Salient Object Detection with Feature Hybrid Enhancement and Multi-loss Fusion

Abstract

To tackle the problem of missing features and poor regional consistency in existing salient object detec-tion algorithms, a salient object detection network which uses feature hybrid enhancement and multi-loss fusion based on fully convolutional neural network is proposed. The network includes a context-aware prediction module (CAPM) and a feature hybrid enhancement module (FHEM). First, the context-aware prediction module is used to extract the multi-scale feature information of the image, in which the spatial-aware module (SAM) is embedded to further extract the high-level semantic information of the image. Furthermore, the feature hybrid enhancement module is used to effectively integrate the global feature information and the detailed feature information generated by the prediction module, and the integrated feature is enhanced through embedded feature aggregation module (FAM). In addition, the multi-loss fusion method is used to supervise the network, which combines the binary cross-entropy (BCE) loss function, the structured similarity (SSIM) loss function and the proposed regional augmentation (RA) loss function. The network with the multi-loss fusion method can maintain the integrity of the foreground region and enhance the regional pixel consistency. The algorithm is verified on five image datasets with multiple salient objects and complex backgrounds. Experimental results demonstrate that the algorithm effectively improves the detection accuracy of saliency objects in complex scenes, and alleviates the problem of saliency map features missing and poor regional consistency.

Keywords

• |convolutional neural network|salient object detection|feature hybrid enhancement|regional aug-mentation loss