Compositional Oil Spill Detection Based on Object Detector and Adapted Segment Anything Model from SAR Images

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.