Non-binary IoU and progressive coupling and refining network for salient object detection

Recently, many salient object detection (SOD) methods decouple image features into body features and edge features, which imply a new development direction in the field of SOD. Most of them mainly focus on how to decouple features, but the fusion method for the decoupled features can be further improved. In this paper, we propose a network, namely Progressive Coupling and Refining Network (PCRNet), which allows the progressive coupling and refining of the decoupled features to get accurate salient features. Furthermore, a novel loss, namely Non-Binary Intersection over Union (NBIoU), is proposed based on the characteristics of non-binary label images and the principle of Intersection over Union (IoU) loss. Experimental results show that our NBIoU performance surpasses binary cross-entropy (BCE), IoU and Dice on non-binary label images. The results on five popular SOD benchmark datasets show that our PCRNet significantly exceeds the previous state-of-the-art (SOTA) methods on multiple metrics. In addition, although our method is designed for SOD, it is comparable with previous SOTA methods on multiple benchmark datasets for camouflaged object detection without any modification on the network structure, verified the robustness of the proposed method.