SCAFNet: Semantic-Guided Cascade Adaptive Fusion Network for Infrared Small Target Detection

Infrared small target detection is a crucial component of infrared target tracking and search. It is challenging due to the complex backgrounds, low contrast between targets and backgrounds, and the small, dim nature of the targets. Therefore, effectively representing the targets and enhancing the distinction between targets and backgrounds is essential. Existing deep-learning (DL)-based methods struggle to capture the subtle details of weak targets, neglecting the complementary characteristics of multilevel features, which leads to inaccurate localization of targets. In this article, we propose a semantic-guided cascade adaptive fusion network (SCAFNet) to address these challenges. To improve the representation of small targets in the deeper layers, we introduce a multiresolution auxiliary enhancement (MAE) encoder to progressively enhance detailed information within the deep features. After extracting multiscale features, an adaptive fusion (AdaFus) decoder is proposed to fuse them. It has a semantic-guided cascade fusion (SGCF) module to integrate feature maps at three different resolutions. Specifically, SGCF first employs rich semantic features from the high-level feature map to guide the spatial distribution of the low-level feature maps, thereby improving the distinction between the target and the background. Then, AdaFus weights are generated to guide the fusion process, ensuring that the final feature map combines rich semantic information with precise spatial details. Furthermore, we perform long-distance modeling on the feature map to achieve detailed reconstruction, which aids in restoring the shape information of the target. The effectiveness of our method is validated through experiments on various public infrared small target detection datasets.