Enhanced coordinate attention network with centralized visual center for infrared small target detection

Single-Frame Infrared Small Target (SIRST) detection aims to extract small and dim objects from the background of heavy noise and clutter. Traditional detection methods rely on handcrafted feature templates and prior knowledge, which exhibit inferior robustness in complex environments. Despite achieving promising outcomes, the existing detection methods based on CNN still suffer from the issues of small targets being lost in deep layers and long-range dependency problems. In this paper, we propose a cnn-based enhanced coordinate attention network with centralized visual center (CVC-ECANet) for infrared small target detection. Specifically, features of small target can be extracted properly by densely nested structure, where the enhanced coordinate attention module (ECAM) in nodes can enhance the features and maintain them in deep layers. In addition, by incorporating the centralized visual center module (CVCM) after the deepest layer of the network, global long-range dependencies can be captured and shallow features can be centrally modulated. Extensive experiments were conducted on the SIRST dataset, and the results demonstrate that the proposed method performs well in terms of probability of detection (Pd), false-alarm rate (Fa), and intersection of union (IoU).