# Multispectral Target Detection Based on Deep Feature Fusion of Visible and Infrared Modalities
## 2 Related Works  
### Multispectral Target Detection  
In recent years, the research in multispectral target detection has advanced significantly, with various approaches proposed to enhance feature fusion and improve detection performance. One direction focused on refining fused multispectral features through iterative refinement mechanisms, ensuring greater consistency in single-spectrum feature representation, while auxiliary tasks such as semantic segmentation provided additional supervisory signals to optimize detection accuracy [10]. Meanwhile, domain adaptation techniques have been explored to bridge the gap between traditional RGB-based detection and multispectral data processing, leading to the development of adversarial-learning-based models capable of robust feature representation across different spectral modalities [18]. Further enhancements in fusion strategies involved decomposing and reorganizing RGB and infrared (IR) feature maps, integrating co-selection and differential enhancement mechanisms to selectively reinforce multispectral information, which resulted in specialized detection networks like YOLOFusion [19].  
Advancements in attention-based fusion techniques have also played a crucial role in improving multispectral target detection. Feature interaction and reconstruction modules were introduced to facilitate deeper modality integration, while self-attention mechanismsrefined feature fusion strategies, leading to more adaptive and precise detection frameworks [20]. Multimodal fusion networks extended these efforts by integrating local, global, and channel-wise feature representations through techniques such as local window shift fusion, global interaction modeling, and cross-channel learning, enabling the generation of highly discriminative feature representations [21]. Additionally, multiscale attention-based architectures leveraged Transformer-based self-attention for feature extraction, which was complemented by dedicated fusion modules that further refined multiscale feature integration [22].  
The recent research has increasingly emphasized the need to balance detection accuracy with model complexity in multispectral fusion networks. For instance, BCMFIFuse introduces a bilateral cross-modality guidance mechanism to enhance mutual information exchange while maintaining computational efficiency [23], and EfficientFuseDet adopts hierarchical attention and bottleneck modules to reduce model size without significantly sacrificing performance [24]. In contrast, our proposed AEFF and C3-PSA modules prioritize the robustness of cross-modal feature weighting and multiscale spatial enhancement, respectively. While these modules introduce moderate additional computational overhead compared to lightweight alternatives, they consistently improve detection accuracy in complex scenarios. This design philosophy aligns with recent advances in hybrid attention mechanisms, which aim to combine local and global feature modeling to strengthen cross-modal dependencies. Although Transformer-based or nonlocal networks offer powerful global context modeling, many existing implementations struggle to fully exploit global features across modalities [25]. Representative multimodal Transformer-based methods are summarized in Table 1. Our method addresses this by integrating attention-based fusion strategies capable of capturing and refining both local and global representations, thereby enhancing the reliability and accuracy of multispectral object detection.