How to effectively utilize and fuse deep features has become a critical point for salient object detection. Most existing methods usually adopt the convolutional features based on U-shape structures and fuse multi-scale convolutional features without fully considering the different characteristics between high-level features and low-level features. Furthermore, existing salient object detection methods rarely consider the role of pooling in convolutional neural networks. Moreover, there is still much room to improve the detection performance for objects in complex scenes. To address the problems mentioned above, we propose a pooling-based feature pyramid (PFP) network to boost salient object detection performance in this paper. First, we design two U-shaped feature pyramid modules to capture rich semantic information from high-level features and to obtain clear saliency boundaries from low-level features respectively. Second, a pyramid pooling refinement module is designed to utilize the pooling to capture more semantic information. Third, a universal channel-wise attention (UCA) module is designed to select effective high-level features of multi-scale and multi-receptive-field for rich semantic information, even in complex scenes. Finally, we fuse the selected high-level features and low-level features together, followed by an edge preservation loss to obtain accurate boundary location. Extensive experiments are conducted on five datasets and the experimental results indicate that our proposed method has the ability to get better salient object detection performance compared to the state-of-the-art methods.

如何有效利用和融合深层特征已成为显着物体检测的关键点。大多数现有方法通常采用基于U形结构的卷积特征并融合多尺度卷积特征，而没有充分考虑高级特征和低级特征之间的差异。此外，现有的显着物体检测方法很少考虑池在卷积神经网络中的作用。而且，对于复杂场景中的物体，仍有很大的提升空间。为了解决上述问题，我们提出了一种基于池的特征金字塔（PFP）网络来提高显着目标的检测性能。第一，我们设计了两个U形特征金字塔模块，分别从高级特征中捕获丰富的语义信息，并分别从低级特征中获得清晰的显着性边界。其次，金字塔池优化模块被设计为利用池来捕获更多的语义信息。第三，设计了通用的通道注意（UCA）模块，即使在复杂的场景中，也可以选择有效的高级多尺度和多接收域特征来获取丰富的语义信息。最后，我们将选定的高级特征和低级特征融合在一起，然后进行边缘保留损失以获取准确的边界位置。