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Pavement distress detection and classification with automated image processing

Authors: null Xiaoming Wang; null Xin Feng;

Pavement distress detection and classification with automated image processing

Abstract

Nowadays, pavement distresses classification becomes more important as the computational power increases. In this article, an enhancement algorithm is initially introduced to remove uneven illumination within the pavement images. This proposed method which uses a multiplicative factor to correct the background illumination, after that use shearlet frame to filtering the pavement images. In the classification of operation, we used Radon transform for line and angle detection about pavement distress of binary images, and use scattering distance to verify the result of classification by the texture feature of pavement distress images. Experimental results demonstrate that the proposed system is an effective method for pavement distress classification. The test performances of this study show the advantages of proposed system:have simple calculation, and have High precision.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
2
Average
Average
Average
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