## 4 Results and Analysis
### VGG-UNet Model Evaluation  
This section compares the performance of U-Net, VGG-UNet, and pre-trained VGG-UNet in the tangerine peel image segmentation task through systematic experiments, with a particular focus on training dynamics and loss convergence characteristics. Prior to model training, all input images were resized to a fixed resolution of 512 \(\times\) 512 pixels to ensure consistency in input dimensions. All models were trained for 100 epochs using the same hyperparameters (initial learning rate = 0.0001 with the Adam optimizer) on a dataset comprising 1680 high-resolution citrus epidermis images (1344 for training and 336 for validation). The evaluation results in terms of MIoU and loss are presented in Table 7.  
Figure 11a,b illustrate the convergence rate and stability during training and validation, respectively. Notably, the pre-trained VGG-UNet demonstrates a significantly superior initialization and feature representation capability. Its initial loss at epoch 1 was 1.15, which is approximately 67% and 68% lower than that of the randomly initialized VGG-UNet (1.82) \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **MIoU 1 (\%)** & **F1 2** & **Loss** \\ \hline UNet & 84.17 (95\%CI 3: 52.892, 73.231) & 0.914 & 0.22 \\ VGG-UNet & 80.54 (95\%CI: 79.862, 80.450) & 0.892 & 0.17 \\ VGG-UNet (pre-train) & 93.58 (95\%CI: 89.482, 90.069) & 0.967 & 0.13 \\ \hline \hline \end{tabular}  
* MIoU: mean intersection over union; 2 F1: F1-score; 3 CI: confidence interval.  
\end{table} 
Table 7: Performance analysis of different semantic segmentation models. Figure 10: Regional division of the actual material board. Figure 9: Clustering image area division. and basic U-Net (1.83), respectively (see Figure 11). Moreover, the MIoU of the pre-trained VGG-UNet was 93.58%, exceeding that of the randomly initialized VGG-UNet (80.54%) and basic U-Net (84.17%) by 13.04 and 9.41 percentage points, respectively (see Figure 12). These results indicate that the use of pre-trained weights effectively alleviates the cold start problem, particularly by capturing general texture features in the shallow convolutional layers. To assess the robustness of the models, we further report the 95% confidence intervals (CI) of MIoU across repeated runs. The pre-trained VGG-UNet showed a narrow CI of [89.482%, 90.069%], indicating high reliability and stability. In contrast, the randomly initialized VGG-UNet had a wider CI of [79.862%, 80.450%], and the basic U-Net showed the lowest robustness with a CI of [52.892%, 73.231%]. We computed the F1-score as a pixel-level performance indicator. As shown in Table 7, the pre-trained VGG-UNet achieved the highest MIoU of 93.58% and a corresponding F1-score of 0.967, significantly outperforming both the basic UNet (F1 = 0.914) and randomly initialized VGG-UNet (F1 = 0.892). These findings underscore the advantage of pre-trained feature extractors in improving both accuracy and consistency of the segmentation performance. Figure 11: (**a**) Comparison of convergence rate and stability during training of each model. (**b**) Comparison of convergence rate and stability during verification of each model. Figure 12: Comparison of MIoU during training for each model. Furthermore, the loss curve of the pre-trained model converged the fastest, reaching a stable plateau at epoch 15 with a loss of 0.15, whereas the randomly initialized VGG-UNet and U-Net only attained stability at epochs 35 and 80, with final loss values of 0.17 and 0.22, respectively. Additional analysis suggests that the parameter space of the pre-trained encoder is closer to an optimal solution. To further validate the segmentation performance of the VGG16-UNet model, tangerine peel images from the test set were segmented and visually compared, as shown in Figure 13. Overall, despite the complex background, the VGG16-UNet model achieved high consistency in multi-target measurements.