Multi-Modal Target Detection Method Based on Adaptive Feature Search

The optical remote sensing image has a high resolution, while the infrared image provides temperature information about the detected object. These two types of information are complementary. However, optical images often suffer from spatial misalignment issues, which make feature fusion operations challenging. To address these problems, we propose a Transformer feature fusion module that captures high-quality fusion feature information. Building upon this, we design a novel two-branch backbone network that utilizes infrared image features to adaptively screen optical image features, thereby enhancing the detection performance. Experimental results demonstrate the superiority of our approach over the baseline on multi-modal data with non-alignment problems.