# Dark-YOLO: A Low-Light Object Detection Algorithm Integrating Multiple Attention Mechanisms
## 3 Experiment
### Ablation Study  
To validate the effectiveness of each module in the Dark-YOLO algorithm for object detection in low-light scenarios, a series of ablation experiments were conducted based on the YOLOv8 network on the ExDark dataset. These experiments aimed to evaluate the contribution of each module to the overall performance of the detection algorithm. In this study, SCI, CSPPF, PSM, and D-RAMiT are abbreviated as S, C, P, and D, respectively. The evaluation metrics included [EMAIL_ADDRESS], [EMAIL_ADDRESS], model computational complexity (FLOPs), and parameter count, to assess the effectiveness of these four improvements. The results of the ablation experiments are presented in Table 9.  
Initially, the baseline YOLOv8n model was tested, achieving an [EMAIL_ADDRESS] of 68.3% and an [EMAIL_ADDRESS]:0.95 of 42.7%. Subsequently, the SCINet module was introduced, resulting in an increase of 0.3% in [EMAIL_ADDRESS] and an increase of 0.8% in [EMAIL_ADDRESS]:0.95, indicating that the inclusion of the SCI module enriched the information content in low-light images. Next, the CSPPF module replaced the original SPPF module, leading to an increase in [EMAIL_ADDRESS] to 69.7% and an improvement in [EMAIL_ADDRESS]:0.95 to 43.2% based on the results of the second configuration. This demonstrates that incorporating CSPPF enhances the model's ability to extract local features from images. Further improvements were made by adding the PSM module to the second, fourth, sixth, and eighth layers of the backbone network. Based on the sixth configuration, this addition resulted in a further increase of 0.5% in [EMAIL_ADDRESS], a reduction in model size by 0.54 MB, a reduction in computational  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Flops/G** & **Params/MB** & **[EMAIL_ADDRESS]/\%** & **[EMAIL_ADDRESS]:0.95/\%** \\ \hline SPPF & 8.1 & 3.01 & 68.6 & 42.5 \\ ASPP & 9.7 & 5.07 & 67.0 & 41.6 \\ SPPFCSPC & 9.4 & 4.61 & 69.1 & 43.1 \\ BasicRFB & 8.2 & 3.17 & 68.6 & 42.5 \\ CSPPF & 8.2 & 3.10 & 69.7 & 43.2 \\ \hline \hline \end{tabular}
\end{table}
Table 7: CSPPF Effectiveness Comparison Experiment.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Batch Size** & **YOLOv8n/[EMAIL_ADDRESS]** & **Ours/[EMAIL_ADDRESS]** \\ \hline
4 & 68.1 & 71.2 \\
8 & 68.6 & 71.2 \\
16 & 68.3 & 71.3 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Batch Size Sensitivity Analysis.  
complexity (GFLOPs) by 14.6%, and a decrease in parameter count by 12.8%. These results suggest that the PSM module enables the algorithm to focus more effectively on key feature information with fewer computational resources, thereby enhancing feature extraction capabilities. Finally, the D-RAMiT module was added, resulting in an [EMAIL_ADDRESS] increase to 71.3% and an [EMAIL_ADDRESS]:0.95 increase to 44.2% based on the fourth configuration. This indicates that the addition of the Dimension Reciprocal Attention Module enables the model to simultaneously focus on both local and global information in the image.  
The ablation study demonstrates that each of the proposed modules contributes effectively to the improvement of the algorithm's performance, and their combined use yields even better results. These enhancements not only strengthen the feature extraction and contextual information propagation capabilities of the algorithm but also improve its attention to target features and localization accuracy.