# Packaging Design Image Segmentation Based on Improved Full Convolutional Networks
## 4 Results
### Performance Validation and Analysis of Model-Improvement Methods  
This study improved the ISS model to address the issue of edge detail loss. This study first conducted ablation experiments. The encoding network, decoding network, and attention mechanism modules were named A1, A2, and A3, respectively, as shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **A1** & **A2** & **A3** & **PA (\%)** & **MPA (\%)** & **MIoU (\%)** \\ \hline
1 & \(\times\) & \(\times\) & \(\times\) & 95.48 & 86.97 & 80.67 \\
2 & \(\surd\) & \(\times\) & \(\times\) & 96.24 & 87.42 & 81.84 \\
3 & \(\times\) & \(\surd\) & \(\times\) & 96.08 & 87.45 & 82.08 \\
4 & \(\times\) & \(\times\) & \(\surd\) & 96.84 & 87.94 & 81.67 \\
5 & \(\surd\) & \(\surd\) & \(\surd\) & 97.59 & 88.12 & 86.67 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of Ablation Experiment.  
Figure 8: Comparison of Precision and False-Positive Rate.  
In Table 3, PA represents pixel accuracy, MPA represents average pixel accuracy, and MIoU represents average intersection to union ratio. When A1-A3 modules were not introduced, the PA, MPA, and MIoU of the model were 95.48%, 86.97%, and 80.67%, respectively. When a module from A1-A3 was introduced separately, the PA, MPA, and MIoU of the model were all improved to varying degrees. When all A1-A3 were integrated into the model, all indicators reached their optimal levels, with a PA of 97.50%, MPA of 88.12%, and MIoU of 86.67%. Therefore, components A1, A2, and A3 all had a positive effect on enhancing the effect of ISS models, and the best effect was achieved when they worked together. Furthermore, this study utilized the CamVid dataset for training and selected a new dataset, namely the Cityscapes dataset, as the test set to compare the performance of the improved model with its original performance. Meanwhile, the joint pyramid attention network (JPANet) was selected as the comparative algorithm. The results are shown in Figure 9.  
In Figure 9, after 10 training sessions, the accuracy of the proposed model improved from 84.67% to 98.16%, with an average of 98.34% in five tests. The highest accuracy of the model before improvement in testing was only 91.08%, while JPANet's accuracy was 96.04% in the third test. In Figure 9, during the first test, the pre-improved model's recall was only 81.84%, and the improved model's recall was only 82.13%, both lower than JPANet. With the increase of training times, the improved model achieved a recall rate of 98.21% in the tenth training session, and the recall rate was higher than 97% in all five tests, demonstrating superior learning efficiency and small-object segmentation performance. The PA, MPA, and MIoU metrics and parameter sizes of the three methods were compared on two datasets. The results are shown in Figure 10.  
In Figure 10, in the training set, the improved model showed varying degrees of improvement in PA, MPA, and MIoU compared to the unimproved model, with PA values reaching 96.19%, MPA values reaching 86.84%, and MIoU values reaching 78.21%. The three indicator values of the unimproved model were 94.64%, 75.49%, and 55.43%, respectively. Therefore, the improved model had an improvement of about 1.64% on PA, about 15.03% on MPA, and about 41.08% on MIoU. The indicator values of JPANet were located between the improved model and the unimproved model. The parameter count of the improved model reached 31.58 M, while the parameter count of the unimproved model was only 16.94 M, and the parameter count of JPANet was only 12.08 M. In Figure 10, in the test set, the performance of the three algorithms was not significantly different from that in the training  
Figure 9: Comparison Results Before and After Model Improvement.  
set, indicating that the improved model had a high index value and the highest number of parameters. Therefore, this study's method could effectively improve the segmentation performance of the model for small targets, but it also brought negative effects of excessive parameter count.