## 4 Experimental Results and Analyses
### Comparative Experiments with Tibia-Yolo  
There were changes in both the parameter quantity and mAP. It can be observed in Table 3 that, based on YOLOv8-s, we improved some terminals of the Backbone and improved the performance of the network by adding the ParNet attention module. It should be noted that, although the average accuracy reached 90.2% after adding the SKAttention module, more parameters were added at the same time, so many computing resources were sacrificed. The number of parameters that we added was 13.12% of the number of parameters added with the SKAttention module, and the average accuracy was very close. Subsequently, we compared the addition of the same EMA module in front of the three detection heads of the Neck and the addition of other attention modules, and we focused on observing changes in the parameter quantities and mAP. In Table 4, we can see the multi-scale attention mechanism's improvement of the network. Through observation, we found that, after adding EMA modules at different scales, the average accuracy was 3% higher than that of the baseline, but the number of parameters basically did not change, ensuring the operating efficiency of the deep network. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Backbone** & **EMA** & **ParNet** & **CARAFE** & **WIoU** & \(mAP_{0.50,95}\) **(\%)** \\ \hline C2F & & & & & 86.7 \\ C2F & ✓ & & & & 89.7 \\ C2F & ✓ & ✓ & & & 89.9 \\ C2F & ✓ & ✓ & ✓ & & 90.1 \\ C2F & ✓ & ✓ & ✓ & ✓ & 90.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The results of the ablation experiments. In order to verify the effectiveness of using WIoU, we added a variety of loss functions for comparison after adding the ParNet module and EMA module to YOLOv8-s and using the CARAFE upsampling operator. As can be seen in Table 5, after comparing Complete-IoU (CIoU) [41], Distance-IoU (DIoU) [41], Efficient-IoU (EIoU) [42], Generalized-IoU (GIoU) [43], and Wise-IoU (WIoU), it was finally found that the WIoU loss function was very advantageous in improving the accuracy. Although 0.1 GFLOPS was added, the mAP accuracy was improved by 0.7%. We compared the improved Tibia-YOLO model based on YOLOv8-s with representative advanced models for other target detection tasks on the Tibia dataset, and we evaluated the changes in parameter size (\(mAP_{0.50.95}\)). Table 6 shows a comparison of different network models on the Tibia dataset. In Figure 7, the results of running different models are analyzed. The Tibia-YOLO model had the highest detection accuracy and confidence, followed by the YOLOv8-s model. The confidence of the YOLOv5 and SSD models decreased. Although the \(mAP_{0.50.95}\) values of the SSD and RetinaNet models were very close, the test results of the RetinaNet model generated incorrect detection frames. After the analysis, the RetinaNet model used focal loss to deal with the problem of an imbalance between positive and negative samples, but this processing method was not applicable to our Tibia dataset, as it would result in false detection. The performance results of YOLOv7, Fast R-CNN, and YOLOX were not good. The results for \(mAP_{0.50.95}\) did not reach 50%, and it can be seen in the figure that these three target detection models produced a large number \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Parameters/M** & **GFLOPs** & \(mAP_{0.50.95}\) **(\%)** \\ \hline YOLOv8-s & 11.2 & 28.8 & 86.7 \\ SKAttention\(\times\) 3 & 40.2 & 81.8 & 90.5 \\ MHSA\(\times\)3 & 12.2 & 30.7 & 85.5 \\ TripletAttention\(\times\)3 & 11.2 & 29.1 & 88.7 \\ NAMAttention\(\times\)3 & 11.2 & 28.8 & 88.2 \\ EMA\(\times\)3 & 11.2 & 29.6 & 89.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of additions of multi-scale attention mechanisms to the Neck.