# Multispectral Target Detection Based on Deep Feature Fusion of Visible and Infrared Modalities
## 5 Experimental Results
### Ablation Study
The performance differences in terms of the [EMAIL_ADDRESS] and [EMAIL_ADDRESS]:0.95 metrics across these models are analyzed. The baseline model, M3, achieved [EMAIL_ADDRESS] and [EMAIL_ADDRESS]:0.95 scores of 74.9 and 32.8, respectively. With the addition of the AEFF (M4), these metrics improved to 75.23 and 32.95, respectively, demonstrating AEFF's effectiveness in enhancing the interaction between cross-modal features. The application of the C3-PSA (M5) further increased the [EMAIL_ADDRESS] and [EMAIL_ADDRESS]:0.95 metrics to 75.73 and 32.8, respectively, emphasizing the C3-PSA module's role in focusing on key information and refining feature representation. Finally, when both the AEFF and C3-PSA were integrated into the model (M6), the [EMAIL_ADDRESS] rose to 75.8, while the [EMAIL_ADDRESS]:0.95 improved to 33.11, achieving the best performance in this experiment. These results highlight the significant synergistic effect of the AEFF and C3-PSA in improving cross-modal feature fusion and target detection tasks. In the ablation experiments on the M\({}^{3}\)FD dataset, the dual-stream YOLOv5 architecture (M6) also demonstrated superior performance compared to the unimodal configurations (M1 and M2), particularly in the [EMAIL_ADDRESS] (84.2) and [EMAIL_ADDRESS]:0.95 (52.4) metrics. Compared to the unimodal models, the dual-stream YOLOv5 architecture significantly enhanced the model's detection capability in complex environments by effectively fusing visible and infrared image features. The dual-stream YOLOv5 (M4), with the introduction of the AEFF, reached 84.4 for the [EMAIL_ADDRESS] score and 51.6 for the [EMAIL_ADDRESS]:0.95 score, showcasing the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Data** & **mAP50** & **mAP50:95** \\ \hline M3 & RGB + IR & 74.9 & 32.8 \\ M4 & RGB + IR & 75.23 & 32.95 \\ M5 & RGB + IR & 75.73 & 32.8 \\
**M6** & RGB + IR & **75.8** & **33.11** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Ablation experiments on KAIST dataset. The best-performing methods are highlighted in bold. Figure 6: Structure of baseline. positive impact of the AEFF in improving the efficiency of inter-modal feature fusion. However, the dual-stream YOLOv5 (M5), which integrates the C3-PSA, showed a slight decrease in the Recall metric but maintained relatively stable performance in the [EMAIL_ADDRESS] and [EMAIL_ADDRESS]:0.95 metrics, further highlighting the effectiveness of the C3-PSA in enhancing inter-modal feature processing. Finally, when both the AEFF and C3-PSA were applied to the dual-stream YOLOv5 (M6), the model maintained high performance across all metrics, with 84.2 for the [EMAIL_ADDRESS] score and 52.4 for the [EMAIL_ADDRESS]:0.95 score. This underscores the important role of the combination of these two techniques in enhancing the model's detection performance. To further quantify the computational cost of our proposed improvements, we report the FLOPs and parameter counts for key model variants in Table 10. Compared to ICAFusion, which incurs 192.6 GFLOPs and 120.3 million parameters, the model using only the AEFF increases to 216.8 GFLOPs and 120.8 M parameters, while the C3-PSA-only model reduces the parameters to 108.8 M with 203.7 GFLOPs. The full YOLO-MEDet model (M6), which integrates both the AEFF and C3-PSA, reaches 227.9 GFLOPs and 121.0 M parameters. These increases in complexity are justified by the observed performance gains in the Precision, Recall, and mAP metrics across multiple datasets. Although our model is not optimized for lightweight deployment, its computational scale remains comparable to state-of-the-art architectures commonly used in multispectral detection tasks. In summary, these results demonstrate that through the synergistic effects of feature fusion and enhancement techniques, dual-stream YOLOv5 shows substantial performance improvements in complex multimodal scenarios and significantly enhances the accuracy and robustness of target detection.