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    Computer Vision–Based Estimation of Flood Depth in Flooded-Vehicle Images

    Source: Journal of Computing in Civil Engineering:;2021:;Volume ( 035 ):;issue: 002::page 04020072-1
    Author:
    Somin Park
    ,
    Francis Baek
    ,
    Jiu Sohn
    ,
    Hyoungkwan Kim
    DOI: 10.1061/(ASCE)CP.1943-5487.0000956
    Publisher: ASCE
    Abstract: This study proposes a vision-based method for flood depth estimation using flooded-vehicle images with a ground-level view. The proposed method is comprised of three main processes: segmentation of vehicle objects, cross-domain image retrieval, and estimation of flood depth. First, Mask region-based convolution neural network (R-CNN) is used to detect flooded vehicles in flooding images. Second, on the basis of feature maps from VGGNets, dynamic feature space selection is employed to select a three-dimensional (3D) rendered car image most similar to the flooded object using the metric of cosine distance. Finally, the flood depth is calculated through a comparison of the flooded object and the 3D rendered image. The feature maps from Pooling layer 4 of VGG19, under the condition of a cosine distance of <0.55, produces an average error of 7.51 pixels, corresponding to 9.40% of the tire height. A total of 500 flooding images are used to validate the method.
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      Computer Vision&#x2013;Based Estimation of Flood Depth in Flooded-Vehicle Images

    URI
    http://yetl.yabesh.ir/yetl1/handle/yetl/4271087
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    • Journal of Computing in Civil Engineering

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    contributor authorSomin Park
    contributor authorFrancis Baek
    contributor authorJiu Sohn
    contributor authorHyoungkwan Kim
    date accessioned2022-02-01T00:12:47Z
    date available2022-02-01T00:12:47Z
    date issued3/1/2021
    identifier other%28ASCE%29CP.1943-5487.0000956.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4271087
    description abstractThis study proposes a vision-based method for flood depth estimation using flooded-vehicle images with a ground-level view. The proposed method is comprised of three main processes: segmentation of vehicle objects, cross-domain image retrieval, and estimation of flood depth. First, Mask region-based convolution neural network (R-CNN) is used to detect flooded vehicles in flooding images. Second, on the basis of feature maps from VGGNets, dynamic feature space selection is employed to select a three-dimensional (3D) rendered car image most similar to the flooded object using the metric of cosine distance. Finally, the flood depth is calculated through a comparison of the flooded object and the 3D rendered image. The feature maps from Pooling layer 4 of VGG19, under the condition of a cosine distance of <0.55, produces an average error of 7.51 pixels, corresponding to 9.40% of the tire height. A total of 500 flooding images are used to validate the method.
    publisherASCE
    titleComputer Vision–Based Estimation of Flood Depth in Flooded-Vehicle Images
    typeJournal Paper
    journal volume35
    journal issue2
    journal titleJournal of Computing in Civil Engineering
    identifier doi10.1061/(ASCE)CP.1943-5487.0000956
    journal fristpage04020072-1
    journal lastpage04020072-13
    page13
    treeJournal of Computing in Civil Engineering:;2021:;Volume ( 035 ):;issue: 002
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
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