To address the issues of weakened intra-modal features and inefficient cross-modal interaction in current UAV multispectral remote sensing object detection, an object detection method that integrates dual-stream single-modal enhancement and cross-modal feature interaction is proposed. A wavelet transform module based on depthwise separable convolution is designed to extract and enhance the high-frequency detail information in feature maps, thus strengthening the texture detail extraction capability of the visible light branch. A cross-scale global information fusion module is proposed, which utilizes multi-scale feature integration and global information extraction to enhance the detection capability of the infrared branch for thermal objects. A multimodal hybrid self-attention fusion module is constructed to adaptively guide the feature interaction between visible light and infrared modalities, fully exploiting spectral complementarity. Experimental results show that the proposed method achieves mAP@0.5 of 86.7% and 86.0% on the visible-infrared multispectral datasets DVTOD and DroneVehicle, respectively, and improves them by 1.3% and 1.9%, respectively, compared to the baseline model, effectively enhancing the performance of UAV multispectral object detection.
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