The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008 
3.2.6 Classification 
A simple rule-based classification approach was developed, 
taking into account four distress types: longitudinal cracking, 
transverse cracking, alligator cracking and non-cracking. 
Subroutines were implemented with decision rules that compare 
the several parameter in x and y-array extracted from the binary 
image. 
From the characteristics of summed gray level graphs for a 
number of pavement images with the different types of 
distresses, the following was found to be true. Different 
distresses will give distinctly different summed array profiles. 
For alligator crack, the distress zones in two arrays are 
relatively wide. For a longitudinal and transverse crack, the 
narrow and sharp peak of summed gray level is always found in 
the x-array and y-array respectively, whereas the summed gray 
level in the other array should not show noticeable peaks 
3.2.7 Quantification 
Another deterministic approach in APIP is to classify distress 
severity level based on “A Guide to Visual Assessment of 
Flexible Pavement Surface Condition” (JKR, 1992). The 
manual explains how to measure and give the unit of 
measurement for each type of crack. Alligator cracking is 
measured in square meters. Longitudinal cracking and 
transverse cracking are measured in linear meters. The severity 
levels of each type of crack are classified as low, moderate, or 
high. For example, a moderate severity of longitudinal 
cracking is described as “cracks with moderately severe 
spalling; meaning unsealed crack width greater than 3 mm; 
sealant material in bad condition” (JKR, 1992). 
The standard crack density concept can be readily and logically 
implemented in pavement image processing (Lee and Oshima, 
1994). The standard crack density is determined by multiplying 
the extent with the average crack width. The standard crack 
density concept is at an advantage for use in image processing 
analysis since it takes into consideration both extent and width 
of cracks simultaneously. 
A simple concept of determining length, area, average width 
and extent of crack is developed. Area (A) of distress can be 
calculated from the total number of pixels from distress area 
and multiplied by the pixel size. Length (/) will be determined 
based on the polylines of one pixel width. Then, the average 
crack width (CW) can be derived from area by dividing the 
length. To illustrate the computation of cracking quantification 
in APIP, rule-based classification of severity level was 
programmed and severities of each type of crack are mainly 
referred to crack density and average width. 
4 RESULTS ANDANALAYSIS 
The accuracy of the system performance was determined by 
using t-test. This is required in order to determine how close an 
observation matches the accepted reference value or the 
assumed true value. For comparison purposes, visual field 
surveys were adopted as the true^ values or reference values. 
Therefore, the observed data from the photogrammetric system 
and Automated Pavement Imaging Program (APIP) were 
compared with the data from the visual field inspection. 
4.1 Photogrammertric System 
Ten pavement distresses, including five potholes and five 
delaminations were randomly selected at fields and processed 
through the photogrammetric system. The final estimations of 
volume, area and depth resulted from manual inspection along 
with the values from the photogrammetric system were 
calculated. 
The t-test was performed to determine whether the mean of 
photogrammetric system was different from the mean of manual 
method. At a significance level of 0.20, the computed t a value 
was found to be 1.332 with 18 degrees of freedom. To examine 
the relationship between the evaluations, t statistic values were 
determined using the calculated data. As indicated in Table 2, 
we do not reject the null hypothesis since the computed t 
statistic (volume), t statistic (area) and t statistic (depth) were 
not greater than the t a value. Therefore, the mean of the 
volumes, areas and depths obtained from the photogrammetric 
system are not significantly different from those of the visual 
field surveys. 
(a) Analysis of Volume 
Technique 
Mean (m J ) 
S.D 
t 
t a 
Manual 
0.001341 
0.001243 
0.175 
1.332 
Photogrammetry 
0.001246 
0.001196 
(b) Analysis of Area 
Technique 
Mean (nf) 
S.D 
t 
ta 
Manual 
0.0642 
0.0398 
0.104 
1.332 
Photogrammetry 
0.066 
0.0387 
(c) Analysis of Depth 
Technique 
Mean (mm) 
S.D 
t 
ta 
Manual 
17.28 
8.26 
0.509 
1.332 
Photogrammetry 
15.4 
8.3 
Table 2. Paired t-test for manual and photogrammetric system 
Sam. 
No. 
Distress Type 
Severity Level 
Accuracy 
Manual 
Photogram. 
1 
Pothole 
Low 
Low 
100 
2 
Pothole 
Low 
Low 
100 
3 
Pothole 
Low 
Low 
100 
4 
Pothole 
Moderate 
Moderate 
100 
5 
Pothole 
Moderate 
Moderate 
100 
6 
Delamination 
Moderate 
Moderate 
100 
7 
Delamination 
Low 
Low 
100 
8 
Delamination 
Low 
Low 
100 
9 
Delamination 
Low 
Low 
100 
10 
Delamination 
Low 
Low 
100 
Total Accuracy 
100 
Table 3. Severity level comparison (Manual vs. Photogram.) 
using ten samples 
Table 3 summarizes the severity rating results from the 
photogrammetry and manual approaches. It can be clearly seen 
that, in all the samples testes, the severity level detected by the 
photogrammteric method is in total agreement with the level 
obtained from manual method. 
4.2 Automated Pavement Imaging Program 
The validation of the Automated Pavement Imaging Program 
(APIP) algorithms is discussed in this section. Firstly, proper 
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