# An Oracle Bone Inscriptions Detection Algorithm Based on Improved YOLOv8
## 3 Results
### Result Analysis  
#### 3.2.1 Quantitative Analysis  
* Adding a Small Target Detection Head Experiment  
After integrating the small target detection head component, precision was almost unchanged compared with the baseline model, but the recall was enhanced by 1.4%, and the F-measure increased from 0.826 to 0.832. The improved recall rate indicates that the model's ability to identify true positives was enhanced, thereby enabling the more accurate reduction of false negatives. The experimental data show that the detection performance underwent a modest enhancement after adding the small target detection head. However, FLOPs increased by approximately 50% over the baseline model. * Improving Loss Function Experiment  
Changing the loss function from CIoU to WIoU resulted in a minor improvement in recall compared with the baseline model, with a 2.9% improvement in model accuracy. The improved accuracy indicates more precise model predictions, which enhance reliability, reduce misjudments, and ultimately improve decision accuracy. The experimental results show a 1.53% increase in the comprehensive F-measure metric, showing that modifying the loss function is the most significant improvement in the entire algorithm. Some of the images in the OBI detection dataset are of poor quality, and the WIoU loss function prioritizes ordinary-quality anchor boxes, thus improving the overall detection performance. * Adding Attention Module Experiment  
After adding the CBAM to the ninth layer of the backbone network, the model's recall rate increased by 0.7%, the model's accuracy improved by 0.3%, and the comprehensive indicator F-measure increased by 0.5% compared with the baseline model. The images in the OBI detection dataset contain a lot of noise. The attention module enabled the model to concentrate on important information, so the detection performance was improved to varying degrees by incorporating different attention mechanisms. This improvement contributes to the model's ability to provide more comprehensive and reliable results. * The Proposed Algorithm Experiment  
In the ablation experiment--in addition to testing the contribution of three improvement strategies separately--the effects of any combination of two improvement strategies were also tested and compared with the improvement plan adopted in this study. The experimental outcomes indicate that the proposed improvement plan had the best effect, with a 1.78% increase in F-measure. However, the algorithm's time complexity also increased by approximately 50%. To assess the generalization capability of the model, testing was conducted on the training and validation sets, as shown in Table 4. The improved model's F-measure was 1.76% higher on the training set and 1.78% higher on the validation set compared with YOLOv8. The results indicate that the improved model and YOLOv8 have the same generalization ability. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Precision** & **Recall** & **F-Measure** &
\begin{tabular}{c} **FLOPs** \\ **(GFLOPs)** \\ \end{tabular} \\ \hline YOLOv8\_n + small target & & & & \\ detection head + WIoU + GAM & 0.866 & 0.821 & 0.8429 & 14.0 \\ YOLOv8\_n + small target & & & & \\ detection head + WIoU + SE & 0.856 & 0.829 & 0.8423 & 12.6 \\ YOLOv8\_n + small target & & & & \\ detection head + WIoU + EMA & 0.86 & 0.821 & 0.84 & 12.7 \\ YOLOv8\_n + small target & & & & \\ detection head + WIoU + CBAM & 0.85 & 0.837 & 0.8434 & 12.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The results of embedding the attention module. * Comparison of the Results of the Improved Algorithm with Other Algorithms  
To further assess the model's performance, we compared the algorithm introduced in this study and various other object detection techniques. These algorithms include common first-stage and second-stage mainstream models as well as some efficient algorithms proposed for this dataset.