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. To increase the affordability of such a swarm, the system cost per drone serves as a primary constraint. 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 final network achieves 7.6 FPS in the field (8.64 FPS on the bench) with an 800x480 input resolution. The detection apparatus is mounted on a drone and field tests validate the system feasibility and efficacy.
Electrical Engineering (MS)
Department, Program, or Center
Electrical Engineering (KGCOE)
McClure, Jonathan, "A Low-Cost Search-and-Rescue Drone Platform" (2019). Thesis. Rochester Institute of Technology. Accessed from
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