AquaMamba: Selective state space modeling for underwater scene segmentation in marine robotics
Year: 2026
Type: article
Abstract: Underwater scene segmentation is challenging because image degradation and domain shift vary greatly in marine environments, and the behavior of selective state-space models (SSMs) in this environment remains insufficiently characterized. We propose AquaMamba, a ResNet34-UNet segmentation framework that replaces the bottleneck operator with stacked Mamba layers, and compare it with five established CNN-based methods and a constructed Transformer-bottleneck baseline in the SUIM benchmark. AquaMamba achieves an mIoU of 0.669, 1.9 percentage points higher than the controlled Trans-UNet baseline (0.650), while using fewer parameters (29.5M vs. 33.9M). DeepLabV3 + remains the strongest overall model, reaching 0.712 mIoU. On the unlabeled UIEB dataset, AquaMamba shows the highest confidence retention among four representative models, retaining 93.5% of its source-domain prediction confidence. Due to the lack of semantic annotation in UIEB, this result reflects prediction confidence rather than target-domain segmentation accuracy. Scale- and boundary-specific diagnostics within the selected representative subset further show complementary behavior among AquaMamba, Trans-UNet and DeepLabV3 + : AquaMamba is competitive in several large object categories and obtains the lowest boundary error point estimate, while DeepLabV3 + remains stronger for human divers and plant/seagrass, and AquaMamba is limited on the reported small-RO subset. These findings position AquaMamba as a controlled SSM baseline for underwater semantic segmentation and emphasize the need for future SSM designs to better integrate multi-scale and high-resolution features.
Source: PLoS ONE
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,382
OpenAlex ID: W7225816142