The disparity between human and machine perception of spatial information presents a challenge for machines to accurately sense their surroundings and improve target detection performance. Cross-modal data fusion emerges as a potential solution to enhance the perceptual capabilities of systems. This article introduces a novel spatial perception method that integrates dual-modality feature fusion and coupled attention mechanisms to validate the improvement in detection performance through cross-modal information fusion. The proposed approach incorporates cross-modal feature extraction through a multi-scale feature extraction structure employing a dual-flow architecture. Additionally, a transformer is integrated for feature fusion, while the information perception of the detection system is optimized through the utilization of a linear combination of loss functions. Experimental results demonstrate the superiority of our algorithm over single-modality target detection using visible images, exhibiting an average accuracy improvement of 30.4%. Furthermore, our algorithm outperforms single-modality infrared image detection by 3.0% and comparative multimodal target detection algorithms by 3.5%. These results validate the effectiveness of our proposed algorithm in fusing dual-band features, significantly enhancing target detection accuracy. The adaptability and robustness of our approach are showcased through these results.