## 4 Experimental Setup
### Quantitative Results
This indicates that the computational overhead introduced by the frequency-domain fusion module can be compensated for by optimizing the channel attention mechanism. Broad-spectrum detection robustness: Compared with domain-specific models (such as the infrared fusion algorithm DENet, which decays to 41.3% mAP\({}_{50}\) on the visible-light test set), UMFNet realizes dynamic adaptation to multi-band features through learnable frequency-domain basis functions, maintaining cross-modal robustness (\(\Delta\)mAP\({}_{50}<3.5\%\)). \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Method** & **mAP@50** & **mAP@50:95** & **Params (M)** & **FLOPs (G)** & **FPS (GPU)** & **Year** \\ \hline YOLOv3 (N) [15] & 0.430 & 0.276 & **8.7** & 13.2 & 92 & 2018 \\ YOLOv3 (L) [15] & 0.455 & 0.282 & 21.4 & 36.5 & 45 & 2018 \\ YOLOv8 (Baseline) [15] & 0.687 & 0.440 & 43.6 & 8.5 & 158 & 2023 \\ YOLOv9 [26] & 0.695 & 0.445 & 54.1 & 10.3 & 135 & 2024 \\ MS-DAYOLO [27] & 0.443 & 0.280 & 23.6 & 28.3 & 68 & 2021 \\ DAYOLO [28] & 0.462 & 0.281 & 40.5 & 32.1 & 70 & 2021 \\ IA-YOLO [12] & 0.494 & 0.305 & 62.2 & 45.7 & 58 & 2022 \\ \hline Zero-DCE + YOLOv8 [3] & 0.687 & 0.441 & 43.6 & 8.5 & 142 & 2020 \\ KinD + YOLOv8 [10] & 0.683 & 0.439 & 44.2 & 8.6 & 139 & 2021 \\ MBLLEN + YOLOv8 [24] & 0.685 & 0.440 & 43.9 & 8.5 & 145 & 2018 \\ EnlightenGAN + YOLOv8 [11] & 0.689 & 0.441 & 43.8 & 8.6 & 138 & 2021 \\ \hline LLFlow + YOLOv8 [5] & 0.699 & 0.445 & 47.3 & 10.1 & 122 & 2022 \\ ELAN + YOLOv8 [29] & 0.698 & 0.444 & 45.5 & 9.2 & 130 & 2023 \\ DENet + YOLOv8 [14] & 0.695 & 0.443 & 44.1 & 8.7 & 132 & 2023 \\ RT-DETR [25] & 0.707 & 0.452 & 76.3 & 140.2 & 60 & 2023 \\ EfficientFormer [23] & 0.672 & 0.428 & 9.2 & **5.1** & **210** & 2022 \\ \hline
**UMFNet + YOLOv8 (Ours)** & **0.731** & **0.458** & 43.9 & 8.6 & 150 & **2025** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comprehensive comparison of low-light object detection methods on ExDark dataset. Figure 6: Performance comparison of UMFNet with existing algorithms on the EXDark benchmark. Our method (highlighted in red) shows superior robustness to noise while preserving structural details. Table 3 presents the per-class AP values using YOLOv8. Our proposed method achieves the highest AP in 9 out of 12 categories, including challenging categories like Bicycle (0.800 vs. 0.776 in DENet) and Bottle (0.759 vs. 0.738 in DENet). The **Total** column reveals our method's overall superiority, with a 0.731 mAP@50, demonstrating a 6.4% relative improvement over the baseline YOLOv8 (0.731 vs. 0.687). While DENet achieves better performance in the Cat (0.647 vs. 0.634) and Chair (0.548 vs. 0.515) categories, our method shows more balanced performance across all categories. \begin{table}
\begin{tabular}{l c c c c c c} \hline
**Variant** & **SCCA Type** & **FAF Levels** & **Coord. Embed** & **mAP@50** & **mAP@50:95** & **FPS** \\ \hline Baseline & ✗� & ✗ & ✗ & 0.687 & 0.440 & 158 \\ \hline A1 & SE & ✗ & ✗ & 0.685 & 0.438 & 153 \\ A2 & CBAM & ✗ & ✗ & 0.689 & 0.441 & 149 \\ A3 & SCCA (Ours) & ✗ & ✗ & 0.692 & 0.445 & 151 \\ \hline B1 & ✗ & 2 & ✗ & 0.689 & 0.442 & 145 \\ B2 & ✗ & 3 & ✗ & 0.694 & 0.447 & 143 \\ B3 & ✗ & 4 & ✗ & 0.696 & 0.448 & 140 \\ \hline C1 & SCCA & 3 & ✗ & 0.701 & 0.450 & 142 \\ C2 & SCCA & 3 & ✓ & **0.731** & **0.458** & 150 \\ \hline \end{tabular}
\end{table} 
Table 4: Ablation study on component combinations.