# Multi-Target Detection Algorithm for Fusion Images Based on an Attention Mechanism
## 2 Related Work  
The main objective of this study is to enhance multi-target detection performance on infrared-visible fused images by integrating attention mechanisms into a deep learning-based detection framework. To contextualize our work, we review related research from three perspectives: infrared-visible image fusion techniques, deep learning-based object detection methods, and attention mechanisms in visual detection tasks. Infrared-visible (IR-VIS) image fusion methods have been extensively studied to combine complementary information from thermal and optical sensors. [PERSON] et al. provide a comprehensive survey of IR-VIS fusion techniques [7], highlighting the need to preserve both salient infrared targets and rich visible textures in the fused result. Recent deep learning approaches exemplify this [PERSON] et al. decomposed images into base and detail layers and used multi-layer CNNs with discrete cosine transforms to extract and fuse features for detail preservation [8]. While this approach improves texture preservation, it lacks adaptive mechanisms to prioritize different spectral information based on scene content. [PERSON] et al. further exploited CNNs by estimating saliency-weight maps and employing a multi-scale fusion strategy, explicitly retaining IR target luminance and visible background textures [9]. Although this method shows promise in maintaining thermal targets, it does not adequately address the challenge of small-object detection in complex backgrounds. Generative adversarial models have also been applied: for example, [PERSON] et al. proposed MrFDDGAN, which uses multi-receptive-field convolutions and a dual-discriminator GAN to balance infrared intensity with color visible texture in the fusion results [10]. Despite achieving visually appealing results, GAN-based methods often lack direct optimization for downstream detection tasks, potentially leading to suboptimal fusion for object detection applications. In parallel, deep learning has revolutionized object detection. Early two-stage detectors like Faster R-CNN leverage a Region Proposal Network to generate candidate object regions [11], while one-stage detectors such as YOLO and SSD reformulate detection as a single-shot regression task [12]. These models achieve real-time performance and high accuracy on benchmarks, but their effectiveness diminishes significantly when applied to infrared-visible fused images due to domain gap issues. Subsequent advances improved robustness and speed: for example, [PERSON] et al. introduced focal loss in RetinaNet to mitigate extreme foreground-background class imbalance in dense detection [13]. Surveys of deep detectors note that modern architectures typically employ deep convolutional backbones and multi-scale feature fusion to detect objects at various scales. While focal loss addresses class imbalance, it does not specifically tackle the unique challenges posed by fused multispectral imagery, where small thermal targets may be overwhelmed by rich visible textures. Finally, attention mechanisms have been increasingly integrated into object detection architectures to enhance feature representation. Channel attention modules, such as Squeeze-and-Excitation proposed by [PERSON] et al., adaptively recalibrate feature responses along the channel dimension [14]. However, these methods primarily focus on single-modality inputs and may not effectively handle the heterogeneous nature of fused multispectral features. Combined spatial-channel attention modules, including CBAM by [PERSON] et al. and BAM by [PERSON] et al., sequentially generate attention maps to emphasize both spatial and channel-wise features [15; 16]. These modules can be embedded into convolutional neural networks to focus on informative regions, thereby improving both classification and localization performance. More recently, transformer-based detectors have introduced global self-attention mechanisms.