DCONet: A Dual-Task Collaborative Optimization Network for Infrared Small Target Detection

Infrared small target detection is crucial in military reconnaissance, remote sensing, and so on. However, due to its small size and the high coupling with complex backgrounds, the present methods still face challenges in precise detection. They predominantly focus on target feature learning while neglecting the critical role of background modeling for small target decoupling. To this end, we propose a dual-task collaborative optimization network (DCONet), which decouples the task into background estimation and target segmentation using a multistage iterative optimization strategy. First, considering significant directional distribution characteristics in infrared backgrounds, we propose a direction-aware background estimation module (DBEM) to capture directional features, such as clouds and trees, thereby generating an initial background estimation. Second, we propose a background suppression gating unit (BSGU), which employs a gating mechanism and a channel-level adjustment factor to dynamically suppress background noise based on the preliminary background estimation, thereby generating the target segmentation result. Finally, the estimated background, target segmentation, and the reconstructed original image based on them are propagated to the next stage for further iterative optimization. The experimental results show that DCONet performs better than existing methods across three public datasets. The source code is available at https://github.com/tustAilab/DCONet