At present, most saliency detection methods are based on fully convolutional neural networks (FCNs). However, FCNs usually blur the edges of salient objects. Due to that, the multiple convolution and pooling operations of the FCNs will limit the spatial resolution of the feature maps. To alleviate this issue and obtain accurate edges, we propose a hierarchical edge refinement network (HERNet) for accurate saliency detection. In detail, the HERNet is mainly composed of a saliency prediction network and an edge preserving network. Firstly, the saliency prediction network is used to roughly detect the regions of salient objects and is based on a modified U-Net structure. Then, the edge preserving network is used to accurately detect the edges of salient objects, and this network is mainly composed of the atrous spatial pyramid pooling (ASPP) module. Different from the previous indiscriminate supervision strategy, we adopt a new one-to-one hierarchical supervision strategy to supervise the different outputs of the entire network. Experimental results on five traditional benchmark datasets demonstrate that the proposed HERNet performs well when compared with the state-of-the-art methods.

中文翻译：

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用于显着性检测的分层边缘细化网络


目前，大多数显着性检测方法都是基于全卷积神经网络（FCN）。然而，FCN 通常会模糊显着对象的边缘。因此，FCN 的多重卷积和池化操作将限制特征图的空间分辨率。为了缓解这个问题并获得准确的边缘，我们提出了一种分层边缘细化网络（HERNet）来进行准确的显着性检测。具体来说，HERNet主要由显着性预测网络和边缘保持网络组成。首先，显着性预测网络用于粗略地检测显着性对象的区域，并且基于改进的 U-Net 结构。然后，使用边缘保留网络来准确检测显着对象的边缘，该网络主要由多孔空间金字塔池化（ASPP）模块组成。与之前的无差别监督策略不同，我们采用了一种新的一对一的分层监督策略来监督整个网络的不同输出。五个传统基准数据集上的实验结果表明，与最先进的方法相比，所提出的 HERNet 表现良好。