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Li Lin

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Proceedings Article | 23 January 2024 Paper

ShipDC: a new baseline for optical remote sensing ship detection and classification

Li Lin, Zongji Zhao

Proceedings Volume 12978, 129781M (2024) https://doi.org/10.1117/12.3019841

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Recently, ship detection in satellite remote sensing images has gradually become one of the most popular research in the field of remote sensing. In maritime traffic, maritime rescue, and military operations, ship detection based on satellite remote sensing images has important application value. Optical satellite remote sensing has the characteristics of high resolution, strong detection capability, and easy for human interpretation. It has important application significance in both military and civilian fields. In order to respond to the needs of practical applications, we constructed a ship object detection dataset with the Gaofen-2 satellite images. We optimize and improve the existing deep learning network according to the characteristics of optical satellite remote sensing images, and proposes a high-resolution optical satellite remote sensing image ship object detection and classification algorithm based on YOLOv5. In addition, a channel attention module is used to improve the network's ability to represent features. We conduct experiments on the proposed dataset, and the results show that the algorithm has the ability to detect large maritime ships and to classify multicategory military ships.

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