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Research on ship based digital image processing and sea ice concentration recognition based on deep learning
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Yunhan Ma1, Xiaodong Chen1, *, Guanhui Zhao2, 3, Shunying Ji1, Haitian Yang1
Haiyang Xuebao | 2025, 47(3) : 118 - 128
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Haiyang Xuebao | 2025, 47(3): 118-128
Research on ship based digital image processing and sea ice concentration recognition based on deep learning
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Yunhan Ma1, Xiaodong Chen1, *, Guanhui Zhao2, 3, Shunying Ji1, Haitian Yang1
Affiliations
  • 1State Key Laboratory of Structural Analysis, Optimization and CAE Software for Industrial Equipment, Dalian University of Technology, Dalian 116023, China
  • 2College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
  • 3China Ship Development and Design Center, Wuhan 430064, China
Published: 2025-03-31 doi: 10.12284/hyxb2025007
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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
Yunhan Ma, Xiaodong Chen, Guanhui Zhao, Shunying Ji, Haitian Yang. Research on ship based digital image processing and sea ice concentration recognition based on deep learning[J]. Haiyang Xuebao, 2025 , 47 (3) : 118 -128 . DOI: 10.12284/hyxb2025007
Year 2025 volume 47 Issue 3
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Article Info
doi: 10.12284/hyxb2025007
  • Receive Date:2024-08-20
  • Online Date:2025-10-27
  • Published:2025-03-31
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  • Received:2024-08-20
  • Revised:2024-12-20
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Affiliations
    1State Key Laboratory of Structural Analysis, Optimization and CAE Software for Industrial Equipment, Dalian University of Technology, Dalian 116023, China
    2College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
    3China Ship Development and Design Center, Wuhan 430064, China
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表12种不同金属材料的力学参数

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Number of
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小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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