# Multispectral Target Detection Based on Deep Feature Fusion of Visible and Infrared Modalities
## 5 Experimental Results
### Qualitative Analysis  
To comprehensively evaluate the performance of each model, we visualize the detection results of the baseline model, ICAFusion, and our proposed method on the FLIR and M\({}^{3}\)FD datasets and compare them with the ground truth annotations. Figure 9 illustrates the detection results under various lighting and environmental conditions, covering daytime and nighttime scenes with high target density, long target distances, and strong light interference. As one can observe, the baseline model and ICAFusion frequently failed to detect certain cars and pedestrians in daytime scenes, and their performance degraded significantly at night. In contrast, our method performed consistently under all lighting conditions, successfully detecting nearly all targets during both day and night. In target-intensive scenes (e.g., daytime scene a and nighttime scene c), our method accurately detected all pedestrians and vehicles within the field of view, whereas the baseline model and ICAFusion were particularly prone to missing small targets at night. Furthermore, when targets were at greater distances, the baseline model also struggled with misdetections under strong illumination in nighttime scene d, while our method continued to recognize all targets successfully. Additionally, Figure 10 presents the detection results of the three methods in different scenarios, including regular scenes, small-target scenes, and crowded scenes. In regular scenes, both the baseline model and ICAFusion failed to detect certain targets compared to our model. In small-target scenes, our method consistently classified and localized all targets, regardless of daytime or nighttime, whereas the baseline model and ICAFusion missed more distant and smaller targets, such as pedestrians and vehicles. In crowded scenes, our model effectively captured all targets, while the baseline model and ICAFusion continued to suffer from missed detections. Figure 9: A visual comparison of the detection results of our model with those of SOTA and baseline methods on the FLIR dataset. Subfigures (a–d) represent different sample scenes. Yellow triangles indicate objects successfully detected by our model but missed by the other two methods, while red triangles denote incorrectly detected objects. Note that bounding box layouts may appear similar, but the overlaid triangle markers highlight key detection differences. These experimental results further validate the advantages of our method in multi-spectral information fusion and cross-modal feature interaction, as well as its superior detection capability in complex scenes. To complement the qualitative detection comparisons, we further visualized the training dynamics of each model to objectively validate performance differences. Figure 11 presents the evolution of the [EMAIL_ADDRESS] and [EMAIL_ADDRESS]:0.95 scores on the FLIR dataset, while Figure 12 shows the training loss trajectories for box, objectness, and classification components. As seen in Figure 11, it is evident that YOLO-MEDet achieved faster convergence and higher accuracy across all IoU thresholds compared to the baseline and ICAFusion. Meanwhile, Figure 12 illustrates that our model maintained consistently lower training losses throughout training. These results confirm that the improvements introduced by the AEFF and C3-PSA not only enhance detection quality but also facilitate more stable and efficient optimization during training. Figure 11: mAP progression curves of YOLO-MEDet, ICAFusion, and the baseline on the FLIR dataset. Figure 10: A visual comparison of the detection results of our model with those of SOTA and baseline methods on the M\({}^{3}\)FD dataset. Subfigures (**a–d**) represent different sample scenes. Yellow triangles indicate objects successfully detected by our model but missed by the other two methods, while red triangles denote incorrectly detected objects. To verify the generality of this behavior, we conducted the same training visualization on the M\({}^{3}\)FD dataset.