Compositional Oil Spill Detection Based on Object Detector and Adapted Segment Anything Model from SAR Images
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866929642882990080 |
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| author | Wu, Wenhui Wong, Man Sing Yu, Xinyu Shi, Guoqiang Kwok, Coco Yin Tung Zou, Kang |
| author_facet | Wu, Wenhui Wong, Man Sing Yu, Xinyu Shi, Guoqiang Kwok, Coco Yin Tung Zou, Kang |
| contents | Semantic segmentation-based methods have attracted extensive attention in oil spill detection from SAR images. However, the existing approaches require a large number of finely annotated segmentation samples in the training stage. To alleviate this issue, we propose a composite oil spill detection framework, SAM-OIL, comprising an object detector (e.g., YOLOv8), an Adapted Segment Anything Model (SAM), and an Ordered Mask Fusion (OMF) module. SAM-OIL is the first application of the powerful SAM in oil spill detection. Specifically, the SAM-OIL strategy uses YOLOv8 to obtain the categories and bounding boxes of oil spill-related objects, then inputs bounding boxes into the Adapted SAM to retrieve category-agnostic masks, and finally adopts the OMF module to fuse the masks and categories. The Adapted SAM, combining a frozen SAM with a learnable Adapter module, can enhance SAM's ability to segment ambiguous objects. The OMF module, a parameter-free method, can effectively resolve pixel category conflicts within SAM. Experimental results demonstrate that SAM-OIL surpasses existing semantic segmentation-based oil spill detection methods, achieving mIoU of 69.52\%. The results also indicated that both OMF and Adapter modules can effectively improve the accuracy in SAM-OIL. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_07502 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Compositional Oil Spill Detection Based on Object Detector and Adapted Segment Anything Model from SAR Images Wu, Wenhui Wong, Man Sing Yu, Xinyu Shi, Guoqiang Kwok, Coco Yin Tung Zou, Kang Computer Vision and Pattern Recognition Semantic segmentation-based methods have attracted extensive attention in oil spill detection from SAR images. However, the existing approaches require a large number of finely annotated segmentation samples in the training stage. To alleviate this issue, we propose a composite oil spill detection framework, SAM-OIL, comprising an object detector (e.g., YOLOv8), an Adapted Segment Anything Model (SAM), and an Ordered Mask Fusion (OMF) module. SAM-OIL is the first application of the powerful SAM in oil spill detection. Specifically, the SAM-OIL strategy uses YOLOv8 to obtain the categories and bounding boxes of oil spill-related objects, then inputs bounding boxes into the Adapted SAM to retrieve category-agnostic masks, and finally adopts the OMF module to fuse the masks and categories. The Adapted SAM, combining a frozen SAM with a learnable Adapter module, can enhance SAM's ability to segment ambiguous objects. The OMF module, a parameter-free method, can effectively resolve pixel category conflicts within SAM. Experimental results demonstrate that SAM-OIL surpasses existing semantic segmentation-based oil spill detection methods, achieving mIoU of 69.52\%. The results also indicated that both OMF and Adapter modules can effectively improve the accuracy in SAM-OIL. |
| title | Compositional Oil Spill Detection Based on Object Detector and Adapted Segment Anything Model from SAR Images |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2401.07502 |