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

Source: [Compositional Oil Spill Detection Based on Object Detector and Adapted Segment Anything Model from SAR Images](<https://www.sota2.com/research/paper/68fa765c1e209131cea6c9e9>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T03:52:28.667Z

Authors: Wenhui Wu, Man Sing Wong, Xinyu Yu, Guoqiang Shi, Coco Yin Tung Kwok, Kang Zou

Published: 2024-01-15T07:03:10.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Semantic Segmentation on M4D Dataset 24 (test)](<https://www.sota2.com/research/sota/semantic-segmentation-on-m4d-dataset-24-test.md>): Semantic segmentation; M4D Dataset 24 (test); SeaSurface IoU: 96.05.

- [Oil-Spill Detection on OSCD (test)](<https://www.sota2.com/research/sota/oil-spill-detection-on-oscd-test.md>): Oil Spill Detection; OSCD (test); OSIoU: 41.84.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2401.07502>)

[PDF](<https://arxiv.org/pdf/2401.07502.pdf>)