Open AccessHorizon detection in maritime images using scene parsing network
Author(s) -
Jeong C.Y.,
Yang H.S.,
Moon K.D.
Publication year - 2018
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2018.0989
Subject(s) - horizon , artificial intelligence , parsing , computer science , boundary (topology) , computer vision , segmentation , image (mathematics) , pixel , line (geometry) , moving horizon estimation , image segmentation , artificial neural network , pattern recognition (psychology) , kalman filter , mathematics , extended kalman filter , geometry , mathematical analysis
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.