, and accuracy. By applying image compression techniques, we are able to optimize performance. The two techniques applied, lossless compression and image scaling, improve speed and memory consumption with no or little change in accuracy. The image scaling technique achieves a 100% runnable dataset and we suggest combining both techniques in order to optimize the speed/memory/accuracy trade-off.

Item Type:Conference or Workshop Item
Status:Published
Refereed:No
Glasgow Author(s) Enlighten ID:Cano Reyes, Dr Jose
Authors: Lofqvist, M., and Cano, J.
College/School:College of Science and Engineering > School of Computing Science
Published Online:01 August 2020
Copyright Holders:Copyright © 2020 The Authors
Publisher Policy:Reproduced with the permission of author
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