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 
distress detection system. Journal of Information Sciences. 108, 
pp. 219-240. 
Kim, J. 1998 Development of a Low-Cost Video Imaging 
System for Pavement Evaluation. Oregon State University: 
Ph.D. Thesis. 
Lee, H., and Oshima, H., 1994. New Cracking Imaging 
Procedure Using Spatial Autocolletion Function. Journal J. 
Transp. Engrg, 120(2), pp. 206-228. 
Oh, H., 1998. Image Processing Technique in Automated 
Pavement Evaluation System. University of Connecticut: Ph.D. 
Dissertation.