# BSD-YOLOv8: Enhancing YOLOv8 for Book Signature Marks Detection
## 3 Experimental Results
### Ablation Experiments  
To evaluate the superiority of LMRNet, we conducted experiments by replacing different lightweight backbone networks with YOLOv8n under consistent training conditions. As shown in Table 5, the detection accuracy of other lightweight backbone networks decreased, while LMRNet achieved the best detection performance. To assess the effectiveness of each enhancement technique proposed in this study, ablation experiments were conducted under the same experimental settings as shown in Table 6.  
Figure 10: Comparison of actual test results: (**a**) original image; (**b**) inference results for YOLOv8n; (**c**) inference results for Improved-YOLOv5s; (**d**) inference results for BSMD-YOLOv8. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Backbone** & **P/\%** & **R/\%** & **mAP50/\%** & **Params/M** & **GFLOPs/G** & **FPS** \\ \hline CSP-Darknet-53(YOLOv8n) & 96.0 & 88.2 & 95.2 & 3.01 & 8.2 & 111 \\ EfficientNetv2 [31] & 67.7 & 57.0 & 68.9 & 2.12 & 2.6 & 118 \\ GhostNet [32] & 95.5 & 84.6 & 93.5 & 2.82 & 7.8 & 115 \\ MobileOne [33] & 93.1 & 80.1 & 91.0 & 2.86 & 8.4 & 113 \\ LMRNet & 97.2 & 94.5 & 97.8 & 0.93 & 10.4 & 120 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of different backbone networks. In YOLOv8n_L, it was demonstrated that the incorporation of LMRNet significantly enhances the model's accuracy, recall, and mAP50, while also notably reducing the number of model parameters. However, in YOLOv8n_L, there was a decrease in accuracy by 0.8%, recall decreased by 1.4%, and the mAP50 decreased by 0.7%. These results indicate that the use of the improved PANet alone does not significantly enhance performance. This is attributed to the fact that the features extracted by the backbone network of YOLOv8n contain a significant amount of noise information, which leads to the fusion of redundant and invalid features by the Low-GD and High-GD modules, ultimately affecting the model's performance. When comparing YOLOv8n_L_I with YOLOv8n_L, it is evident that using Improved_PANet based on LMRNet results in increases in accuracy, recall, and mAP50 by 0.8%, 0.7%, and 0.3% respectively. These findings verify the effectiveness of the improved PANet. Furthermore, in YOLOv8_W and BSMD-YOLOv8, the introduction of the WIoU loss function led to improvements in accuracy, recall, and mAP50. Figure 11 comprehensively displays the evaluation metrics of ablation experiment models over 200 training epochs, with a particular focus on mAP50 and Recall. In the initial stages, all models exhibit a steep surge in both mAP50 and recall, emphasizing the significant training effects achieved during this early phase. As the number of epochs progresses, the metrics gradually plateau, indicating convergence. Overall, the enhancement effect of using LMRNet as the backbone network in YOLOv8n (such as YOLOv8n_L, YOLOv8_L_I, BSMD-YOLOv8) is significantly more pronounced compared to the models that do not use LMRNet (such as YOLOv8n, YOLOv8_I, YOLOv8_W). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Models** & **LMRNet** & **Improved\_PANet** & **WIoU** & **P/\%** & **R/\%** & **mAP50/\%** & **Params/M** \\ \hline YOLOv8n & \(\times\) & \(\times\) & \(\times\) & 96.0 & 88.2 & 95.2 & 3.01 \\ \hline YOLOv8n\_L & \(\surd\) & \(\times\) & \(\times\) & 96.8 & 94.3 & 98.4 & 0.93 \\ \hline YOLOv8n\_I & \(\times\) & \(\surd\) & \(\times\) & 95.2 & 86.8 & 94.5 & 3.50 \\ \hline YOLOv8n\_W & \(\times\) & \(\times\) & \(\surd\) & 96.6 & 88.5 & 95.5 & 3.01 \\ \hline YOLOv8n\_L\_I & \(\surd\) & \(\surd\) & \(\times\) & 97.6 & 95.0 & 98.7 & 1.05 \\ \hline BSMD-YOLOv8 & \(\surd\) & \(\surd\) & \(\surd\) & 98.2 & 96.8 & 99.1 & 1.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of ablation experiments. Figure 11: Training progress plot comparing ablation experiments based on mAP50 and Recall: (**a**) mAP50 vs. Epochs; (**b**) Recall vs. Epochs. Figure 12 compares the detection results of YOLOv8L_I and BSMD-YOLOv8.