01697467

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With the rapid development of national economy and the continuous expediting of urbanization the traffic demand is increasing quickly. However, the heavy traffic flow brings huge pressure on highway pavement and may result in the ruting disease, which is the leading cause of pavement diseases. Thus, it is necessary to proposed the efficient and accurate methods to detect rut depth, and provide reliable disease data for pavement maintenance. Regarding to detecting methods, most of the studies has focused on how to improve the overall processing procedure to reduce the measurement error. The effects of special scenes that occur during the detection process, such as traffic markings under strong light, remain unknown and undetected. To fill this gap, this study proposed a line-structured rut detection method to improve the detecting accurate of rut depth. A fast gray scale correction algorithm and a fusion segmentation algorithm are mainly used to mitigating the effect of traffic markings under strong light. A total number of 1320 pavement images collected from Cao’an Highway in Shanghai are used to verify the proposed rut detection method with traffic markings interference under strong light. Experimental results verify that the proposed method is able to provide accurate rut depth detection results of pavement with traffic markings under strong light within a relatively short execution time.

This paper was sponsored by TRB committee AFD20 Standing Committee on Pavement Condition Evaluation.

19-01641

English

8p

2019

Transportation Research Board 98th Annual Meeting

Digital/other

Figures; References; Tables

Asphalt pavements; Detection and identification; Light; Ruts (Pavements); Traffic control devices

Shanghai (China)

Highways; Maintenance and Preservation; Pavements

Transportation Research Board Annual Meeting 2019 Paper #19-01641

TRIS, TRB, ATRI

Dec 7 2018 9:28AM