# Packaging Design Image Segmentation Based on Improved Full Convolutional Networks
## 4 Results
### Performance Verification and Analysis of Model Lightweight Design Method  
In response to the negative impact of excessive parameter count, this study conducted experiments to verify the lightweight effect of the model. Firstly, the roles of two key components in lightweight design, the adaptive multi-scale feature fusion module, and the SA mechanism embedded with CA were validated and named A1 and A2. A standard semantic segmentation network, DeepLabv3, was selected as the baseline model, and the A1 and A2 components were integrated separately on this basis. Both Cityscapes and CamVid were selected as datasets. Table 4 shows the test results. In Table 4, the PA of DeepLabv3 was 91.23%. After introducing A1, the PA was increased to 92.10%, indicating that feature fusion helped improve the overall pixel classification accuracy. After introducing A2, the PA slightly decreased to 91.87%, possibly due to the attention mechanism focusing more on details and edges, sacrificing some overall pixel accuracy. When both A1 and A2 were introduced simultaneously, the PA reached 93.01%, indicating a synergistic effect between the two modules and significantly improving the pixel level classification accuracy. Similarly, when A1 was combined with A2, MPA and MIoU increased to 88.15% and 81.67%, respectively. Although the parameter count increased from 58.7 M to 65.2 M, the inference time also increased from 0.076 s to 0.092 s. However, considering the significant improvement in performance, this increase in resource consumption was reasonable. Furthermore, this study validated the role of the edge knowledge distillation module in lightweight design methods. It was named A3. The test results are shown in Figure 11. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **PA (\%)** & **MPA (\%)** & **MioU (\%)** & **Parameters (M)** & **Reasoning Time (s)** \\ \hline DeepLabv3 & 91.23 & 86.45 & 79.34 & 58.7 & 0.076 \\ DeepLabv3 + A1 & 92.10 & 87.21 & 80.51 & 62.3 & 0.084 \\ DeepLabv3 + A2 & 91.87 & 87.02 & 80.02 & 60.5 & 0.080 \\ DeepLabv3 + A1 + A2 & 93.01 & 88.15 & 81.67 & 65.2 & 0.092 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Component Function Test Results. Figure 10: Results of Comparison between Training Set and Test Set. In Figure 11, when the A3 component was not added, the running time of the model exceeded 100 ms, and the highest memory occupancy rate reached 68.45%. After using the A3 component, the model no longer occupied memory after running for 72 ms, indicating that the inference had been completed, and the highest memory usage rate was only 24.68%. In Figure 11, when the A3 component was not added, the model's parameter size fluctuated between 50 M and 90 M during 100 training sessions, with an average of 68.46 M. After using the A3 component, the model parameter size was compressed to below 20 M, with an average of only 12.57 M. From this, the edge knowledge distillation module effectively achieved the lightweighting of the model and the improvement of inference efficiency. Further, the traditional basic model and lightweight and efficient model were selected for comparative experiments, including the FCN and mobile network V2 (MobileNetV2) and efficient neural network (ENet). The results are shown in Table 5.  
In Table 5, the proposed model and its lightweight version optimized the use of computing resources while maintaining high segmentation accuracy. Compared with the basic FCN, the proposed model reduced the number of parameters to 15.7 M and the memory usage to 5.5 GB, and the pixel accuracy was greatly improved to 92.10%. In addition, the lightweight version further optimized the computational performance, reducing the number of parameters to 2.8 M, the memory footprint to 3.3 GB, and the inference time to 0.09 s/sheet, which was almost on par with ENet, and the accuracy was only slightly lower than the full model (91.78%).