| dc.description.abstract | The pavement maintenance of a road today relies mainly on manual pavement condition inspection and distress rating, and this manual method is costly, labor-intensive, time-consuming, and dangerous to construction workers and may affect traffic flow. Moreover, such the manual method is very subjective and may have a high degree of variability, being unable to provide meaningful information. Additionally, because only a small area of the road surface can be sampled by using the manual method, it may result in relatively low accuracy of pavement distress information. Hence, an automatic inspection system of pavement distress images is desired in hope of resolving the above problems.
This paper presents a novel method to classify pavement distress images. The system is called pavement distress recognition system (PDRS), and it is installed on a pavement inspection vehicle with image acquiring devices. First, pavement images were processed to show only black-and-white pixels that can render true pavement cracks. Then, the pavement images were transformed into a set of clusters in order to capture the distress locations of each crack. Next, the distress types, i.e., horizontal, vertical, alligator-like, or man-hole-like, were obtained by applying a decision tree algorithm. Then, the system saves the data and images into a database and provides spatial query functions to users to retrieve crack information. Finally, the pavement distress database is generated to save the information of pavement distress, and using crack grouping algorithm (CGA) and crack linking algorithm (CLA) to create the spatial relation for distress management. The present results showed that our method can successfully recognize various types of pavement distress. Our system also provides information regarding pavement crack lifecycle, i.e., when the crack was identified, when it was fixed, etc., so that public road agencies can define maintenance plans in accordance with real pavement conditions.
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