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  • Crack detection via salient structure extraction from textured background

    Thumbnail
    Author(s)
    Nayyeri, F
    Hou, L
    Zhou, J
    Guan, H
    Liew, Alan
    Griffith University Author(s)
    Liew, Alan Wee-Chung
    Guan, Hong
    Zhou, Jun
    Year published
    2017
    Metadata
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    Abstract
    A reliable crack detection system is essential for automatic safety inspection of infrastructures such as roads and bridges. Queensland's population is increasing, putting greater pressure on our already aging civil infrastructure. To get the most out of government investment in our State's thousands of bridges, we need to find quicker, cheaper and more reliable ways to assess and maintain them. In this paper, we propose a novel method for crack detection via salient structure extraction from textured background. This method contains two key steps. In the first step, we extract strong edges and distinguish them from strong ...
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    A reliable crack detection system is essential for automatic safety inspection of infrastructures such as roads and bridges. Queensland's population is increasing, putting greater pressure on our already aging civil infrastructure. To get the most out of government investment in our State's thousands of bridges, we need to find quicker, cheaper and more reliable ways to assess and maintain them. In this paper, we propose a novel method for crack detection via salient structure extraction from textured background. This method contains two key steps. In the first step, we extract strong edges and distinguish them from strong textures in a local neighborhood via a relative total variation approach. In the second step, the spatial distribution of texture features are calculated so as to detect cracks as salient structures that are not widely spread across the whole image. The outputs from these two steps are fused to calculate the final structure saliency map which is then binarised to generate the crack masks. This method was evaluated on a crack dataset with images collected from the Internet. Comparison with several alternative approaches shows the superior performance of our method.
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    Conference Title
    SHMII 2017 - 8th International Conference on Structural Health Monitoring of Intelligent Infrastructure, Proceedings
    Publisher URI
    https://ishmii.org/events-meetings/events-meetings/shmii-8-brisbane/
    Subject
    Artificial intelligence
    Civil engineering
    Publication URI
    http://hdl.handle.net/10072/391061
    Collection
    • Conference outputs

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    • Gold Coast
    • Logan
    • Brisbane - Queensland, Australia
    First Peoples of Australia
    • Aboriginal
    • Torres Strait Islander