EDGE-Net: an edge-guided enhanced network for RGB-T salient object detection

With similar textures, dark backgrounds, and complex scenes, RGB images are usually unable to provide discriminative information for model training, which often leads to inaccurate prediction results. Compared with RGB salient object detection (SOD) methods, RGB-T SOD have thermal infrared (TIR) information as an informational supplement. As a result, RGB-T SOD can adapt to more complex environments and achieve better results. However, existing methods do not efficiently integrate features between different modalities and do not fully exploit spatial information from the shallow-level features. Accordingly, we propose an EDGE-Net. First, we propose an edge extraction module to capture the edge information in the shallow-level features and use it to guide the subsequent decoding. The original edge features are then weighted after channel attention processing. Second, to additionally suppress the noise of the shallow-level features, we design a global information extraction module. In this module, multiple convolutions are used instead of single convolutions to reduce the computational effort, and convolutions with different dilation rates are used to obtain different receptive fields. We conduct extensive experiments on the RGB-T dataset and show that the proposed method achieves superior performance compared to several state-of-the-art algorithms. The code and results of our method are available in a Github repository available at: https://github.com/BorreloadD/EDGE-Net.git.