A Dual Lights Inspection Method for Automatic Pavement Survey
Date Issued
2010
Date
2010
Author(s)
Su, Yung-Shun
Abstract
A pavement survey is a time-consuming but necessary task to ensure the serviceability
of road pavements. Many investigators have used image-processing methods to automate
the survey processes and enhance the quality and accuracy of survey results. However,
the image-processing methods often mistakenly treat the oil spillages, shadow, and road
marking as distresses since their features are very close to the ones of distresses. Therefore,
in this research, we have developed a dual light inspection (DLI) method to reduce
false alarms.
DLI includes four major steps: (1) image capture: we retrieved two images as a pair
from the same position and orientation with two different light setups; (2) image subtraction:
we subtracted these two images pixel by pixel to obtain a subtracted image which
shows the differences between them; (3) image enhancement: we applied an edge detection
method to retrieve the distress features; (4) image classification: finally, we used a
classification algorithm to identify images including distresses with the ones without.
A field test was conducted to verify the DLI method. We took 212 pairs of images
at night, including alligator cracks (42 pairs), manholes (42 pairs), longitudinal cracks
(58 pairs), spillages (34 pairs), and road markings (52 pairs). Twenty percent of the images
(i.e. 45 pairs) are used as training sets to train the classification model. We then used remaining images to test the accuracy of the classification model. We compared
the accuracy between the DLI method, which uses dual light image pairs, and traditional
method, which uses individual images. We found that DLI can significantly improve the
accuracy in identifying spillages (traditional method: 18%, DLI: 82%) and road markings
(traditional method: 8%, DLI: 96%). The accuracy of the two methods in identifying
other distresses, including alligator cracks (traditional method: 95%, DLI: 90%), manholes
(traditional method: 97%, DLI: 100%) and longitudinal cracks (traditional method:
62%, DLI: 69%) was approximately. This result indicates that DLI is a reliable method
to conduct pavement inspections.
Subjects
dual-light inspection
pairing images
pavement survey
pavement distresses
computer vision
image enhancement
image classification
Type
thesis
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