Paper
18 December 2023 Rotating target detection for nearshore SAR ships based on improved YOLOv7
Kai Zhao, Ruitao Lu, Siyu Wang, Xiaogang Yang, Fangjia Lian
Author Affiliations +
Abstract
To address the problems of complex background of land buildings and islands in near-shore SAR image ship detection, dense ship docking, and thus inaccurate localization and target miss detection, we propose a YOLOv7 near-shore SAR ship rotation target detection model based on the attention mechanism and KLD improvement. Firstly, considering the lack of attention mechanism and remote dependency of YOLOv7, CA attention mechanism is added to the backbone network to improve the model context encoding capability and enhance the model accuracy. Secondly, the 3D nonreference attention mechanism SimAm is introduced to further improve the attention to ship features. Finally, the angular information is considered for the problem that the ship targets of SAR images are closely aligned in any direction. KLD is used as the localization loss function. The experimental results on the SSDD dataset show that the improved algorithm in this paper improves AP by 14.34% in near-shore scenes and the same in offshore scenes, with 2.22% improvement in all scenes relative to the original YOLOv7 model. The experimental results show that the algorithm applies to detecting ship targets in any direction in the near-shore scenes.
(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Kai Zhao, Ruitao Lu, Siyu Wang, Xiaogang Yang, and Fangjia Lian "Rotating target detection for nearshore SAR ships based on improved YOLOv7", Proc. SPIE 12960, AOPC 2023: Infrared Devices and Infrared Technology; and Terahertz Technology and Applications, 1296002 (18 December 2023); https://doi.org/10.1117/12.2692585
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KEYWORDS
Target detection

Synthetic aperture radar

Detection and tracking algorithms

Performance modeling

3D modeling

Feature extraction

Brain mapping

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