Compositional Oil Spill Detection Based on Object Detector and Adapted
  Segment Anything Model from SAR Images
Wenhui Wu Man Sing Wong Xinyu Yu Guoqiang Shi Coco Yin Tung Kwok Kang Zou
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 Ordered Mask Fusion
(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.