Underwater multi-sensor information fusion for salient object detection.

Abstract

Underwater salient object detection is a critical task in computer vision, relying heavily on data from underwater sensors, with wide-ranging applications in object tracking, content-aware editing, and object recognition. To tackle the challenges inherent in underwater multimodal information fusion, this paper introduces a novel underwater salient object detection framework based on an information cross-fusion network. The proposed approach integrates a cross-attention feature injection module and an information embedding module to facilitate efficient multimodal feature aggregation and refinement across both channel and spatial dimensions. By modeling the complementarity between RGB and depth data at global and local scales, these modules enhance the representation of salient regions while effectively suppressing background noise. Furthermore, the architecture employs multi-level and multimodal information fusion, which mitigates the effects of depth-related noise and reduces uncertainty in predictions. Extensive experiments conducted on multiple underwater datasets demonstrate that the proposed method achieves superior performance compared to state-of-the-art approaches, highlighting its efficacy in multimodal feature integration and salient object detection.