Name
Abstract | ||
|---|---|---|
In this work, an unmanned aerial system is implemented to search an outdoor area for an injured or missing person (subject) without requiring a connection to a ground operator or control station. The system detects subjects using exclusively on-board hardware as it traverses a predefined search path, with each implementation envisioned as a single element of a larger swarm of identical search drones. Imagery is streamed from a camera to an Odroid single-board computer, which prepares the data for inference by a Neural Compute Stick vision accelerator. A single-class TinyYolo network, trained on the Okutama-Action dataset and an original Albatross dataset, is utilized to detect subjects in the prepared frames. The detection apparatus is mounted on a drone and field tests validate the system feasibility and efficacy. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/SYSOSE.2019.8753882 | 2019 14th Annual Conference System of Systems Engineering (SoSE) |
Keywords | DocType | ISBN |
search and rescue,drone,UAS,deep learning,edge,autonomous,neural compute stick | Conference | 978-1-7281-0458-4 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jonathan McClure | 1 | 0 | 0.34 |
| Ferat Sahin | 2 | 706 | 45.49 |