1. Drone Based Aerial Imaging For Post-Disaster Reconnaissance
Théau Héral1, William Greenwood2, Dimitrios Zekkos2, PhD, PE and Jerome Lynch2, PhD
1Department of Aerospace Engineering • 2Department of Civil and Environmental Engineering • University of Michigan • Ann Arbor, MI
Email: theau@umich.edu • wwgreen@umich.edu • zekkos@umich.edu
References
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wars/308304/
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Photogramm. Remote Sens. Spatial Inf. Sci., XL-1 W, 2, 21-25.
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response-philippines/
6. DJI, (4/19/2015), http://www.dji.com/
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monitoring-quadcopters/
8. Huang Y B, Thomson S J, Hoffmann W C, Lan Y B, Fritz B K. Development and prospect of unmanned
aerial vehicle technologies for agricultural production management. Int J Agric & Biol Eng,2013;6(3):1-10.
9. MatLab Documentation, (4/19/2015), http://www.mathworks.com/help/matlab/
10. Remondino, F., Barazzetti, L., Nex, F., Scaioni, M., & Sarazzi, D. (2011). UAV photogrammetry for mapping
and 3d modeling–current status and future perspectives. International Archives of the Photogrammetry,
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11. Tweaktown, (4/19/2015), http://www.tweaktown.com/news/42572/faa-issues-drone-permits-real-
estate- agriculture-commercial-use/index.html
Conclusions
There is a wide array of imaging and computer vision applications for UAVs
with the ability to impact many industries such public safety, agriculture, and
engineering. One such application is post-disaster reconnaissance and
infrastructure assessment. Before images and video can be utilized, post-
processing for lens corrections is required. A MatLab program has been
written for automatically correcting radial distortion in photos and videos
taken by a Phantom 2 Vision+ UAV. The code is flexible enough to be adapted
for other cameras and UAVs. The lens correction has been integrated with a
simple crack detection algorithm and will be incorporated with detection of
other features related to geotechnical engineering.
Abstract
Immediately following natural disasters, such as earthquakes, reconnaissance
studies are performed to collect data and observe damage to infrastructure
and geotechnical systems. However, access to sites is often limited due to
safety considerations, difficulty and time. An Unmanned Autonomous Aerial
Vehicle (UAAV) capable of gaining access to these areas could solve many
problems and lead to more efficient post-disaster reconnaissance. A UAAV
site reconnaissance and characterization platform is being developed. Among
the many data collection features, the UAAV will collect photos and videos
used to identify damage and features of interest. Commercial Unmanned
Aerial Vehicles (UAVs), for performing preliminary field testing, were
compared. The DJI Phantom 2 Vision + was selected after an investigation of
the UAVs most commonly used with image processing techniques. Photos and
videos recorded by the Phantom are used as the basis for calibrating image
processing methods for identifying geotechnical features of interest. Before
these aerial images and videos can be used for this purpose, significant post-
processing is required. A MatLab program was developed to automatically
detect photos and videos and batch process them to apply the necessary
corrections. A correction for lens distortion is applied to remove the barrel
effect (also known as fisheye effect) caused by the Phantom camera lens.
Once the photos are corrected, they were used for automated crack
detection.
Acknowledgements
The authors would like to acknowledge funding from Rackham Graduate
School of the University of Michigan – Ann Arbor through a Rackham
Graduate Student Research Grant. The authors would like to thank Bob
Spence and Jan Pantolin for efforts in constructing the indoor flight facility.
The graduate student is further funded by NSF grant award #1362975.
Image Processing
Lens Correction
• Correct radial lens distortion (“fisheye”)
• Inputs: Intrinsic Matrix and radial distortion
coefficients specific to the camera
Crack detection
• Performed on grayscale images (ignoring color)
• 2D Gaussian filter is applied to the image
• The low-pass filter smooths the image
• Possible cracks are traced by detecting gradients above a specified
threshold
Applications of Drone Technology
Agriculture
• Health monitoring, crop duster,… Huang et al. (2013)
Atmospheric Measures
• Pollution, meteorology,…
Cinematography and Photography
• Aerial views of events, movies,…
Delivery
• Home delivery, medical supply delivery,…
Disaster Assessment
• Earthquakes, tornado, floods, wildfire, search and rescue,…
Baiocchi et al. (2013)
Mapping
• Remote mapping, 3D mapping, archeology… Remondino et al.
(2011)
Disaster Reconnaissance
Objective
Development of a UAAV site reconnaissance and characterization platform:
• Photo and video recording;
• LiDAR scans to collect point clouds of surface deformations;
• Wireless sensor deployment of set predetermined geophone arrays,
• Perform in situ shear wave velocity measurements.
Method
The research focused first on understanding the state-of-the-art practices and
applications of imaging drones with a literature review. Once the DJI Phantom
2 Vision+ was chosen, the images/videos acquisition was conducted by
operating the drone in an indoor cage. MatLab codes for post-processing the
collected images were developed & incorporate feature detection algorithms.
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DJI Phantom 2 Vision + Specifications
Aircraft Specifications
Battery Autonomy 20-25 min
Communication Distance (open area) 500-700m
Hover Accuracy (Ready to Fly) Vertical: 0.8m; Horizontal: 2.5m
Onscreen Real-Time Flight Parameters Photography and Waypoints
3-axis High Performance Gimbal Control Accuracy: ±0.03°
FC 200 Camera
Operating Environment Temperature 0℃-40℃
Sensor size 1/2.3"
Effective Pixels 14 Megapixels
Resolution 4384×3288
HD Recording 1080p30 & 720p
Recording Field Of View (FOV) 110° / 85°
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Lens Correction
Problem: “Fisheye” lens causes barrel distortion
Batch
Process
User input: Save
video frames or not
and at what interval?
Correct images
Save frames
from video
Save images &
EXIF data
Start End
Input
Images or
videos
Correct frame by
frame and saves in a
new corrected video
Collect photos and
images
Save
frames or
not
Uncorrected Corrected
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