CMAI-Det: Cross-Modal Alignment and Interaction for RGB-T Object Detection in Drone Scenes

RGB-T object detection is increasingly applied in drone surveillance, autonomous driving, and intelligent transportation due to its robustness in complex conditions. However, most existing methods assume well-aligned image pairs and often overlook misalignment between modalities, which arises from drone motion, viewpoint shifts, and sensor inconsistencies, thereby hindering detection accuracy. To address this limitation, we present CMAI-Det, a framework designed for object detection in unaligned RGB-T images. The approach employs a dual-stream extractor to strengthen the representational capacity of visible and thermal features. A modality-cooperative alignment module, using the thermal stream as reference, integrates multi-scale deformable convolutions and attention to align visible features. An adaptive fusion scheme is further introduced to balance modality contributions according to their reliability under varying conditions, enhancing feature robustness. Finally, a perception-driven deformable detection head improves discriminability by reinforcing spatial selectivity and structural adaptability, enabling precise modeling of diverse object appearances. Extensive experiments on the DVTOD dataset show that CMAI-Det surpasses 12 state-of-the-art RGB-T detectors in challenging drone scenarios, achieving an mAP of 86.1. It also performs strongly at standard thresholds, with AP50 of 90.0 and AP75 of 82.3, underscoring its robustness and effectiveness in complex detection tasks. The code is available at https://github.com/yinhaixu2000-coder/CMAI-Detection.