To address the challenges of multispectral feature mismatch，complex scene interference，and insufficient detection accuracy in drone aerial vehicle target detection，this paper proposes Multi-Scale Gated Fusion Network（MSGF-Net），a YOLOv10-based multi-feature space joint optimization network.First，the network employs a dual-stream feature extraction network and introduces a gated local-global fusion （GLGF） module into the backbone.This enables effective interaction and joint optimization of visible and infrared features across mult-feature spaces，thereby mitigating the impact of feature mismatch and enhancing feature representation.Subsequently，after the feature pyramid network，MSGF-Net incorporates a cross-modulation block （CMB） module to perform the pixe

l-wise weighted fusion of multi-feature space information，further improving the complementarity between different spectral features.Experiments on the public DroneVehicle dataset demonstrate that MSGF-Net achieves 83.4%

mAP

0

.

5

and 63.9%

mAP

0

.

5：0

.

95

，showing a significant improvement compared to the single-channel model YOLOv10n.Furthermore，compared to leading multi-modal fusion algorithms like C2Former and TSDADet，MSGF-Net increases the

mAP

0

.

5：0

.

95

by over 9 percentage points，providing compelling evidence of its superior accuracy and robustness.