PSNet: Parallel symmetric network for RGB-T salient object detection

Abstract

RGB-Thermal (RGB-T) salient object detection (SOD) aims to utilize RGB and thermal infrared data to discover the most salient object(s) in an image. Although SOD has achieved great progress, few efforts are devoted to RGB-T SOD. For this task, on the one hand, it is worth paying attention to how to aggregate critical saliency cues from both modalities to enhance salient feature representation and thus produce accurate salient object detection results. On the other hand, effective multi-level feature fusion remains a big challenge. To overcome the above mentioned issues, this paper proposes a Parallel Symmetric Network (PSNet) for RGB-T salient object detection. Specifically, we first develop a cascaded aggregation module (CAM), which fully accumulates and excavates the valuable saliency semantics from two different modalities to strengthen feature representation by cascading the designed residual-based enhancement unit. Then, we design a parallel-symmetric fusion (PSF) module to integrate crucial saliency cues from adjacent layers for saliency prediction in a parallel and symmetric manner. Besides, to make full use of multi-level features, we introduce a guidance strategy that enhances the details of the saliency map with the low-level features to boost the performance of salient object detection. Extensive experiments show that our proposed model significantly outperforms the existing fifteen state-of-the-art models on three challenging benchmark datasets. Moreover, the superior performance on RGB-D SOD also demonstrates the generalization and robustness of the proposed method. The source code will be made publicly available soon.