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An Improved YOLOv8-Based Detection Model for Multi-Scale Sea Ice in Satellite Imagery
Research Article | Published: 29 March 2025
Chinese Journal of Information Fusion Volume 2, Issue 1, 2025: 79-99
Volume 2, Issue 1, Chinese Journal of Information Fusion
Volume 2, Issue 1, 2025
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Fengbao Yang
Fengbao Yang
North University of China, China
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Chinese Journal of Information Fusion, Volume 2, Issue 1, 2025: 79-99

Open Access | Research Article | 29 March 2025
An Improved YOLOv8-Based Detection Model for Multi-Scale Sea Ice in Satellite Imagery
by
Yang Liu Yang Liu 1
Qiang Guo Qiang Guo 1 *
Chengguo Dong Chengguo Dong 2
Yiping Luo Yiping Luo 3
1 School of Computer and Control Engineering, Yantai University, Yantai 264005, China
2 School of Architectural Engineering, Weifang University of Science and Technology, Weifang 261000, China
3 Deep Space Exploration Laboratory, Hefei 230000, China
* Corresponding Author: Qiang Guo, [email protected]
DOI: 10.62762/CJIF.2025.695812
Received: 05 March 2025, Accepted: 23 March 2025, Published: 29 March 2025  
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Abstract
Sea ice detection is of vital importance for maritime navigation. Satellite imagery is a crucial medium for conveying information about sea ice. Currently, most sea ice detection models mainly rely on texture information to identify sea ice in satellite imagery, while ignoring sea ice size information. This research presents an improved YOLOv8-Based detection algorithm for multi-scale sea ice. First, we propose a fusion module based on the attention mechanism and use it to replace the Concat module in the YOLOv8 network structure. Second, we conduct an applicability analysis of the bounding box regression loss function in YOLOv8 and ultimately select Shape-IoU, which is more suitable for sea ice, as the loss function for bounding box regression. Third, we analyze the distribution characteristics of sea ice with different sizes in the NWPU-RESISC45 dataset. Based on these distribution characteristics, the bounding box information predicted by YOLOv8 are converted into evidence vectors for uncertainty quantification. Subsequently, information fusion is achieved by fusing these vectors with the probability of sea ice categories. Compared to YOLOv8 and other detection algorithm, our improved YOLOv8 achieves better detection accuracy on both the NWPU-RESISC45 and the Landsat-8-derived Sea Ice datasets.
Graphical Abstract
An Improved YOLOv8-Based Detection Model for Multi-Scale Sea Ice in Satellite Imagery
Keywords
satellite imagery
YOLO
attention mechanism
loss function
information fusion
evidential reasoning
Data Availability Statement
The datasets used in this work include: (1) the publicly available NWPU-RESISC45 remote sensing benchmark dataset, accessible via Baidu Wangpan at http://pan.baidu.com/s/1mifR6tU; and (2) our Landsat-8-derived Sea Ice detection dataset processed from Landsat 8 OLI/TIRS imagery, which has been made publicly available on GitHub at https://github.com/LiuYang0911/A-Proprietary-Visible-Light-based-Sea-Ice-Dataset.
Funding
This work was supported in part by the National Natural Science Foundation of China under Grant 62072392 and Grant 62272405; in part by the Shandong Natural Science Foundation of China under Grant ZR2020QF010; in part by the Yantai City Science and Technology Innovation Development Program - Basic Research Category Projects under Grant 2024JCYJ038.
Conflicts of Interest
Yiping Luo is an employee of Deep Space Exploration Laboratory, Hefei 230000, China.
Ethical Approval and Consent to Participate
Not applicable.
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APA Style
Liu, Y., Guo, Q., Dong, C., & Luo, Y. (2025). An Improved YOLOv8-Based Detection Model for Multi-Scale Sea Ice in Satellite Imagery. Chinese Journal of Information Fusion, 2(1), 79–99. https://doi.org/10.62762/CJIF.2025.695812
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