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. 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.

Library of Congress Subject Headings

Search and rescue aircraft--Design and construction; Drone aircraft--Design and construction; Search and rescue operations--Technological innovations

Publication Date

5-2019

Document Type

Thesis

Student Type

Graduate

Degree Name

Electrical Engineering (MS)

Department, Program, or Center

Electrical Engineering (KGCOE)

Advisor

Ferat Sahin

Advisor/Committee Member

Raymond Ptucha

Advisor/Committee Member

Sohail Dianat

Campus

RIT – Main Campus

Plan Codes

EEEE-MS

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