Boundary-Aware Refinement with Environment-Robust Adapter Tuning for Underwater Instance Segmentation

Abstract

Underwater instance segmentation is a challenging task due to adverse visual conditions such as light attenuation, scattering, and color distortion, which severely degrade image quality and hinder model performance. In this work, we propose \\textbf\{BARD-ERA\}, a unified framework that integrates three novel components to address these challenges. First, the \\textbf\{Boundary-Aware Refinement Decoder (BARDecoder)\} improves mask quality through progressive feature refinement and lightweight upsampling using a Multi-Stage Gated Refinement Network and Depthwise Separable Upsampling. Second, the \\textbf\{Environment-Robust Adapter (ERA)\} enables efficient adaptation to underwater degradations by injecting environment-specific priors with over 90% fewer trainable parameters than full fine-tuning. Third, the \\textbf\{Boundary-Aware Cross-Entropy (BACE) loss\} enhances boundary supervision by leveraging range-null space decomposition. Together, these modules achieve state-of-the-art performance on the UIIS dataset, surpassing Mask R-CNN by 3.4 mAP with Swin-B and 3.8 mAP with ConvNeXt V2-B, while maintaining a compact model size. Our results demonstrate that BARD-ERA enables robust, accurate, and efficient segmentation in complex underwater scenes.