Research on Ship based Digital Image Processing and Sea Ice Concentration Recognition Based on Deep Learning

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，， , et al. Research on Ship based Digital Image Processing and Sea Ice Concentration Recognition Based on Deep Learning[J]. Haiyang Xuebao，2025, 47(x)：1–11

Citation:

，， , et al. Research on Ship based Digital Image Processing and Sea Ice Concentration Recognition Based on Deep Learning[J]. Haiyang Xuebao，2025, 47(x)：1–11

Citation:

，， , et al. Research on Ship based Digital Image Processing and Sea Ice Concentration Recognition Based on Deep Learning[J]. Haiyang Xuebao，2025, 47(x)：1–11

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Research on Ship based Digital Image Processing and Sea Ice Concentration Recognition Based on Deep Learning

Received Date: 2024-08-20
Rev Recd Date: 2024-12-20

Available Online: 2025-01-21

Abstract

Abstract

Sea ice is a typical environmental feature of polar sea areas, and pixel-level classification of ship-borne video images can provide high-resolution sea ice information. Due to the complex light conditions and sea ice morphology in polar scenes, traditional computer graphics methods lack the generalization needed for intelligent identification of sea ice elements. Therefore, this paper deploys a deep learning approach based on the DeeplabV3+ semantic segmentation network structure to identify sea ice elements in polar scenes. The dataset consists of sea ice images captured by the icebreaker ‘Xuelong’ during its navigation in ice-covered regions, and also is used to train and validate the deep learning model. To meet the requirements of sea ice element identification and the characteristics of the underway observation video images, the pixel information is divided into four semantic categories: sea ice, sky, seawater, and ship. The deep learning model is built based on the correlation between image information and semantic information in the training set. The model trained is used to predict the semantic information of pixels in the validation set or other images, thereby achieving automatic identification of sea ice information. To study the robustness of this method, the influences of sea ice concentration, lighting conditions, and sea ice types on the identification results was further analyzed. Additionally, the effects of dataset size and the number of iterations on identification accuracy were examined. The recognition results for images show that the mean Intersection over Union (mIoU) for the four types of semantic information exceeds 95%, indicating that the deep learning method can accurately classify various elements in the polar environment.
- sea ice,
- DeeplabV3+,
- semantic segmentation,
- image recognition,
- deep learning

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References(31)

References

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