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Journal Article Horizon Detection in Maritime Images using Scene Parsing Network
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Authors
Jeong Chi Yoon, 양현승, Moon Kyeong Deok
Issue Date
201806
Source
Electronics Letters, v.54 no.12, pp.760-762
ISSN
0013-5194
Publisher
IET
DOI
https://dx.doi.org/10.1049/el.2018.0989
Project Code
18PS1200, Development of original technology for artificial intelligence system for autonomous navigating ship , Moon Kyeong Deok
Abstract
A method for horizon detection in maritime scenes using a scene parsing network is proposed. First, each pixel from an input image is segmented into corresponding semantic categories using a scene parsing network, which relies on a deep neural network. Then, the boundary information related to the horizon and the sea is extracted. Scene segmentation allows the proposed method to identify the horizon, regardless of whether the boundary between the sea and sky is smooth or blurry, or whether the image contains many line elements like the horizon. Moreover, least squares and median filtering are iteratively used to retrieve an accurate estimation of the horizon line. Experimental results demonstrate the superior accuracy of the proposed method to identify the horizon when compared to state-of-the-art methods.
KSP Keywords
Deep neural network(DNN), Least Squares(LS), Median Filtering, Scene Parsing, Scene Segmentation, Semantic categories, accurate estimation, boundary information, horizon detection, state-of-The-Art