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To address this limitation, we propose a novel Dual-Channel and Spatial Attention Fusion Module (DCSAFM), which constructs unique attention feature maps for each modality along the channel dimension and achieves deep fusion through self-attention in the spatial dimension. This module simultaneously benefits from the distinctive information within each modality and their complementary relationships, thereby significantly enhancing the accuracy and robustness of multispectral object detection. By embedding DCSAFM at multiple feature levels, we propose a multispectral object detection network named Dual-Channel and Spatial Attention Fusion Network (DCSAFNet), which achieves state-of-the-art performance on the FILR dataset with an mAP of 83.7%. Moreover, extensive experiments on the KAIST and LLVIP datasets demonstrate the strong generalization capability of our method, achieving a miss rate of 7.7% and an mAP of 97.7%, respectively. 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