# Dark-YOLO: A Low-Light Object Detection Algorithm Integrating Multiple Attention Mechanisms
## 3 Experiment
### Comparative Experiment  
To evaluate the superiority of the proposed low-illumination object detection algorithm, Dark-YOLO, comparative experiments were conducted against mainstream state-of-the-art (SOTA) algorithms, SOTA algorithms enhanced with the SCINet image enhancement network, and classic low-illumination detection methods, including ZERO-DCE (ZERO-DCE + YOLOv3), IA-YOLO, IAT (Illumination-Adaptive Transformer), and LOL-YOLO. These comparisons were performed on the ExDark dataset with splits of 8:1:1 and 6:2:2, as well as the Dark-Face dataset. The results are summarized in Tables 10-12. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **[EMAIL_ADDRESS]\%** & **[EMAIL_ADDRESS]\%** & **Weight/MB** & **GFLOPS** & **Param/10\({}^{6}\)** \\ \hline SSD & 54.8 & 24.8 & - & - & - \\ YOLOv3-tiny & 59.2 & 33.0 & 23.2 & 18.9 & 12.10 \\ YOLOv5n & 69.0 & 42.1 & 5.3 & 7.1 & 2.51 \\ YOLOv8n & 68.3 & 41.7 & 5.9 & 8.1 & 3.01 \\ YOLOv9 & 58.9 & 37.1 & 6.5 & 11.7 & 2.81 \\ YOLOv10n & 55.5 & 33.6 & 5.7 & 8.3 & 2.69 \\ YOLOf & 63.6 & 35.8 & - & - & - \\ SCI + YOLOv11n & 68.3 & 43.0 & 5.5 & 6.3 & 2.58 \\ SCI + RTDETR & 68.6 & 42.5 & 86.0 & - & 41.96 \\ SCI + MobileNetv3 & 63.1 & 37.9 & 5.0 & 5.7 & 2.35 \\ SCI + YOLOv5n & 70.1 & 43.7 & 5.3 & 7.1 & 2.51 \\ ZERO-DCE & 68.0 & 43.6 & 147.4 & - & 44.50 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparative Experiment on ExDark dataset (8:1:1). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Model** & **S** & **C** & **P** & **D** & **[EMAIL_ADDRESS]\%** & **[EMAIL_ADDRESS]:0.95/\%** & **Weight/MB** & **GFLOPS** & **Param/10\({}^{6}\)** \\ \hline Base & & & & 68.3 & 41.7 & 5.96 & 8.1 & 3.008 \\
2 & \(\surd\) & & & 68.6 & 42.5 & 5.96 & 8.1 & 3.008 \\
3 & \(\surd\) & \(\surd\) & & & 69.7 & 43.2 & 6.14 & 8.2 & 3.106 \\
4 & \(\surd\) & & \(\surd\) & & 70.0 & 42.7 & 5.50 & 6.9 & 2.609 \\
5 & \(\surd\) & & \(\surd\) & & 70.1 & 43.4 & 6.30 & 8.5 & 3.045 \\
6 & \(\surd\) & \(\surd\) & \(\surd\) & & 70.2 & 43.3 & 5.60 & 7.0 & 2.708 \\
7 & \(\surd\) & \(\surd\) & & \(\surd\) & 70.1 & 43.7 & 6.50 & 8.6 & 3.143 \\
8 & \(\surd\) & \(\surd\) & \(\surd\) & 70.7 & 43.5 & 5.61 & 7.4 & 2.647 \\
9 & \(\surd\) & \(\surd\) & \(\surd\) & 71.3 & 44.2 & 5.70 & 7.5 & 2.745 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Ablation experiments on the ExDark dataset for the effect of each of our modules on the object detection model. From Table 10, it is evident that on the ExDark (8:1:1) dataset, the proposed algorithm Dark-YOLO outperforms other object detection methods, achieving [EMAIL_ADDRESS] and [EMAIL_ADDRESS]:0.95 values of 71.3% and 44.2%, respectively. The proposed model has a parameter count of 2.744971 \(\times\) 10\({}^{6}\), a computational complexity of 7.5 GFLOPS, and a model size of 5.7 MB. While these metrics reflect an increase in model size, computational complexity, and parameter count compared to YOLOv5n, the proposed method offers a substantial improvement in average detection accuracy. This demonstrates that the proposed algorithm not only delivers superior performance but also maintains high computational efficiency, enabling effective operation under constrained computational resources. Specifically, Dark-YOLO achieves the highest [EMAIL_ADDRESS] among all models, outperforming the