This document summarizes a project to develop a semi-automated crow detection system using a quadcopter. The system aims to help researchers study crow roosting patterns. It involves assembling hardware including a quadcopter, cameras, and wireless transmission equipment. Software includes a GUI for viewing live video streams and processing images using MATLAB to detect and count crows with 86% accuracy. Future work involves improving the quadcopter, cameras, GUI, image processing, and video transmission to increase functionality and performance of the crow detection system.

1. Semi-Automated Crow
Detection System
Ricky Chan
Aaron Gupta
Michael Ma
Donny Sun

2. Agenda
- Introduction, Background and Project Description
- Hardware Assembly
- GUI (Graphical User Interface)
- Video Transmission
- Image Processing
- Testing and Results
- Future Work
- Difficulties and What We Have Learned

3. Introduction
The University of Washington, Bothell, Biology
Department is studying the crows and plants of the
North Creek wetlands.
Professor Doug Wacker is interested in studying the
roosting patterns of the crows.
Our project is integrating the quadcopter, which is
made by the mechanical engineering team, to help
Professor Doug Wacker to take images of crows.

4. Background
From fall to late spring, over 10,000 crows roost in
the North Creek wetlands at dusk.
Professor Wacker’s research includes the following
focuses:
(1) the number of crows that roost in the dusk;
(2) whether spatial patterns exist among individual
crow roosting locations; and
(3) how, if at all, the spatial roosting patterns relate
to the location of nearby plants.

5. Project Description
Video Transmission
Taking video stream from the quadcopter and Pi camera
Transferring video stream to ground station
Ground Station Computer and GUI
Run a script, download and save the video stream
User can capture images while video is playing
Image Processing
Run a MATLAB script to process the capture image

6. Hardware Assembly
WiFi Transmitter
Raspberry Pi Monitor ModuleRaspberry Pi ModuleRaspberry Pi NoIR Camera V2
WiFi Receiver

7. GUI
Language used: Python
Libraries used: PyGame & Tkinter (TeaKay Interface)
Platform: Unix/Linux

8. GUI
Quits program
Retrieves video from
raspberry pi, converts
into a usable format and
opens video file
Opens video
inside gui
Alternates playing and
pausing current video
Skips 5 seconds
ahead in video
Stops current video
playing
Saves current
frame as JPEG
and processes it
Skips to a specific
time frame
Captures and saves
the images
corresponding to the
button clicks
}

9. Dataflow
Diagram

10. Video Transmission: System
Comprises of :
- 2 dual band 2.4Ghz/5Ghz Wifi Adapters
- 2 Raspberry Pi’s
- Raspberry Pi Camera
- USB
Utilizes:
- Open source project - wifibroadcast
Inner workings:
- Monitor mode & packet injection
R.Pi
Drone
C
a
m
e
r
a
W
ifiadapter
R.Pi
W
ifiadapter
U
S
B R.Pi. Screen
live stream

11. Image Processing: Introduction
Original Image Processed Image

12. Image Processing: System Characteristics
Characteristic Limitation Reasoning
Crow Detection
Detects, but doesn’t
recognize, crows
Cannot differentiate
between crow and
bird-shaped objects
Sports Field is clear of
debris; only crows roost on
Sports Field
Background Location
UWB Sports Field
Doesn’t work in North Creek
woods
Crows roost on Sports Field
& S.F. has relatively low
noise
Object Distance
5 - 15m
Not guaranteed to work
outside of this range
Meets contract
specifications
Time of Day
Dusk Untested at night Tested w/o infrared lights
Camera Perspective
Top-down Top-down only
Reduces noise, gimbal
shape

13. Image Processing: Active Contour
- General theory
- Main Benefit
- Autonomous & adaptive method
- Drawbacks
- Minute features ignored
- Needs adjustment to increase
accuracy

14. Image Processing: Flowchart

15. Image Processing: Example Case
1. Load captured screenshot 2. “Grayscale” image

16. Image Processing: Example Case (con’t.)
3. Adjust image contrast 4. Threshold image

17. Image Processing: Example Case (con’t.)
5. Trim image 6. Generate mask

18. Image Processing: Example Case (con’t.)
7. Apply mask to find crows 8. Determine background

19. Image Processing: Example Case (con’t.)
9. Subtract background 10. Generate final result

20. Testing/Results
Range test for video transmission
1. 5 dBi antenna
Distance from soccer field through trees ~183m
Distance from soccer field through short foliage ~203m
2. 9 dBi antenna
Distance from soccer field through trees 237m~
Distance from soccer field through short foliage ~291m

21. Testing/Results (con’t.)
- Image tested : 13
- Failed Detection(s) : Image #8
- Total crows in 12 images: 57
- Total crows counted : 66
- FP : 66 - 57 = 9, TP: 57, FN = 0, TN = 0
- ≅ 86%
Image 1 2 3 4 5 6 7 8 9 10 11 12 13
# of
crows
6 6 4 4 5 6 4 4 6 6 2 4 4
#s
counted
6 6 4 6 5 6 6 30 8 5 3 6 5

22. Future Improvements
- Quadcopter
○ Reduce the operating noise of quadcopter
- Camera
○ Obtain and use high-resolution thermal vision camera
- GUI
○ Add more features (such as playback bar to control the video)
- Image Processing
○ Adapting machine learning algorithm to do the pattern recognition of crows
- Video transmission
○ Change system to 2.4GHz bandwidth to increase range and reliability

23. What We Have Learned
- The difficulties of working as a team
- Importance of self-motivation → Conducting individual research
- The Value of:
- Good communication skills
- Periodic re-evaluation of the project

24. Project Difficulties
- Broken quadcopter
- Evolution of project scope
- Hardware limitations
- Crows refused to cooperate
- Coordinating with external help

25. Thank You!
Questions?