# Dark-YOLO: A Low-Light Object Detection Algorithm Integrating Multiple Attention Mechanisms
## 3 Experiment
### Comparative Experiment
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **[EMAIL_ADDRESS]/\%** & **[EMAIL_ADDRESS]:0.95\%** & **Weight/MB** & **GFLOPS** & **Param/10\({}^{6}\)** \\ \hline ISD & 51.6 & 23.1 & - & - & - \\ YOLOv3-tiny & 52.5 & 30.9 & 23.2 & 18.9 & 12.10 \\ YOLOv5n & 60.8 & 34.9 & 5.3 & 7.1 & 2.51 \\ YOLOv8n & 57.3 & 33.5 & 5.9 & 8.1 & 3.01 \\ YOLOv9 & 61.9 & 38.4 & 6.5 & 11.7 & 2.81 \\ YOLOv10n & 57.8 & 34.9 & 5.7 & 8.3 & 2.69 \\ YOLOf & 60.1 & 34.4 & - & - & - \\ SCI + YOLOv11n & 58.8 & 35.1 & 5.5 & 6.3 & 2.58 \\ SCI + RTDETR & 62.2 & 34.9 & 86.0 & - & 41.96 \\ SCI + MobileNetv3 & 54.6 & 31.7 & 5.0 & 5.7 & 2.35 \\ SCI + YOLOv5n & 60.9 & 34.8 & 5.3 & 7.1 & 2.51 \\ ZERO-DCE & 58.7 & 35.2 & 147.4 & - & 44.50 \\ IA-YOLO & 60.8 & 38.6 & 156.2 & - & 62.31 \\ IAT & 57.8 & 34.9 & 173.7 & - & 57.49 \\ LOL-YOLO [47] & 57.3 & 34.7 & 11.2 & 20.6 & 5.66 \\ Ours & 62.2 & 37.2 & 5.7 & 7.5 & 2.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Comparative Experiment on ExDark dataset (6:2:2). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **[EMAIL_ADDRESS]/\%** & **[EMAIL_ADDRESS]:0.95\%** & **Weight/MB** & **GFLOPS** & **Param/10\({}^{6}\)** \\ \hline ISD & 32.3 & 11.6 & - & - & - \\ YOLOv3-tiny & 32.7 & 11.9 & 23.2 & 18.9 & 12.10 \\ YOLOv5n & 46.0 & 19.7 & 5.3 & 7.1 & 2.50 \\ YOLOv8n & 46.8 & 20.6 & 5.9 & 8.1 & 3.01 \\ YOLOv9 & 43.3 & 18.4 & 6.5 & 11.7 & 2.81 \\ YOLOv10n & 41.0 & 17.7 & 5.7 & 8.3 & 2.69 \\ YOLOf & 42.2 & 15.4 & - & - & - \\ SCI + YOLOv11n & 47.9 & 21.6 & 5.5 & 6.3 & 2.58 \\ SCI + YOLOv16 & 48.6 & 21.5 & 86.0 & - & 41.96 \\ SCI + MobileNetv3 & 41.1 & 17.1 & 5.0 & 5.7 & 2.35 \\ SCI + YOLOv5n & 47.6 & 20.0 & 5.3 & 7.1 & 2.51 \\ ZERO-DCE & 48.6 & 21.7 & 147.4 & - & 44.50 \\ IA-YOLO & 48.9 & 21.7 & 156.2 & - & 62.31 \\ IAT & 47.2 & 19.6 & 173.7 & - & 57.49 \\ LOL-YOLO [47] & 46.8 & 20.9 & 11.2 & 20.6 & 5.66 \\ Ours & 49.1 & 21.9 & 5.7 & 7.5 & 2.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Comparative Experiment on Dark-Face dataset. second-best SCI + YOLOv5n by 1.2%, while maintaining a model complexity comparable to YOLOv5n. Compared to mainstream object detection algorithms such as SSD, YOLOv3-tiny, YOLOv5n, YOLOv8n, YOLOv9, YOLOv10n, and YOLOf, Dark-YOLO improves [EMAIL_ADDRESS] by 16.5%, 12.1%, 2.3%, 3.0%, 12.4%, and 15.8%, respectively. Against algorithms incorporating the SCINet enhancement network, including YOLOv11n, RTDETR, MobileNetv3, and YOLOv5n, Dark-YOLO enhances [EMAIL_ADDRESS] by 3.0%, 2.7%, 8.2%, and 1.2%, respectively. When compared to classic low-light enhancement algorithms like ZERO-DCE, IA-YOLO, and IAT, Dark-YOLO boosts [EMAIL_ADDRESS] by 3.3%, 3.5%, and 4.8%, respectively. On the ExDark (6:2:2) dataset, as shown in Table 11, the mAP of all models generally decreases; for instance, YOLOv5n's [EMAIL_ADDRESS] drops from 69% to 60.8%, indicating a significant impact of reduced training data on performance. Although YOLOv9 and YOLOv10n show improvements in [EMAIL_ADDRESS], Dark-YOLO still outperforms other models, tying for first place with SCINet-enhanced RTDETR. However, Dark-YOLO's model size is only 5.7 MB, accounting for just 6.6% of RTDETR's size. Additionally, across different ExDark splits, Dark-YOLO's [EMAIL_ADDRESS] fluctuation range (71.3% to 62.2%) is smaller than that of YOLOv5n (69% to 60.8%), demonstrating its stronger adaptability to varying training data sizes. On the Dark-Face dataset, as shown in Table 12, Dark-YOLO leads in both [EMAIL_ADDRESS] (49.1%) and [EMAIL_ADDRESS]:0.95 (21.9%). Although ZERO-DCE's [EMAIL_ADDRESS] is close to Dark-YOLO's, its model size (147.4 MB) and parameter count (44.50 M) are over 26 times larger than those of Dark-YOLO, highlighting a significant efficiency disadvantage. In summary, Dark-YOLO achieves simultaneous improvements in detection accuracy and computational efficiency on both the ExDark and Dark-Face datasets. The experiments demonstrate that Dark-YOLO exhibits superior robustness compared to existing methods under data-scar conditions (6:2:2 split) and complex low-light scenarios.