Semantic-Aware Ship Detection with Vision-Language Integration

• URL: http://arxiv.org/abs/2508.15930v1
• Date: Thu, 21 Aug 2025 19:24:52 GMT
• Title: Semantic-Aware Ship Detection with Vision-Language Integration
• Authors: Jiahao Li, Jiancheng Pan, Yuze Sun, Xiaomeng Huang,
• Abstract summary: Ship detection in remote sensing imagery is a critical task with wide-ranging applications, such as maritime activity monitoring, shipping logistics, and environmental studies.<n>We propose a novel detection framework that combines Vision-Language Models (VLMs) with a multi-scale adaptive sliding window strategy.<n>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.
• Score: 9.49989812166076
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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