The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008
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constant values of A (1.8 and 2.0) were selected for the image
segmentation (thresholding) algorithm. This is followed by
comparing the crack width (CWs) and cracking density (CDs)
obtained from the automated rating against those from the
manual rating. Twenty-eight image samples collected from
various road segments were used for the analysis.
4.2.1 Crack Width and Cracking Density
To evaluate the APIP performance using the on-field
measurement approach, the results had to be analysed by using
statistical approaches. The results of the paired t-test are
summarized in Tables 4 and 5. The statistic t-values from the t-
test are listed in two categories, i.e., CWs and CDs.
(A) Analysis of Average Crack Width
Technique
Mean (mm)
S.D
t
ta
Manual
6.077
2.271
0.363
2.004
APIP
6.08
2.503
(B) Analysis of Average Cracking Density
Technique
Mean (cm/cmj
S.D
t
ta
Manual
0.0422
0.428
0.841
2.004
APIP
0.0516
0.0415
Table 4: Statistic test for Manual and APIP (A=1.8) inspection
As shown in Table 4, all statistic t-values were lower than the t a
at the significance level a = 0.20. Thus, t-distribution indicated
that there were no significant differences between the overall
crack width and cracking density for the 1.8 APIP and the field
inspections. In Table 4, test for 2.0APIP also showed higher
t a -values than the statistic t-values at the 0.20 significance level.
This indicates that the mean difference between each data was
not significantly different.
(A) Analysis of Average Crack Width
Technique
Mean (mm)
S.D
t
ta
Manual
6.077
2.271
0.222
2.004
APIP
5.735
2.41
(B) Analysis of Average Cracking Density
Technique
Mean (cm/cm")
S.D
t
ta
Manual
0.0422
0.428
0.206
2.004
APIP
0.0444
0.0373
Table 5. Statistic test for Manual and APIP (A=2.0) inspections
4.2.2 Cracking Type Prediction
The APIP was then tested to determine whether it could
distinguish between several types of crack and images without
crack. Using additional three actual pavement images without
crack, the automated imaging algorithms correctly identified
most of them.
From the results, APIP worked very well in predicting the
presence or absence of distress features on the image. Of the 25
images with distress features, the 1.8 APIP correctly predicted
23 images that had distress features while 2.0APIP correctly
predicted 24 images. Only 2 images out of 25 images with
distress features were incorrectly categorized by using
1.8 APIP. However, 2.0APIP had only wrongly categorised 1
image. Of the 3 distress-free images, both 1.8_APIP and
2.0APIP correctly categorised them all as non-crack distress.
Therefore, the overall predictions were found to be 92.86 %
accurate for 1.8 APIP and 96.43% accurate for 2.0APIP.
4.2.3 Severity Level Classification
Severity comparisons indicated that the APIP algorithms were
mainly in the range of 88%. However, in a few cases, severity
appeared to be lower by using the automated system. The lower
classifications of the severity indicated similar concerns that
were present during any of the survey at fields, which resulted
from interpretations of the severity level from person to person.
5 CONCLUSIONS
It has been shown that the combination of a digital
photogrammetric system and APIP allows complete automation
with near real-time measurement of pavement distresses. More
importantly however, the accuracy of this system in identifying
pavement distress meets the standards of set out by the road
authority for pavement evaluation. From the ten tested samples,
the photogrammetric system produced very reliable and
objective results. The output from APIP will be in a form of a
report detailing the type, extent and severity of the various
types recognized. To date, the overall system has been found to
be about 90 percent accurate.
Even though there were some limitations, the system is
expected to provide a user-friendly approach to local
transportation agencies as well as the government sector. An
integration of several image processing algorithm in one
Automated Pavement Imaging program has the capability of a)
processing the image to isolate the distress features, b)
displaying the binary image, c) reporting the type and severity
of that image in an output report. It is anticipated that the
distress quantification will be effectively and conveniently done
without physically touching the pavement area, thus reducing
the risk of traffic movement interference.
Whilst the application developed in this research was focused
towards pavement evaluation, the concept derived from non-
contact, close-range digital metrology and the image processing
algorithms are clearly independent of the specific application.
The methods utilized in this study could be applied to many
other civil engineering fields.
REFERENCES
Cheng, H. D., and Miyojim, M., 1998. Automatic pavement
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Kim, J. 1998 Development of a Low-Cost Video Imaging
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Ph.D. Thesis.
Lee, H., and Oshima, H., 1994. New Cracking Imaging
Procedure Using Spatial Autocolletion Function. Journal J.
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Oh, H., 1998. Image Processing Technique in Automated
Pavement Evaluation System. University of Connecticut: Ph.D.
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