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Conventional human-visual and manual field pavement crack detection method and approaches are very costly,
time-consuming, dangerous, labor-intensive and subjective. They possess various drawbacks such as having a high
degree of variability of the measure results, being unable to provide meaningful quantitative information and almost
always leading to inconsistencies in crack details over space and across evaluation, and with long-periodic measurement.
With the development of the public transportation and the growth of the Material Flow System, the conventional method
can far from meet the demands of it, thereby, the automatic pavement state data gathering and data analyzing system
come to the focus of the vocation's attention, and developments in computer technology, digital image acquisition, image
processing and multi-sensors technology made the system possible, but the complexity of the image processing always
made the data processing and data analyzing come to the bottle-neck of the whole system. According to the above
description, a robust and high-efficient parallel pavement crack detection algorithm based on Multi-Scale Space is
proposed in this paper. The proposed method is based on the facts that: (1) the crack pixels in pavement images are
darker than their surroundings and continuous; (2) the threshold values of gray-level pavement images are strongly
related with the mean value and standard deviation of the pixel-grey intensities. The Multi-Scale Space method is used to
improve the data processing speed and minimize the effectiveness caused by image noise. Experiment results
demonstrate that the advantages are remarkable: (1) it can correctly discover tiny cracks, even from very noise pavement
image; (2) the efficiency and accuracy of the proposed algorithm are superior; (3) its application-dependent nature can
simplify the design of the entire system.
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