O2Former:Direction-Aware and Multi-Scale Query Enhancement for SAR Ship Instance Segmentation
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911005320151040 |
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| author | Gao, F. Li, Y He, X Sun, J Wang, J |
| author_facet | Gao, F. Li, Y He, X Sun, J Wang, J |
| contents | Instance segmentation of ships in synthetic aperture radar (SAR) imagery is critical for applications such as maritime monitoring, environmental analysis, and national security. SAR ship images present challenges including scale variation, object density, and fuzzy target boundary, which are often overlooked in existing methods, leading to suboptimal performance. In this work, we propose O2Former, a tailored instance segmentation framework that extends Mask2Former by fully leveraging the structural characteristics of SAR imagery. We introduce two key components. The first is the Optimized Query Generator(OQG). It enables multi-scale feature interaction by jointly encoding shallow positional cues and high-level semantic information. This improves query quality and convergence efficiency. The second component is the Orientation-Aware Embedding Module(OAEM). It enhances directional sensitivity through direction-aware convolution and polar-coordinate encoding. This effectively addresses the challenge of uneven target orientations in SAR scenes. Together, these modules facilitate precise feature alignment from backbone to decoder and strengthen the model's capacity to capture fine-grained structural details. Extensive experiments demonstrate that O2Former outperforms state of the art instance segmentation baselines, validating its effectiveness and generalization on SAR ship datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_11913 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | O2Former:Direction-Aware and Multi-Scale Query Enhancement for SAR Ship Instance Segmentation Gao, F. Li, Y He, X Sun, J Wang, J Computer Vision and Pattern Recognition Instance segmentation of ships in synthetic aperture radar (SAR) imagery is critical for applications such as maritime monitoring, environmental analysis, and national security. SAR ship images present challenges including scale variation, object density, and fuzzy target boundary, which are often overlooked in existing methods, leading to suboptimal performance. In this work, we propose O2Former, a tailored instance segmentation framework that extends Mask2Former by fully leveraging the structural characteristics of SAR imagery. We introduce two key components. The first is the Optimized Query Generator(OQG). It enables multi-scale feature interaction by jointly encoding shallow positional cues and high-level semantic information. This improves query quality and convergence efficiency. The second component is the Orientation-Aware Embedding Module(OAEM). It enhances directional sensitivity through direction-aware convolution and polar-coordinate encoding. This effectively addresses the challenge of uneven target orientations in SAR scenes. Together, these modules facilitate precise feature alignment from backbone to decoder and strengthen the model's capacity to capture fine-grained structural details. Extensive experiments demonstrate that O2Former outperforms state of the art instance segmentation baselines, validating its effectiveness and generalization on SAR ship datasets. |
| title | O2Former:Direction-Aware and Multi-Scale Query Enhancement for SAR Ship Instance Segmentation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.11913 |