# DScanNet: Packaging Defect Detection Algorithm Based on Selective State Space Models
## 5 Experiments
### Ablation Experiment  
In this section, we conduct ablation studies to evaluate the impact of different improvements on the final performance of the network and analyze the effectiveness of the module; \"\(\surd\)\" indicates that the corresponding improvement strategy is used in the model. The results of the ablation experiments are shown in Table 4.  
Experiments show that the model accuracy, FPS (detection speed), and [EMAIL_ADDRESS] (detection accuracy) are all improved after the introduction of MEFE Block in the backbone network, which suggests that MEFE Block can enhance the backbone network's ability of extracting and processing the target features and improve the detection accuracy.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **MEFE Block** & **PCR** & **Mamba Block** & **Precision** & **FPS** & **[EMAIL_ADDRESS]** & **Params (M)** \\ \hline
1 & & & & 92.5 & 83.1 & 93.1 & 7.02 \\
2 & \(\surd\) & & & 93.1 & 92.5 & 93.5 & 7.27 \\
3 & \(\surd\) & \(\surd\) & & 94.3 & 96.4 & 93.9 & 5.67 \\
4 & \(\surd\) & \(\surd\) & \(\surd\) & **96.8** & **97.1** & **95.1** & **3.68** \\ \hline \hline \end{tabular} Note: Bold values indicate the best performance.  
\end{table}
Table 4: Ablation experiments were conducted to analyze the effectiveness of the different improvement strategies in this paper.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Precision** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]:0.95** & **FPS** & **Params (M)** & **FLOPs (B)** \\ \hline YOLOv6 [21] & 92.7 & 91.8 & 61.3 & 60 & 9.14 & 17.23 \\ YOLOv7 [22] & 93.5 & 93.1 & 64 & 90.2 & 6.007 & 13 \\ YOLOv8 [23] & 90.1 & 88.7 & 58.2 & 76 & 11.2 & 28 \\ YOLOv9 [24] & 89.5 & 85 & 54.4 & 72.5 & 7.2 & 26 \\ YOLOv10 [25] & 60.2 & 58.7 & 34.9 & 83.4 & 7.2 & 21.6 \\ YOLOv11 [26] & 86.8 & 86.1 & 54.8 & 72.5 & 9.4 & 21.5 \\ YOLOv12 [27] & 85.9 & 82.2 & 47.9 & 65.7 & 9.3 & 21.4 \\ SSD [34] & 84.98 & 86.06 & 50.9 & 52.3 & 62.74 & 26.29 \\ Faster R-CNN [35] & 85.66 & 87.05 & 56 & 40 & 137 & 370.21 \\
**DScanNet** & **96.8** & **95.1** & **66.1** & **97.1** & **3.68** & **9.4** \\ \hline \hline \end{tabular} Note: Bold values indicate the best performance.  
\end{table}
Table 3: We conducted experiments on a home-made defect dataset and compared the current mainstream detection algorithms for this defect.  
After replacing the traditional convolution with the PCR module, the accuracy, FPS and [EMAIL_ADDRESS] are further improved while the number of parameters is reduced. This shows that it can streamline the model structure and improve the computational efficiency, removing the redundant parts without increasing the burden of the model, making the model more efficient.  
After adding Mampa Block, the accuracy and [EMAIL_ADDRESS] of the model were significantly improved, while maintaining a high efficiency, indicating that it has good feature learning and processing capabilities, which can greatly improve the performance of the model, so that the model achieves a good balance between efficiency and performance.  
The addition of MEFE Block, PCR, and Mampa Block all had a positive impact on the performance of the model, especially the addition of the Mampa Block, which significantly improved the accuracy and [EMAIL_ADDRESS] of the model, while maintaining a high efficiency, indicating that the model achieved a good balance between efficiency and performance. These improvements make Model 4 the best choice for comprehensive performance.  
The ablation experiments in this section fully demonstrate the contribution of each module to the overall performance improvement of the DSCanNet algorithm and also verify the rationality and effectiveness of the improvement strategy.