This document provides an overview of UAV image recognition technology and applications. It defines UAVs and describes the key technologies that have enabled their development, such as autopilots, GPS, and miniaturized components. It outlines the UT UAV group's work on autonomous target recognition for competition, including detecting, analyzing, and determining the position of targets in images. The group's system achieves 85% detection accuracy and aims to reduce position error below 50 feet. Potential applications of UAVs discussed include monitoring oil pipelines and ranches as well as aiding wildfire response. Strict regulations govern UAV use due to safety concerns.
1. UAV Image Recognition
Technology and Applications
The UT UAV Group
Cockrell School of Engineering
The University of Texas at Austin
Harmony Mones‐Murphy
Chockalingam Viswanathan
Tejas Kulkarni
2. What is a UAV?
• The Department of Defense Dictionary
defines a UAV as:
A powered, aerial vehicle that does not carry a
human operator, uses aerodynamic forces to
provide vehicle lift, can fly autonomously or be
piloted remotely, can be expendable or
recoverable, and can carry a lethal or nonlethal
payload.
3. Driving Technology
• Powered heavier than air flight
• Radio control (R/C)
• Autopilots
• GPS
• Imagery systems
• High density power batteries
• Long range and low‐power micro radio devices
• Miniaturized parts
• Wireless networks
• Powerful micro‐processors
5. 1898: First demonstration
of radio‐control
Nikola Tesla’s “Teleautomaton,” a radio‐
control boat
Electrical Exposition at Madison
Square Garden
6. 1912: First autopilot
Elmer and Lawrence Sperry
Curtiss B‐2
• A gyrostabilizer hydraulically
operated the elevators and rudder.
• Allowed the aircraft to fly straight
and level without pilot input.
17. Our Team
• Undergraduate
• Interdisciplinary
• Student leadership
18. AUVSI Competition
• Student UAS
Competition in
Maryland
• Reconnaissance
mission
• Fourth year of
participation
• 1st in Autonomous
Target Recognition in
2010
24. Foreground Image
• Average RGB pixels in frame
0.06
Red Plane
0.04
P e rc en t
0.02
0
0 50 100 150 200 255
Green Plane
0.04
P erc e nt
0.02
0
0 50 100 150 200 255
Blue Plane
0.04
P erc e nt
0.02
0
0 50 100 150 200 255
Pixel Intensty
• Compute distance from mean
• Distance threshold determines potential
targets
28. Target Detection Performance
• Tested on scaled airfield and recorded
video
– Robust to trees, runways
– Poor at detecting some colors
Specification Performance
Speed 10 frames per second
Detection Accuracy* 85%
False Positive Rate 10%
* Accuracy = ratio of targets detected to total number of targets