# Portrait Semantic Segmentation Method Based on Dual Modal Information Complementarity
## 4 Experiments
### Network Comparison Experiment  
In this section, in order to evaluate the performance of the model proposed in this paper, it is compared with the evaluation indexes of the algorithm models U-net [5], LinkNet [32], PortraitNet [33], Deeplab v3+ [7] and Trans UNet [34]. The experimental results are shown in Table 4. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Network** & **Attention Gate** & **CBAM** &
\begin{tabular}{c} **Mixed-Parallel** \\ **Attention Module** \\ \end{tabular} & **PA/\%** & **MIoU/\%** \\ \hline Network\_1 & & & & 94.72 & 92.06 \\ Network\_2 & ✓ & & & 95.22 & 93.02 \\ Network\_3 & & ✓ & & 95.66 & 93.35 \\ Network\_4 & & & ✓ & 95.80 & 93.56 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of ablation experiment based on Attention module, where ✓ means that the network contains this module. \begin{table} \begin{tabular}{c c c c} \hline \hline
**\(\alpha\) Value** & \(\beta\)**Value** & **PA/\%** & **MIoU/\%** \\ \hline
0.1 & 0.9 & 94.85 & 92.26 \\
0.2 & 0.8 & 94.88 & 92.28 \\
0.3 & 0.7 & 94.91 & 92.32 \\
0.4 & 0.6 & 94.90 & 92.30 \\
0.5 & 0.5 & 94.86 & 92.27 \\
0.6 & 0.4 & 94.82 & 92.23 \\
0.7 & 0.3 & 94.80 & 92.19 \\
0.8 & 0.2 & 94.77 & 92.17 \\
0.9 & 0.1 & 94.76 & 92.15 \\ \hline \hline \end{tabular}   
As can be seen from Table 4, the algorithm proposed in this paper performs best on the two segmentation indexes PA and MIoU. Among them, compared with U-net, the benchmark model in this paper, the improvement in PA and MIoU is 2.17% and 3.42%, respectively, which means that the improvement is obvious. Compared with LinkNet, which is improved based on the U-net network and the residual structure in the encoder stage, the model in this paper has a 1.35% and 2.36% improvement in PA and MIoU. Compared with the lightweight model PortraitNet, the proposed model also has advantages of 0.94% and 1.15% in PA and MIoU. Meanwhile, compared with Deeplab v3+, which also uses ASSP module for feature extraction optimization in the encoder stage, the model in this paper can also improve by 0.67% and 0.79% in PA and MIoU. Finally, compared with Trans Unet, with transformer introduced in the encoder stage, the model in this paper can also lead by 0.29% and 0.32% in PA and MIoU. At the same time, the parameter number and average segmentation time of this model are less than that of Trans Unet. In order to evaluate the segmentation effect of the research model in this paper more intuitively, two groups of images are selected to display the visual effect of the actual results of each model in the above figure. Among them, the second group of experimental images are portrait segmentation images in the low-light scene. The segmentation results are shown in Figure 7.  
It can be seen that the U-net model loses the target portrait in Figure 7(a1,a2). The segmentation effect of LinkNet and PortraitNet is better than that of U-net, but in both Figure 7(c1,d1), at the point of contact between the portrait and the table, some degree of mis-segmentation of the background being categorized as portrait semantics occurs. Still \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Network** & **PA/\%** & **MIoU/\%** & **Parameters/M** & **Spent Time Each Image/S** \\ \hline U-net & 94.72 & 92.06 & 44.93 & 0.017 \\ LinkNet & 95.54 & 93.12 & 47.82 & 0.020 \\ PortraitNet & 95.95 & 94.33 & 49.14 & 0.016 \\ Deeplab V3+ & 96.22 & 94.69 & 54.71 & 0.022 \\ Trans Unet & 96.60 & 95.16 & 58.16 & 0.035 \\ Ours & 96.89 & 95.48 & 56.02 & 0.029 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of different segmentation model. Figure 7: Segmentation results of different models. **a** is the original image; **b–f** is the segmentation effect of U-net, LinkNet, PortraitNet, Deeplab v3+ and Trans UNet; and **g** is the segmentation effect of this paper’s segmentation model. worse, in the low-light scenes of Figure 7(c2,d2), both show the more obvious phenomenon of portraits being misjudged as backgrounds.