EAF-Net: an enhancement and aggregation–feedback network for RGB-T salient object detection

Salient object detection (SOD) aims at highlighting important foreground objects automatically from the background. Most existing SOD methods only employ visible images (RGB images) for salient detection, which limits the performance of real-life applications when encountering challenging scenarios such as low illumination, haze, and smog. In this paper, we take advantage of the RGB and thermal images and propose an Enhancement and Aggregation–Feedback Network (EAF-Net) for SOD. Specifically, to achieve effective complementation between modalities and prevent the interference from noises, we first treat RGB and thermal images equally in the Feature Enhancement Block (FEB), and further, the Global Context Module expands receptive field to obtain the global features and the Top-Feature Enhancement Module suppresses the redundant information that may destroy the original features from the top layer. Subsequently, we embed several Cross Feature Aggregation Modules (CFAMs) into the Aggregation-and-Feedback Decoder to fuse different level features and compensation features for further obtaining comprehensive feature expression. Moreover, a feedback mechanism is adopted to propagate these fused features back into previous layers for refinement and generate saliency maps to decode features in a progressive way. Comprehensive experiments on RGB-T datasets demonstrate that EAF-Net achieves outstanding performance against the state-of-the-art models.