contributor author | Somin Park | |
contributor author | Francis Baek | |
contributor author | Jiu Sohn | |
contributor author | Hyoungkwan Kim | |
date accessioned | 2022-02-01T00:12:47Z | |
date available | 2022-02-01T00:12:47Z | |
date issued | 3/1/2021 | |
identifier other | %28ASCE%29CP.1943-5487.0000956.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4271087 | |
description 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. | |
publisher | ASCE | |
title | Computer Vision–Based Estimation of Flood Depth in Flooded-Vehicle Images | |
type | Journal Paper | |
journal volume | 35 | |
journal issue | 2 | |
journal title | Journal of Computing in Civil Engineering | |
identifier doi | 10.1061/(ASCE)CP.1943-5487.0000956 | |
journal fristpage | 04020072-1 | |
journal lastpage | 04020072-13 | |
page | 13 | |
tree | Journal of Computing in Civil Engineering:;2021:;Volume ( 035 ):;issue: 002 | |
contenttype | Fulltext | |