# Packaging Design Image Segmentation Based on Improved Full Convolutional Networks
## 4 Results
### Performance Verification and Analysis of Model Lightweight Design Method
The results showed that through a lightweight design such as edge knowledge distillation, the proposed model achieved a good balance between performance and efficiency and was suitable for practical application scenarios requiring efficient processing. This study compared the semantic segmentation performance of the improved and lightweight model with the ISS techniques mentioned in references [6; 8; 10; 11]. The results are shown in Figure 12.  
Figure 11: Effect Verification of Edge Knowledge Distillation Module. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Inference Time (s/image)** & **Memory Usage (GB)** & **Model Parameters (M)** & **Pixel Accuracy (\%)** \\ \hline FCN & 0.12 & 5.8 & 20.6 & 85.23 \\ MobileNetV2 & 0.10 & 4.2 & 3.4 & 89.45 \\ ENet & 0.08 & 3.1 & 0.4 & 84.79 \\ Research method & 0.24 & 5.5 & 15.7 & 92.10 \\ Research method (Light) & 0.09 & 3.3 & 2.8 & 91.78 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparative experimental results. In Figure 12a, for the 50 images used in packaging design, the boundary F1 score of the proposed method remained above 96%, with an average of 97.18%. However, the technical performance of the methods in references [6; 8; 10; 11] was not as good as that of the proposed method, with the method in reference [11] showing strong performance fluctuations. In Figure 12b, the segmentation error rate of the proposed method was significantly lower than the four compared methods, with an average of only 1.42%. However, the segmentation error rates of the techniques in references [6; 8; 10; 11] were 4.95%, 4.01%, 5.75%, and 4.23%, respectively. The proposed method showed high performance in ISS tasks, with excellent indicators in boundary detection and overall segmentation accuracy, significantly better than existing technologies in the reference literature. Finally, the four indexes of AUROC, G-Mean, Jaccard index, and F1 value of different methods were studied and compared. The results are shown in Table 6.  
As shown in Table 6, the method proposed in this study had excellent performance in all quality indexes, and AUROC (98.12%) and F1 scores (97.18%) were significantly higher than other reference methods, indicating that this method has significant advantages in the balance of classification ability, accuracy, and recall rate. The results of G-Mean (97.46%) and Jaccard index (85.78%) also showed that it performed well in dealing with complex tasks, especially small targets and boundary segmentation in packaging design. In contrast, the performance of the methods in the references was relatively weak, among which the scores of reference [10; 11] were lower on multiple indicators compared to the methods proposed in the research, showing their limitations in the task of semantic segmentation. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **AUROC (\%)** & **G-Mean (\%)** & **Jaccard Index (\%)** & **F1 Score (\%)** \\ \hline Research & 98.12 & 97.46 & 85.78 & 97.18 \\ method & 95.34 & 93.12 & 81.23 & 93.56 \\ Reference [6] & 94.67 & 92.78 & 80.67 & 94.01 \\ Reference [8] & 92.85 & 90.56 & 78.34 & 91.23 \\ Reference [11] & 93.72 & 91.23 & 79.56 & 92.48 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of Methods Under Different Indicators. Figure 12: Semantic Segmentation Performance Comparison [6; 8; 10; 11].