ITE Journal July 2018 - 43

```pH

GSD = -
c

Equation 1

wpH
ssidelap = (1-0.70) -
c

lpH

v = (1-0.70)(-
ci )

Equation 2

Equation 3

Where H is the altitude in meters, p is the pixel size of the
camera in millimeters/pixel, c is the focal length of the camera
in millimeters, w is the lateral image frame size in pixels, l is the
longitudinal image frame size in pixels, and i is the time interval
between image sampling in seconds.

Table 1. Mission Design Table for DJI Phantom 4 Camera Parameters
Altitude, Ground sampling
Spacing Between Speed,
H (m)
distance, GSD (mm/pixel) Passes, ssidelap (m) v (km/h)
5
10
15
20
25
30
35
40
50

1.4
2.7
4.1
5.5
6.9
8.2
9.6
11.0
13.7

2.2
4.5
6.7
9.0
11.2
13.5
15.7
18.0
22.5

2.7
5.4
8.1
10.7
13.4
16.1
18.8
21.5
26.9

UAS Ortho-rectified Images and 3D Model Results
This section describes the results of flying a grid pattern covering
approximately 38 m by 112 m (Figure 4a) directly over the staged
crash scene. The objective of the photogrammetric processing is to
produce an ortho-rectified image, which has a uniform scale. The
production of this image includes the identification of conjugate
landmarks and determination of relative orientation parameters
among neighboring images.
Figure 5a shows the locations of each of the nadir-looking
images taken at 2-second intervals at an altitude of 20 m. The
individual images (5472 x 3648 pixels) have a GSD of 5.50 mm.
Small features, such as the control marker shown in Figure 5b, can
be identified by zooming in. Figure 5c shows the ortho-rectified
image from approximately 220 photos. The six scaling control
markers are labeled by number. The flight duration was approximately 6.9 minutes. This ortho-rectified image can be used to
obtain distance measurements with an approximate accuracy of 2
cm. The following section describes that assessment. Figure 5d is an
example of a 3D model that can also be generated. These 3D models
can be printed by inexpensive 3D printers to provide physical
models (Figure 5e). A data repository contains the original images,
3D model, and composite image for this mission (9). To evaluate the
feasibility of mapping a large linear crash scene, a second mission,
covering 590 m by 45 m, was flown in 12.0 minutes with similar
GSD and horizontal accuracy.

Assessment of Scaling positional accuracy
To provide scaling distances for the ortho-rectified images, six
distances were measured by Purdue Police (Figure 2d and Figure
5c). Segments 1-2 and 3-4 were used to scale the ortho-rectified
image. Digital measurements were then extracted from the scaled,
ortho-rectified image for segments 2-3, 4-5, 5-6, and 6-1 and
compared with those measured in the field with the tape measure.
The maximum error was found to be 23 mm (segment 4-5) and the
minimum error was found to be 6 mm (segment 2-3)

Conclusions and Recommendations
The use of consumer-grade Unmanned Aircraft Systems can
decrease incident clearance times and provide high quality
ortho-rectified images. This paper presented a field procedure for
preparing a crash scene for a UAS mission and mission design
parameters. Two missions were flown at an altitude of 20 m
and were completed within flight times of 6.9 minutes and 12.0
minutes.9 Subsequent analysis of the imagery found that the
ortho-rectified imagery produced by the consumer-grade UAS
had a maximum observed error of 2.3 cm. In addition to the
ortho-rectified imagery, physical 3D prints can be made in under
1 hour with low-cost 3D printers. Based upon the speed of the field
work, short flight time, and high-quality scaling accuracy, there
is an opportunity to significantly reduce incident clearance times
by training public safety officials to deploy UAS for crash scene
mapping. Not only will the resulting imagery provide improved
graphics, but additional secondary crashes can be prevented by
reducing the duration of the road restrictions and queueing. itej

References
1. Mekker, M. M., S. M. Remias, M. L. McNamara, and D. M. Bullock.
Characterizing Interstate Crash Rates Based on Traffic Congestion Using
Probe Vehicle Data. TRB 95th Annual Meeting Compendium of Papers,
Washington, DC, 2016.
2. State of Indiana. Traffic Incident Management Effort, 2017. www.in.gov/
intime/index.htm. Accessed July 18, 2017.
3. Jacobson, L. N., B. Legg, and A. J. O'Brien. Incident Management Using
Total Stations. Transportation Research Record, no. 1376, 1992, pp. 64-70.
4. Agent, K. R., J. A. Deacon, J. G. Pigman, and N. Stamatiadis. Evaluation
of Advanced Surveying Technology for Accident Investigation.
Transportation Research Record, no. 1485, 1995, pp. 124-133.
5. Ardestani, S. M., P. J. Jin, O. Volkmann, J. Gong, Z. Zhou, and C. Feeley. 3D
Accident Site Reconstruction Using Unmanned Aerial Vehicles (UAV). TRB
95th Annual Meeting Compendium of Papers, Washington, DC, 2016.
w w w .i t e.o r g

J u ly 2018

43

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