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Abstract
The Nvidia Tegra X1 (TX1) is a credit-card size system-on-a-chip (SoC) that contains an entire suite of input, output, and processing hardware. It is designed to take advantage of Nvidia’s graphics processing unit (GPU) architecture and CUDA (formerly Compute Unified Device Architecture) parallel computing platform in order to provide a deep learning capability within a small form factor. The novelty of such a small size makes the TX1 capable of being deployed onboard a satellite, or as the primary instrument of a CubeSat. Accompanying software exists to optimize the TX1 for image processing tasks such as image recognition, object detection and location, and image segmentation. Such on-board processing power would make an equipped satellite able to execute complex decisions based on the images it receives during flight. This paper describes the effort to achieve these image processing tasks on the ground based on original datasets, with the motivation that models could be trained to be deployed onboard spacecraft containing cameras and GPU hardware. Though the distances of space make high-resolution images difficult to obtain from orbital assets, compact devices such as the Nvidia TX1 (and the newer TX2) demonstrate the potential for a spacecraft to achieve increased situational awareness based on streams of collected images.
Presentation
Satellite Identification Imaging for Small Satellites Using NVIDIA
The Nvidia Tegra X1 (TX1) is a credit-card size system-on-a-chip (SoC) that contains an entire suite of input, output, and processing hardware. It is designed to take advantage of Nvidia’s graphics processing unit (GPU) architecture and CUDA (formerly Compute Unified Device Architecture) parallel computing platform in order to provide a deep learning capability within a small form factor. The novelty of such a small size makes the TX1 capable of being deployed onboard a satellite, or as the primary instrument of a CubeSat. Accompanying software exists to optimize the TX1 for image processing tasks such as image recognition, object detection and location, and image segmentation. Such on-board processing power would make an equipped satellite able to execute complex decisions based on the images it receives during flight. This paper describes the effort to achieve these image processing tasks on the ground based on original datasets, with the motivation that models could be trained to be deployed onboard spacecraft containing cameras and GPU hardware. Though the distances of space make high-resolution images difficult to obtain from orbital assets, compact devices such as the Nvidia TX1 (and the newer TX2) demonstrate the potential for a spacecraft to achieve increased situational awareness based on streams of collected images.
