HDANet: Enhancing Underwater Salient Object Detection With Physics-Inspired Multimodal Joint Learning
Y. Liu, Xiaoyu Zhang, Jinchao Zhu, Biting Ma, Yutai Duan, Panlong Tan
Abstract
Underwater salient object detection (USOD) poses a significantly greater challenge than traditional terrestrial scenes, due to both the complex image degradation and the absence of multimodal information in underwater environments. Existing image enhancement methods are not specifically optimized for USOD, while current USOD approaches rarely consider effective extraction and utilization of multimodal information, leading to limited performance. This paper proposes HydroDepthAwareNet (HDANet), which addresses these challenges through developing targeted designs to enhance USOD performance. It first integrates a task-driven underwater image enhancement module, named HydroDepthEnhanceModule (HDEM), which is based on physical models to provide enhanced images and multimodal information optimized for USOD tasks. Furthermore, we develop a physics-inspired three-way unsupervised learning strategy, leveraging the complementary effects of re-enhancement and re-degradation to improve HDEM’s generalization across diverse underwater image degradation scenarios. Additionally, we design a robust cross-attention (RCA) module to effectively fuse multimodal features while mitigating noise and blurring by exploiting channel and spatial cross-attention mechanisms. Extensive experiments on various USOD datasets demonstrate that the proposed HDANet significantly outperforms existing state-of-the-art methods. The source code will be made available at https://github.com/mikurules/USOD-HDANet.