Fusion-oriented registration of UAV-acquired RGB–infrared images for maritime target detection and segmentation

Abstract This study introduces a novel target-aware registration framework for infrared and visible images acquired by unmanned aerial vehicles (UAVs), designed to improve feature-matching accuracy and robustness. The proposed method begins with image segmentation to remove irrelevant background regions, thereby retaining only the target objects that require registration. This step significantly suppresses background interference during the registration process. The segmentation stage is further guided by bounding boxes generated from a target-detection model, improving the accuracy and stability of the segmentation results. In addition, we propose a novel evaluation strategy for assessing infrared–visible image registration performance. This metric segments both the original and registered images and then computes the mean intersection over union between the segmented regions and the original bounding boxes. Furthermore, we incorporate image-fusion metrics from downstream post-registration tasks to provide a more comprehensive assessment of registration quality. Extensive experimental results demonstrate that the proposed method outperforms existing approaches in terms of both registration accuracy and stability, providing a robust solution for infrared–visible image alignment.