Comprehensive-Detail Synergy with Multi-Level Dynamic Interaction for Enhanced Salient Object Detection

Abstract

Salient Object Detection involves identifying and segmenting the most visually distinctive objects in an image. A key challenge is distinguishing Salient objects from complex backgrounds while preserving global features and minimizing local detail loss. To address this issue, we introduce a Comprehensive-Detail Synergy with Multi-Level Dynamic Interaction for Enhanced Salient Object Detection aimed at enhancing salient object features. Initially, a Multi-Scale Pooling Self-Attention Module is introduced to capture global contextual information of salient objects by combining multi-scale max pooling across spatial dimensions with self-attention. Additionally, to better preserve local details, an Adaptive Channel Enhancement Block is proposed, utilizing an adaptive weighting strategy to prioritize salient channels and enhance the model’s ability to capture intricate local features. Furthermore, to enhance the interaction between features at different levels, a Multi-Level Diffusive Synergy Block is introduced. With the integration of the cross-attention and dynamic diffusion refinement mechanism, it enables deep features to guide shallow features in focusing on salient regions. To alleviate the loss of local details due to excessive deep feature guidance, a Dual-Domain Fusion Attention Module is proposed, which integrates global self-attention with locally enhanced feature extraction units, thereby balancing global context modeling and local detail preservation. The experimental results conducted on six challenging publicly available datasets demonstrate that the proposed method outperforms the state of the art, achieving improvements of 4.9%, 3.3%, 2.4%, 2.3%, 1.2%, and 7.2% in the Weighted Harmonic Mean of Precision and Recall. These results demonstrate that the method improves accuracy and boundary detail.