School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China; Key Laboratory of Intelligent Rehabilitation and Neromodulation of Hebei Province, Yanshan University, Qinhuangdao, Hebei 066004, China; Key Laboratory of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao, Hebei 066004, China.
School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China.
Neural Netw. 2024 Dec;180:106751. doi: 10.1016/j.neunet.2024.106751. Epub 2024 Sep 24.
Though depth images can provide supplementary spatial structural cues for salient object detection (SOD) task, inappropriate utilization of depth features may introduce noisy or misleading features, which may greatly destroy SOD performance. To address this issue, we propose a depth mask guiding network (DMGNet) for RGB-D SOD. In this network, a depth mask guidance module (DMGM) is designed to pre-segment the salient objects from depth images and then create masks using pre-segmented objects to guide the RGB subnetwork to extract more discriminative features. Furthermore, a feature fusion pyramid module (FFPM) is employed to acquire more informative fused features using multi-branch convolutional channels with varying receptive fields, further enhancing the fusion of cross-modal features. Extensive experiments on nine benchmark datasets demonstrate the effectiveness of the proposed network.
虽然深度图像可为显著目标检测（SOD）任务提供补充的空间结构线索，但深度特征的不当利用可能会引入噪声或误导性特征，从而极大地破坏 SOD 的性能。为了解决这个问题，我们提出了一种用于 RGB-D SOD 的深度掩模引导网络（DMGNet）。在该网络中，设计了一个深度掩模引导模块（DMGM），用于从深度图像中预分割显著目标，然后使用预分割的对象创建掩模，以引导 RGB 子网提取更具判别力的特征。此外，采用特征融合金字塔模块（FFPM），利用多分支卷积通道和不同的感受野，获取更多信息丰富的融合特征，进一步增强了跨模态特征的融合。在九个基准数据集上的广泛实验证明了所提出的网络的有效性。
