Rethinking Two-B-Real Net for Real-Time Salient Object Detection

Exploring a fast and accurate salient object detection (SOD) model is a promising research area. TBRS has been proposed a two-branch network for real-time SOD. However, its principle of adding an extra path to encode spatial information is time-consuming. And its backbone is borrowed from image classification tasks, may be inefficient for SOD due to the deficiency of task-specific design. To handle these problems, we propose a novel and efficient structure named short-range concatenate module (SRCM) by removing structure redundancy. Specifically, we gradually reduce the dimension of feature maps and use the aggregation of them for image representation, which forms the basic module of SRCM network. Moreover, we propose an efficient detail guidance branch (DBG) to further encode detail structural information in low-level stages instead of the time-consuming perceptual branch used in TBRS. Finally, low-level features and high-level features are fused by the feature projection module (FPM). Extensive evaluations and analysis demonstrate that our proposed algorithm achieves the leading accuracy performance with real-time speed (216fps). We hope that our series of works can motivate future research for real-time SOD task.