Abstract
We develop and apply a deep learning-based computer vision pipeline to automatically identify crew members in archival photographic imagery taken on-board the International Space Station. Our approach is able to quickly tag thousands of images from public and private photo repositories without human supervision with high degrees of accuracy, including photographs where crew faces are partially obscured. Using the results of our pipeline, we carry out a large-scale network analysis of the crew, using the imagery data to provide novel insights into the social interactions among crew during their missions.
Original language | English |
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Pages (from-to) | 262-269 |
Number of pages | 8 |
Journal | ACTA ASTRONAUTICA |
Volume | 200 |
Early online date | 12 Aug 2022 |
DOIs | |
Publication status | Published - Nov 2022 |
Keywords
- Amazon rekognition API
- Computer vision
- Deep neural networks
- Face detection and identification
- International Space Station
- Social analysis