# _Applied sciences_
## 4 Experimental Evaluation
### Comparison Experiment  
First, K-means, ResNet, U-Net, and the proposed UR-Net were used to semantically segment the earth red and ultramarine blue pixels in the color painting test samples, and the corresponding color segmentation images were obtained and compared with the true results, as shown in Figure 15. The number of K-means clusters is difficult to estimate, and the segmentation effect achieved for the color painting images is not ideal. When the sample dataset is sparse, the segmentation accuracy achieved by the semantic segmentation model trained by ResNet is low, and it cannot accurately recognize the color pixels, easily \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Structure** & **Dataset** & **PA** & **Recall** & **Precision** & **F1-Score** & **IoU** \\ \hline \multirow{2}{*}{UR-Net-2b} & Earth red & 0.9891 & 0.9875 & 0.9364 & 0.9613 & 0.9254 \\  & Ultramarine blue & 0.9872 & 0.9845 & 0.9299 & 0.9564 & 0.9165 \\ \hline \multirow{2}{*}{UR-Net-2i} & Earth red & 0.9890 & 0.9912 & 0.9329 & 0.9612 & 0.9253 \\  & Ultramarine blue & 0.9876 & 0.9764 & 0.9390 & 0.9573 & 0.9182 \\ \hline \multirow{2}{*}{UR-Net-4b} & Earth red & 0.9894 & 0.9812 & 0.9437 & 0.9621 & 0.9269 \\  & Ultramarine blue & 0.9867 & 0.9578 & 0.9497 & 0.9537 & 0.9116 \\ \hline \multirow{2}{*}{UR-Net-4i} & Earth red & 0.9883 & 0.9873 & 0.9314 & 0.9585 & 0.9203 \\  & Ultramarine blue & 0.9877 & 0.9840 & 0.9332 & 0.9580 & 0.9193 \\ \hline \multirow{2}{*}{UR-Net-6b} & Earth red & 0.9882 & 0.9881 & 0.9301 & 0.9582 & 0.9198 \\  & Ultramarine blue & 0.9874 & 0.9600 & 0.9523 & 0.9561 & 0.9159 \\ \hline \multirow{2}{*}{UR-Net-6i} & Earth red & 0.9888 & 0.9886 & 0.9336 & 0.9603 & 0.9237 \\  & Ultramarine blue & 0.9870 & 0.9851 & 0.9280 & 0.9557 & 0.9151 \\ \hline \multirow{2}{*}{UR-Net-8b} & Earth red & 0.9889 & 0.9851 & 0.9371 & 0.9605 & 0.9240 \\  & Ultramarine blue & 0.9868 & 0.9738 & 0.9359 & 0.9545 & 0.9130 \\ \hline \multirow{2}{*}{UR-Net-8i} & Earth red & 0.9882 & 0.9818 & 0.9356 & 0.9581 & 0.9196 \\  & Ultramarine blue & 0.9870 & 0.9532 & 0.9552 & 0.9542 & 0.9125 \\ \hline \multirow{2}{*}{UR-Net-10b} & Earth red & 0.9875 & 0.9900 & 0.9244 & 0.9561 & 0.9159 \\  & Ultramarine blue & 0.9878 & 0.9769 & 0.9401 & 0.9581 & 0.9197 \\ \hline \multirow{2}{*}{UR-Net-10i} & Earth red & 0.9906 & 0.9819 & 0.9510 & 0.9662 & 0.9346 \\  & Ultramarine blue & 0.9890 & 0.9670 & 0.9561 & 0.9615 & 0.9259 \\ \hline \end{tabular}
\end{table} 
Table 4: Accuracy comparison of ten structures of UR-Net. Figure 14: Salt-and-Pepper noise from different structural UR-Net segmentations. (**a**) UR-Net-2b. (**b**) UR-Net-10b. (**c**) UR-Net-2i. (**d**) UR-Net-6i. causing overfitting. Although the semantic segmentation model trained by U-Net can achieve better color consistency, the output color semantic map still contains salt-and-pepper noise, which affects the segmentation accuracy. In this paper, the proposed UR-Net combines the advantages of ResNet and U-Net by adding residual blocks to the U-Net architecture, which make the network recognize pixels more accurately in the training process and remove noise while ensuring segmentation accuracy, thus achieving color consistency. From Figure 15, it can be seen that the segmentation accuracy of the UR-Net model is significantly higher than that of the K-means clustering algorithm and the ResNet model, and the generated salt-and-pepper noise is also considerably reduced compared with that of the U-Net model, so the proposed UR-Net model is more suitable for acquiring the semantic segmentation images of painted colors. Second, the numbers of earth red pixels and ultramarine blue pixels in the segmentation results of the four methods are compared with the true results, as shown in Figure 16. By K-means segmentation, the number of earth red pixels is 42,794, and the number of ultramarine blue pixels is 32,881. By ResNet segmentation, the number of earth red pixels is 70,324, and the number of ultramarine blue pixels is 49,596.