  Deep-learning based salient object detection methods achieve great
improvements. However, there are still problems existing in the predictions,
such as blurry boundary and inaccurate location, which is mainly caused by
inadequate feature extraction and integration. In this paper, we propose a
Multi-scale Edge-based U-shape Network (MEUN) to integrate various features at
different scales to achieve better performance. To extract more useful
information for boundary prediction, U-shape Edge Network modules are embedded
in each decoder units. Besides, the additional down-sampling module alleviates
the location inaccuracy. Experimental results on four benchmark datasets
demonstrate the validity and reliability of the proposed method. Multi-scale
Edge based U-shape Network also shows its superiority when compared with 15
state-of-the-art salient object detection methods.
