IM-CMDet: An Intramodal Enhancement and Cross-Modal Fusion Network for Small Object Detection in UAV Aerial Visible-Infrared Imagery

Uncrewed aerial vehicle (UAV) aerial visible–infrared [RGB-thermal (RGBT)] object detection has been widely applied in fields such as military operations and rescue missions. However, although numerous UAV aerial RGBT object detection methods exist, several challenges remain in this field. On the one hand, drones typically operate at high altitudes, and objects only occupy a small number of pixels in imaging, posing a significant challenge to object detection. On the other hand, spatial misalignment between modalities remains a major obstacle in cross-modal fusion—especially given the small size of the objects. To address the above issues, this article proposes IM-CMDet, an intramodal enhancement and cross-modal fusion network for small object detection in UAV-based RGBT imagery, which comprises three effective modules: the detail-semantics joint enhancement (DSJE) module, the differential-based fusion weight generation (DFWG) module, and the feature reconstruction network (FRN). The DSJE module prevents small object features from being overwhelmed by background noise through optimizing feature representations across different levels. The FRN module is designed to overcome modality differences and build intermodality information correlation via the Swin-Transformer architecture. To further enhance the network’s sensitivity to small objects, the DFWG combines differential and spatial attention to generate the final fusion weights while reducing the impact of background noise on detection performance. Extensive experiments on RGBTDronePerson and two additional benchmarks demonstrate that IM-CMDet achieves state-of-the-art performance through effective cross-modal fusion, significantly advancing small-object detection in complex aerial scenarios. The code is available at https://github.com/RS-Minchao/IM-CMDet