Fully convolutional network (FCN) based salient object detection methods have shown their advantages in highlighting salient regions because they can obtain global semantic information. And the high-level semantics are usually passed in a top-down pathway. However, the semantic information would be diluted progressively among different level features. To alleviate this issue, we propose a novel edge-aware salient object detection network. Our network utilizes high-level semantic information to assist the feature selection of shallower layers. Specifically, we extract refined features from different levels of the backbone. Then, we obtain global contextual information to locate the salient objects by extracting multi-scale features and emphasizing the important feature channels. In order to assist the shallower layers to pay attention to the learning of meaningful local information, we adopt a context guidance strategy to fuse the high-level and low-level information. Finally, we supervise the generation of low-level edge information to preserve the salient object boundaries. Extensive experiments demonstrate that the proposed mode performs favorably against most state-of-the-art methods under different evaluation metrics on six popular benchmarks.

基于全卷积网络（FCN）的显着目标检测方法已显示出其在突出显着区域方面的优势，因为它们可以获得全局语义信息。而且高级语义通常以自上而下的方式传递。但是，语义信息将在不同级别的特征之间逐渐稀释。为了缓解这个问题，我们提出了一种新颖的边缘感知显着物体检测网络。我们的网络利用高级语义信息来协助较浅层的特征选择。具体来说，我们从主干的不同级别提取精细特征。然后，通过提取多尺度特征并强调重要特征通道，获得全局上下文信息来定位显着对象。为了帮助较浅的层关注有意义的本地信息的学习，我们采用上下文指导策略来融合高级和低级信息。最后，我们监督底层边缘信息的生成，以保留显着的对象边界。大量的实验表明，在六个流行基准上，在不同的评估指标下，所提出的模式与大多数最新方法相比，具有优越的性能。