Semantic-Aware Ship Detection with Vision-Language Integration
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912548414029824 |
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| author | Li, Jiahao Pan, Jiancheng Sun, Yuze Huang, Xiaomeng |
| author_facet | Li, Jiahao Pan, Jiancheng Sun, Yuze Huang, Xiaomeng |
| contents | Ship detection in remote sensing imagery is a critical task with wide-ranging applications, such as maritime activity monitoring, shipping logistics, and environmental studies. However, existing methods often struggle to capture fine-grained semantic information, limiting their effectiveness in complex scenarios. To address these challenges, we propose a novel detection framework that combines Vision-Language Models (VLMs) with a multi-scale adaptive sliding window strategy. To facilitate Semantic-Aware Ship Detection (SASD), we introduce ShipSem-VL, a specialized Vision-Language dataset designed to capture fine-grained ship attributes. We evaluate our framework through three well-defined tasks, providing a comprehensive analysis of its performance and demonstrating its effectiveness in advancing SASD from multiple perspectives. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_15930 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Semantic-Aware Ship Detection with Vision-Language Integration Li, Jiahao Pan, Jiancheng Sun, Yuze Huang, Xiaomeng Computer Vision and Pattern Recognition Ship detection in remote sensing imagery is a critical task with wide-ranging applications, such as maritime activity monitoring, shipping logistics, and environmental studies. However, existing methods often struggle to capture fine-grained semantic information, limiting their effectiveness in complex scenarios. To address these challenges, we propose a novel detection framework that combines Vision-Language Models (VLMs) with a multi-scale adaptive sliding window strategy. To facilitate Semantic-Aware Ship Detection (SASD), we introduce ShipSem-VL, a specialized Vision-Language dataset designed to capture fine-grained ship attributes. We evaluate our framework through three well-defined tasks, providing a comprehensive analysis of its performance and demonstrating its effectiveness in advancing SASD from multiple perspectives. |
| title | Semantic-Aware Ship Detection with Vision-Language Integration |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.15930 |