BPFINet: Boundary-aware progressive feature integration network for salient object detection

Recently, convolutional neural networks have improved the results of salient object detection by a significant margin. Most existing methods focus on aggregating multi-level features but pay little attention to the differences between their spatial resolution. Besides, the widely used binary cross entropy loss treats all pixels equally and neglects the fact that pixels near the salient boundaries are prone to being misclassified. To solve these problems, we propose a novel network named BPFINet to aggregate low-level detail features, high-level semantic information, and global information progressively by using the U-shape Feature Integration Modules (UFIMs). Moreover, a U-shape Self-Refinement Module (USRM) is proposed to generate multi-scale representation from the intra-layer features and fuse them progressively to generate features robust to scale variation of salient objects. Besides, a Channel Compression Module (CCM) is designed to reduce the channel number of certain features and enhance the features by leveraging channel-wise attention. Furthermore, an integrated loss is introduced to highlight pixels near the salient boundaries and solve the problem caused by the imbalance of foreground and background regions. Experimental results on six benchmark datasets prove that our BPFINet is competitive compared with 16 other state-of-the-art methods. The source code will be publicly available at https://github.com/clelouch/BPFINet.