Co-attentional cascaded perception network for multimodal remote sensing target detection
Li Qi, Lisheng Wei · International Journal of Remote Sensing · 2026
To address issues such as feature misalignment and additional feature uncertainty caused by heterogeneity, asymmetry, and information redundancy between modalities in multimodal remote sensing target detection technology. Therefore, a multimodal remote sensing target detection model based on a Collaborative Attention Cascaded Perception Network (CACPNet) is proposed, with the aim of improving remote sensing target detection performance. Firstly, the Multi-scale Perception Cross-modal Fusion module (MPCF) is designed for complementary fusion learning. It effectively addresses feature misalignment and enhances training robustness by leveraging Dense Residual Learning Network (DRLN) and Cross-scale Perceptual Feature Module (CPFM). Secondly, innovative progressive attention Hierarchical Cascade architecture is introduced. It collaborates with the Multi-directional Position Coordinate Attention (MPCA) module to reduce information redundancy and suppress background noise for feature quality optimization. This architecture fully leverages the strengths of both the cross-modal fusion module and the feature optimization module, balancing computational complexity while improving detection accuracy. Quantitative and qualitative experiments on the VEDAI dataset demonstrate that our model achieves 83.0% mAP50 and 59.3% mAP50-95. Furthermore, generalization studies on the Drone Vehicle dataset effectively validate the high-quality multimodal remote sensing detection capabilities of our approach.