基于通道权重的顺序精炼RGB-D显著检测网络

提出了一种新型的用于RGB-D显著目标检测的网络框架（SR-Net）。为了有效整合多模态特征的互补性，将深度特征提取作为独立分支，采用卷积块注意模块（CBAM）进行深度特征增强，并整合增强后的深度特征与RGB 特征的互补信息。为了去除冗余特征，减少背景噪声对预测结果的干扰，在上采样网络中设计了一种顺序精炼网络，即通过整合多层次、多尺度特征的互补性，获取初级全局特征，并采用基于通道权重的初级全局特征权重矩阵获取模块（PFW）获取初级全局特征的权重矩阵；其次利用获取到的权重矩阵对各层次特征进行精炼，以抑制背景噪声带来的干扰；最后，为了更好地优化整个网络，提出了一种新的损失函数。在四个公共数据集上的实验结果表明，该模型在不同的模型评价指标上均优于近年来九种先进方法，获得了优异的性能。

Sequential refined RGB-D saliency detection network based on channel weight

This paper proposed a new network framework for RGB-D salient object detection(SR-Net). In order to effectively integrate the complementarity of multi-model features, this paper took the depth feature extraction as an independent branch, used the convolutional block attention module(CBAM) to enhance the depth feature, and integrated the complementary information of the enhanced depth feature and RGB feature. Then, in order to remove feature redundancy and reduce the interference of background noise on the prediction results, it proposed a sequential refining network in the up-sampling network. That is, first, the primary global features were obtained by integrating the complementarity of multi-level and multi-scale features, and used the primary global feature weight matrix acquisition module(PFW) which based on the channel weight to obtain the weight matrix of the primary global feature, and then used the obtained weight matrix to refine the features of each level to suppress the interference which caused by background noise. Finally, in order to better optimize the whole network, it proposed a new loss function. The experimental results on four public datasets show that the model is superior to nine advanced methods in different model evaluation indexes, and achieves more advanced performance.