Treffer: 基于边界增强和加权大核的多尺度 RGB-D 显著性目标检测.

To address the challenges of suboptimal foreground-background separation and insufficient background noise suppression in salient object detection, this paper proposed a weighted large-kernel boundary-enhanced multi-scale RGB-D salient object detection network (LKMNet). It designed a boundary-enhanced weighted large-kernel fusion module (BWLKF) to integrate boundary cues with large-kernel convolutions, enhancing foreground focus and boundary localization. In addition, it introduced a dynamic gating multi-scale fusion module (DGMF) to balance local and global features through an adaptive gating mechanism, which highlighted spatially relevant information and suppressed background interference. Experimental results on four benchmark RGB-D datasets demonstrate that LKMNet achieves higher detection accuracy compared to existing methods, confirming its superior performance in salient object detection tasks. [ABSTRACT FROM AUTHOR]

针对显著性目标检测中前景与背景分离效果不佳及背景噪声抑制不足的问题, 提出了一种加权大核边界增强多尺度 RGB-D 显著性目标检测网络 (LKMNet)。该方法通过引入边界增强加权大核融合模块 (BWLKF), 结合边界信息与加权大核卷积结构, 提升前景聚焦能力并增强边界检测精度。同时, 设计了动态门控多尺度融合模块 (DGMF), 通过自适应门控机制平衡局部与全局信息, 从而突出空间相关特征并有效抑制背景干扰。实验结果表明, 该方法在四个 RGB- D 数据集上的检测精度优于现有方法, 验证了其在显著性目标检测任务中的优越性能。 [ABSTRACT FROM AUTHOR]