期刊
出版社
ELSEVIER
DOI: 10.1016/j.jag.2022.102885
关键词
Semantic sea -ice image segmentation; Deep convolutional neural networks; Multi -scale features; Attention module
资金
- National Key Research and Development Program of China [2018YFA0605902, 2016YCF1401505]
- National Natural Science Foundation of China [41576179, 51639004]
- Fundamental Research Funds for the Central Universities [DUT21LK03]
- Science and Technology Innovation Fundation of Dalian [2021JJ13SN80]
This paper proposes a new network model Ice-Deeplab for sea ice image segmentation, which is demonstrated to achieve better segmentation results than the original Deeplab model in various validation scenarios. Moreover, the proposed model demonstrates good generalization ability.
An accurate algorithm for sea ice segmentation is critical for monitoring sea ice parameters of ship navigation in ice-covered seas, as it can automatically extract ice objects and corresponding information to compute essential parameters such as surface ice concentration and ice floe size. In this paper, based on digital images captured by onboard cameras, a novel network called Ice-Deeplab for pixel-wise ice image segmentation is proposed. The Ice-Deeplab network is constructed using the deep convolutional neural network Deeplab and is modified with an attention module and an improved decoding structure. To investigate its reliability, the Ice-Deeplab network is applied to a 320-image dataset, with 80% for training and 20% for validation. The experiments demonstrated that the proposed Ice-Deeplab yields better segmentation results than the original Deeplab model under different validation scenarios, achieving an overall accuracy of 90.5% among the classes sea-ice, ocean, and sky. More-over, the proposed model was applied to un-labelled test data to demonstrate its generalisation ability for real-time ice segmentation.
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