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

arXiv: https://arxiv.org/abs/2401.07502 · HTML: https://mlsanity.com/paper/2401.07502/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 16 references that are in the map.
- First posted on arXiv 15 Jan 2024.

## Builds on (most cited first, 4 of 4)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Segment Anything in High Quality](https://mlsanity.com/paper/2306.01567/) (2023)
- [EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything](https://mlsanity.com/paper/2312.00863/) (2023)
- [RSPrompter: Learning to Prompt for Remote Sensing Instance Segmentation based on Visual Foundation Model](https://mlsanity.com/paper/2306.16269/) (2023)

## Built on by (most cited first, 8 of 8)

- [OSDMamba: Enhancing Oil Spill Detection from Remote Sensing Images Using Selective State Space Model](https://mlsanity.com/paper/2506.18006/) (2025)
- [Diffusion-based Data Augmentation and Knowledge Distillation with Generated Soft Labels Solving Data Scarcity Problems of SAR Oil Spill Segmentation](https://mlsanity.com/paper/2412.08116/) (2024)
- [SAM2-ELNet: Label Enhancement and Automatic Annotation for Remote Sensing Segmentation](https://mlsanity.com/paper/2503.12404/) (2025)
- [SHARP-Net: A Refined Pyramid Network for Deficiency Segmentation in Culverts and Sewer Pipes](https://mlsanity.com/paper/2408.08879/) (2024)
- [From Pixels to Images: A Structural Survey of Deep Learning Paradigms in Remote Sensing Image Semantic Segmentation](https://mlsanity.com/paper/2505.15147/) (2025)
- [Beyond Segmentation: An Oil Spill Change Detection Framework Using Synthetic SAR Imagery](https://mlsanity.com/paper/2601.02139/) (2026)
- [OilSAM2: Memory-Augmented SAM2 for Scalable SAR Oil Spill Detection](https://mlsanity.com/paper/2603.10231/) (2026)
- [MambaMPD: A Mamba-Driven Segmentation Framework for Marine Pollution Detection from Remote Sensing Imagery](https://mlsanity.com/paper/2609.15676/) (2026)

## Abstract (arXiv)

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.