Multi-scale Edge-based U-shape Network for Salient Object Detection
This work addresses boundary and location accuracy issues in salient object detection, which is important for applications like image segmentation, but it appears incremental as it builds on existing U-shape architectures with added modules.
The paper tackles the problem of blurry boundaries and inaccurate location in salient object detection by proposing a Multi-scale Edge-based U-shape Network (MEUN), which integrates multi-scale features and embeds edge modules, achieving superior performance compared to 15 state-of-the-art methods on four benchmark datasets.
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.