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  • Automatic Bridge Crack Detection - A Texture Analysis-Based Approach

    Author(s)
    Chanda, S
    Bu, G
    Guan, H
    Jo, J
    Pal, U
    Loo, YC
    Blumenstein, M
    Griffith University Author(s)
    Loo, Yew-Chaye
    Guan, Hong
    Jo, Jun
    Year published
    2014
    Metadata
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    Abstract
    To date, identifying cracks in bridges and determining bridge conditions primarily involve manual labour. Bridge inspection by human experts has some drawbacks such as the inability to physically examine all parts of the bridge, sole dependency on the expert knowledge of the bridge inspector. Moreover it requires proper training of the human resource and overall it is not cost effective. This article proposes an automatic bridge inspection approach exploiting wavelet-based image features along with Support Vector Machines for automatic detection of cracks in bridge images. A two-stage approach is followed, where in the first ...
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    To date, identifying cracks in bridges and determining bridge conditions primarily involve manual labour. Bridge inspection by human experts has some drawbacks such as the inability to physically examine all parts of the bridge, sole dependency on the expert knowledge of the bridge inspector. Moreover it requires proper training of the human resource and overall it is not cost effective. This article proposes an automatic bridge inspection approach exploiting wavelet-based image features along with Support Vector Machines for automatic detection of cracks in bridge images. A two-stage approach is followed, where in the first stage a decision is made as whether an image should undergo a pre-processing step (depending on image characteristics), and later in the second stage, wavelet features are extracted from the image using a sliding window-based technique. We obtained an overall accuracy of 92.11% while conducting experiments even on noisy and complex bridge images.
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    Conference Title
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume
    8774
    Publisher URI
    http://www.annpr2014.com/
    DOI
    https://doi.org/10.1007/978-3-319-11656-3_18
    Subject
    Image processing
    Publication URI
    http://hdl.handle.net/10072/68381
    Collection
    • Conference outputs

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