# Multi-Target Detection Algorithm for Fusion Images Based on an Attention Mechanism
## 5 Experiments Results
### Effect of Image Fusion on Detection  
In the previous experiments, the detection model was trained using the fused images generated in Section 3. To further validate the effectiveness of the fusion algorithm, this subsection compares the performance of models trained on single-modal images--i.e., visible-light or infrared images--prior to fusion.  
The experimental results, summarized in Table 6, show that detection using fused images consistently outperforms detection using only visible-light or infrared inputs across all evaluation metrics. While the improvement in precision is relatively small, the recall and mAP values, particularly mAP50-95, show a more noticeable increase. These metrics are especially important for detecting small or partially occluded targets, where fine-grained localization and high recall directly affect detection robustness.  
In safety-critical domains such as autonomous driving or security surveillance, even a marginal gain in recall or localization performance can significantly enhance system  
Figure 20: Comparison of detection effects before and after improvement.  
robustness, especially when the risk of missing a small or partially hidden target may result in severe consequences. We further analyzed the detection outputs and found that fused images are more effective in identifying occluded or low-contrast targets. To illustrate these cases, qualitative comparisons have been included in Figure 21. Seven groups of images were selected from visible-light images, infrared images, and fused images for detection effect comparison. In each group, from left to right, the detection results of visible-light images, infrared images, and fused images are shown.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**No.** & **Dataset** & **Precision** & **Recall** & **mAP** & **mAP50-95** & **Inference Speed (ms)** \\ \hline
1 & Fusion & 0.88 & 0.859 & 0.901 & 0.613 & 1.582 \\
2 & Visible Light & 0.88 & 0.858 & 0.887 & 0.6 & 1.582 \\
3 & Infrared & 0.866 & 0.84 & 0.861 & 0.583 & 1.582 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance comparison of different datasets.  
Figure 21: _Cont._  
The first and second image groups were captured in low-light nighttime monitoring environments. In the first group, the fused image successfully detects all individuals, whereas both the infrared and visible-light images fail to identify some of the people present. In the second group, the visible-light image misses individuals located near the image edges and in darker regions, while both the fused and infrared images correctly detect all targets.  
The third, fourth, and fifth groups depict smoke-filled environments, where heavy smoke significantly degrades the quality of visible-light images, obscuring the targets behind it. In contrast, the fused images are able to successfully detect all targets that are missed by the visible-light modality.  
The sixth and seventh groups correspond to high-brightness environments with clear visible-light imagery. In these scenarios, both the visible-light and fused images detect all individuals effectively, including those partially occluded by trees in the sixth group.  
Overall, in low-illumination and smoke-obscured environments, fused images outperform single-modal images in target detection. While precision is similar between the fused and visible-light inputs, the recall and mAP50-95 of fused images are noticeably higher. Specifically, mAP50-95 improves from 0.600 to 0.613 and recall improves from 0.858 to 0.859. These metrics are particularly important in applications involving small or occluded targets, where comprehensive detection with high recall and fine-grained localization with high mAP50-95 are critical.