| dc.description.abstract | Pavement Condition Index, or PCI, is an important index of pavement service level, it not only affects the comfort of driving, but also provides the suitable time for pavement construction and rehabilitation. In order to manage pavement effectively, it is needed to inspect and collect pavement condition data on a regular base. Most of past inspection techniques are time consuming and labor intensive. The objective of this research was to develop an Auto Pavement Damage Image Detection System, or APDIDS, which can provide an automatic inspection technique.
This research used two linear scanning cameras and related image collection equipment to inspect pavement conditions. Moreover, it was then to develop an Auto Pavement Distress Image Recognition System, or APDIRS, which can identify the the common pavement defacts from APDIDS including: longitudinal, transverse cracking, potholes, patching, alligator cracking, manholes, and so on. The APDIRS can automatically evaluate the pavement defacts and conclude the value of PCI.
The research results indicated that by using two line scanning camera, the pavement inspection process enhanced effectively, the pavement image quality also raised. The APDIRS using clustering techniques and classification techniques could classification the damage image and calculate pavement circumstances index effectively.
This study concludes that APDIDS can adequately increase the rate of distress detection and quality of detected images in pavement condition surveying; the APDIRS incorporated with K-means and J48 can sort out different distress types and integrate into calculating the PCI. A Pavement Distress Maintenance and Management System, or PDMMS, was established to incorporate with data from APDIRS which can provide the profile of the roadway network and chronicle data of distresses. Thus, the agencies can efficiently manage the pavement conditions among districts. | en_US |