HySaM: An improved hybrid SAM and Mask R-CNN for underwater instance segmentation

Abstract

Due to inherent absorption and scattering effects, underwater images often exhibit low visibility and significant color deviation. These issues hinder the extraction of discriminative features and adversely impact instance-level segmentation accuracy. To address these challenges, this study proposes a novel Hybrid SAM and Mask R-CNN framework for underwater instance segmentation, integrating the strong generalization capability of SAM with the structural decoding strength of Mask R-CNN. The powerful global modeling ability of SAM effectively mitigates the impact of underwater image degradation, thereby enabling more robust feature representation. Moreover, a novel underwater feature weighted enhancer is introduced in the framework to enhance multi-scale feature fusion and improve the detection of small and scale-varying objects in underwater environments. To provide benchmark data, a large-scale underwater instance segmentation dataset, UW10K, is also constructed, comprising 13,551 images and 22,968 annotated instances across 15 categories. Comprehensive experiments validate the superiority of the proposed model across various instance segmentation tasks. Specifically, it achieves precisions of 74.2 %, 40.5 %, and 70.6 % on UW10K, USIS10K, and WHU Building datasets, respectively. This study is expected to advance ocean exploration and fisheries, while providing valuable training samples for instance segmentation tasks. Datasets and codes are available at https://github.com/xfwang-qut/HySaM.