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arXiv Computer Vision By Shuaiyu Chen, Wei Han, Peng Ren, Chunbo Luo, Zeyu Fu

MambaMPD: A Mamba-Driven Segmentation Framework for Marine Pollution Detection from Remote Sensing Imagery

Read the original on arXiv Computer Vision →

MambaMPD is a new segmentation framework that leverages Vision Mamba models for marine pollution detection in remote‑sensing imagery. It introduces two structural priors—Frequency‑Aware Augmentation (FAA) and multi‑scale Edge‑Guided Attention (EGA)—to better capture low‑contrast, fragmented pollution patterns and sharpen boundaries. Experiments on the MADOS and M4D datasets show that MambaMPD outperforms existing methods in mIoU while using far less computation than foundation‑model approaches.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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