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<p>Accurate and fast instance segmentation in remote sensing images is a long-standing challenge. Since horizontal bounding boxes (HBBs) introduce many interference objects, oriented bounding boxes (OBBs) are usually used for instance identification. However, based on “segmentation within bounding box” paradigm, current instance segmentation methods that use OBBs are overly dependent on bounding box detection performance. Recently, box prompt-based segmentation foundation models (BSMs), e.g., segment anything model (SAM), have been rapidly developed and can alleviate this dependence. However, existing BSMs are based on HBB prompts, which cannot fully leverage the capabilities of BSMs. For objects with multiple scales, dense arrangements and arbitrary orientations, HBB prompts introduce many interference areas. The current methods using BSMs with HBB prompts, such as RSPrompter, cannot meet the high-precision segmentation requirements. In this paper, we propose OBSeg, an accurate and fast instance segmentation framework using BSMs with OBB prompts. Specifically, OBSeg first detects OBBs to distinguish instances and provide coarse localization information. Then, it predicts OBB prompt-related masks for fine segmentation. In addition, to enable BSMs to handle OBB prompts, we propose a novel OBB prompt encoder. Since OBBs serve only as prompts, OBSeg alleviates the over-dependence on bounding box detection performance. Thanks to more accurate OBB prompts, OBSeg outperforms other instance segmentation methods using BSMs with HBB prompts. On the other hand, remote sensing equipment such as drones has a more urgent need for lightweight models. To make BSMs with OBB prompts more lightweight, a Gaussian smoothing-based knowledge distillation method with multi-type target supervision is further introduced. Experiments demonstrate that OBSeg significantly outperforms current instance segmentation methods on multiple datasets in terms of instance segmentation accuracy and has competitive inference speed. The code is available at <a href="https://github.com/zhen6618/OBBInstanceSegmentation">https://github.com/zhen6618/OBBInstanceSegmentation</a>.</p>

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OBSeg: Accurate and Fast Instance Segmentation Framework Using Segmentation Foundation Models with Oriented Bounding Box Prompts

  • 期刊论文

  • 发表日期：

    2026年7月31日

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  • Zhen Zhou,
  • Junfeng Fan,
  • Yunkai Ma,
  • Sihan Zhao,
  • Fengshui Jing,
  • Shuo Wang,
  • Min Tan

摘要

Accurate and fast instance segmentation in remote sensing images is a long-standing challenge. Since horizontal bounding boxes (HBBs) introduce many interference objects, oriented bounding boxes (OBBs) are usually used for instance identification. However, based on “segmentation within bounding box” paradigm, current instance segmentation methods that use OBBs are overly dependent on bounding box detection performance. Recently, box prompt-based segmentation foundation models (BSMs), e.g., segment anything model (SAM), have been rapidly developed and can alleviate this dependence. However, existing BSMs are based on HBB prompts, which cannot fully leverage the capabilities of BSMs. For objects with multiple scales, dense arrangements and arbitrary orientations, HBB prompts introduce many interference areas. The current methods using BSMs with HBB prompts, such as RSPrompter, cannot meet the high-precision segmentation requirements. In this paper, we propose OBSeg, an accurate and fast instance segmentation framework using BSMs with OBB prompts. Specifically, OBSeg first detects OBBs to distinguish instances and provide coarse localization information. Then, it predicts OBB prompt-related masks for fine segmentation. In addition, to enable BSMs to handle OBB prompts, we propose a novel OBB prompt encoder. Since OBBs serve only as prompts, OBSeg alleviates the over-dependence on bounding box detection performance. Thanks to more accurate OBB prompts, OBSeg outperforms other instance segmentation methods using BSMs with HBB prompts. On the other hand, remote sensing equipment such as drones has a more urgent need for lightweight models. To make BSMs with OBB prompts more lightweight, a Gaussian smoothing-based knowledge distillation method with multi-type target supervision is further introduced. Experiments demonstrate that OBSeg significantly outperforms current instance segmentation methods on multiple datasets in terms of instance segmentation accuracy and has competitive inference speed. The code is available at https://github.com/zhen6618/OBBInstanceSegmentation.