# Multi-Target Detection Algorithm for Fusion Images Based on an Attention Mechanism
## 1 Introduction  
The performance of traditional object detection systems that rely solely on visible-light images is often constrained by the inherent limitations of visible-light sensors. These systems experience significant degradation under conditions such as low illumination, adverse weather, and partial occlusions [1]. To address this, infrared and visible-light imaging--two complementary modalities--can be fused to produce composite images that enhance robustness and accuracy in target perception [2].  
Recent advances in deep learning have significantly reshaped the field of object detection. [PERSON] et al. introduced the R-CNN framework [3], which enabled end-to-end learning of deep visual features from region proposals, substantially improving detection performance over traditional hand-crafted approaches. [PERSON] et al. proposed YOLO, a one-stage detector that formulates object detection as a direct regression problem [4], achieving real-time performance by predicting bounding boxes and class probabilities in a single forward pass. Simultaneously, the use of thermal imaging gained momentum. For instance, [PERSON] et al. presented the KAIST multispectral pedestrian dataset [5], comprising aligned RGB and thermal image pairs, and demonstrated that incorporatingthermal data improves detection under poor lighting. More recently, deep fusion networks have emerged that jointly process visible and infrared inputs. One notable example is M2 FNet, a transformer-based two-stream model proposed by [PERSON] et al. [6], which leverages cross-modal attention to fuse features across spectra, enhancing detection robustness in complex environments.  
However, applying general-purpose detectors such as YOLOv8 directly to fused infrared-visible images is often suboptimal. Fused images exhibit specific challenges, including redundant background features, modality inconsistencies, and semantic dilution. These issues are particularly problematic for detecting small or occluded targets, which often lose discriminative features during the fusion process. Moreover, conventional detection frameworks are typically designed for unimodal data and are not optimized to address the heterogeneous and noisy nature of fused inputs, resulting in reduced accuracy and generalization capability.  
To overcome these challenges, we propose an improved detection framework based on the YOLOv8 architecture. The main contributions of this work are summarized as follows:  
* An enhanced YOLOv8 model incorporating a dedicated small-object detection layer, which improves sensitivity to low-resolution or partially occluded targets.
* Integration of the Global Attention Mechanism (GAM), which highlights salient spatial and channel-wise features to improve robustness under background clutter and occlusion.
* A refined loss function based on Weighted IoU (WIoU), designed to improve bounding box regression in scenarios involving ambiguous boundaries or noisy annotations.  
The remainder of this paper is organized as follows. Section 2 reviews related work on image fusion and deep learning-based object detection. Section 3 introduces the image fusion methodology. Section 4 presents the proposed detection framework. Section 5 discusses experimental results and performance evaluation. Sections 6 and 7 discuss and conclude the paper.