PACGNet: Pyramidal Adaptive Cross-Gating Network for Multimodal Object Detection in Aerial Imagery

Object detection in aerial imagery is challenging because targets are often small, backgrounds are cluttered, and imaging conditions vary substantially. Multimodal sensing can improve robustness by providing complementary visual and spectral information, but effective fusion must balance cross-modal interaction with computational cost. This paper presents PACGNet, a lightweight backbone-centric framework for multimodal aerial object detection. Its Symmetrical Cross-Gating (SCG) module performs bidirectional gated interaction between the two modality streams at the same feature scale while retaining residual pathways for the original representations. The Pyramidal Feature-aware Multimodal Gating (PFMG) module subsequently forms a single fused feature pyramid by using the preceding higher-resolution level as a spatial prior for fusion at the next level. PACGNet achieves 82.2% mAP50 on the held-out DroneVehicle test set and 82.1% mAP50 on VEDAI with only 5.2 M parameters and 13.2 GFLOPs. These results indicate a favorable accuracy-efficiency trade-off for lightweight multimodal aerial object detection. Code is available at https://github.com/Alianess/PACGNet.