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IEICE TRANSACTIONS on Information

Coarse-to-Fine Evolutionary Method for Fast Horizon Detection in Maritime Images

Uuganbayar GANBOLD, Junya SATO, Takuya AKASHI

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Summary :

Horizon detection is useful in maritime image processing for various purposes, such as estimation of camera orientation, registration of consecutive frames, and restriction of the object search region. Existing horizon detection methods are based on edge extraction. For accuracy, they use multiple images, which are filtered with different filter sizes. However, this increases the processing time. In addition, these methods are not robust to blurting. Therefore, we developed a horizon detection method without extracting the candidates from the edge information by formulating the horizon detection problem as a global optimization problem. A horizon line in an image plane was represented by two parameters, which were optimized by an evolutionary algorithm (genetic algorithm). Thus, the local and global features of a horizon were concurrently utilized in the optimization process, which was accelerated by applying a coarse-to-fine strategy. As a result, we could detect the horizon line on high-resolution maritime images in about 50ms. The performance of the proposed method was tested on 49 videos of the Singapore marine dataset and the Buoy dataset, which contain over 16000 frames under different scenarios. Experimental results show that the proposed method can achieve higher accuracy than state-of-the-art methods.

Publication
IEICE TRANSACTIONS on Information Vol.E104-D No.12 pp.2226-2236
Publication Date
2021/12/01
Publicized
2021/09/08
Online ISSN
1745-1361
DOI
10.1587/transinf.2021EDP7064
Type of Manuscript
PAPER
Category
Image Recognition, Computer Vision

Authors

Uuganbayar GANBOLD
  Iwate University
Junya SATO
  Gifu University
Takuya AKASHI
  Iwate University

Keyword