Dr. Sayani Sarkar Profile

Dr. Sayani Sarkar

at Bellarmine Univ

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 31 May 2022 Presentation + Paper

A deep-learning, vision-based framework for testing swarm algorithms using inexpensive mini drones

Sayani Sarkar, Nathan Johnson

Proceedings Volume 12124, 1212409 (2022) https://doi.org/10.1117/12.2618137

KEYWORDS: Unmanned aerial vehicles, Detection and tracking algorithms, Neural networks, Surveillance

Read Abstract +

The ability to explore dangerous buildings or hostile landscapes using a swarm of inexpensive mini drones is relevant to many search and rescue or surveillance scenarios encountered by civilian first responders and military personnel. Swarms of mini drones, implementing various path planning algorithms, provide a unique solution in situations where there is the risk to human life or use of expensive Unmanned Aerial Vehicle technology would be cost-prohibitive or both. Although inexpensive, off-the-shelf drones contain stabilization circuitry and onboard cameras, they suffer restricted flying time and lack GPS systems. The limited capability of such drones has curtailed their use by researchers investigating practical search and genetic algorithms, and many researchers rely on simulation, rather than testing with actual drones. In this paper, we describe an ad hoc framework for testing swarm algorithms while taking the first step toward implementing swarm intelligence using low-cost, offthe-shelf drones and an inexpensive network router. We initially created a public dataset, MINIUAV, including images of Tello and TelloEdu mini-drones taken from our live drone video recordings and photos scraped from various internet resources. Using the images, we then trained a deep-learning-based YOLOv4-Tiny (You Only Look Once) object detector allowing us to implement a swarm intelligence rule where drones act collectively based on a swarm alignment rule. Our results show the object detector allows a drone to identify a neighboring drone with greater than 90% accuracy. Finally, the dataset used to train the object detector will be made available on request.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years