# Mechanical Assembly Monitoring Method Based on Semi-Supervised Semantic Segmentation
## 4 Experiments
### Comparison Experiments on the Assembly Image Dataset  
To verify the validity of the AdvSemiSeg-MA network proposed in this study, it is compared with AdvSemiSeg, S4 GAN [23], ClassMix [20], PS-MT [31], and U2 PL [32]. Among them, PS-MT and U2 PL networks are recently proposed and achieve good accuracy in semi-supervised semantic segmentation. Because the RGB images of physical assemblies are manually labeled, there are some errors in the labeling images. Therefore, this study uses the synthesized assembly depth image dataset as the training dataset in comparative experiments, and the synthesized assembly depth image dataset established in this study is used as the training set. The accuracy of the comparison experiments is presented in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Network** & **Batch Size** & **Iteration Times** & \
\begin{tabular}{c} **Pretraining** \\ **Weight** \\ \end{tabular} & **PA/\%** & **F1/\%** &
\begin{tabular}{c} **MIoU/\% (Removing** \\ **Background**) \\ \end{tabular} \\ \hline AdvSemiSeg & 2 & 20,000 & COCO & 99.465 & 98.108 & 93.245 \\ S4 GAN & 2 & 40,000 & COCO & 99.373 & 98.181 & 92.918 \\ ClassMix & 2 & 40,000 & COCO & 99.192 & 96.945 & 90.968 \\ PS-MT & 2 & 61,776 & COCO & 99.504 & 98.076 & 93.523 \\ U2 PL & 2 & 78,560 & COCO & 99.659 & 98.440 & 94.123 \\ AdvSemiSeg-MA (Ours) & 2 & 20,000 & COCO & 99.886 & 99.401 & 97.728 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of semi-supervised methods and our method for segmentation based on the synthesized assembly depth image dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **PA/\%** & **MPA/\%** & **F1/\%** & **MIoU/\%** \\ \hline AdvSemiSeg & 99.465 & 96.936 & 98.108 & 93.245 \\ AdvSemiSeg + ASSF & 99.837 & 98.473 & 99.307 & 97.000 \\ AdvSemiSeg + RFASPP & 99.264 & 95.178 & 97.450 & 91.388 \\ AdvSemiSeg + CoordConv & 99.160 & 94.500 & 96.957 & 90.560 \\ AdvSemiSeg + spectrally normalized & 99.259 & 93.913 & 97.684 & 92.503 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The results of each module on the synthesized assembly depth image dataset.  
In Table 4, the number of iterations for training is the original parameter of each network. Meanwhile, the same batch size of 2 is set, and the COCO dataset is used to pretrain the weights. The remaining parameters of each network are set to the optimal parameters of each network. The background is removed when calculating MIoU, and 1/8 of the training set is randomly selected as the labeled data when training the network. Compared with the MIoU values of each network, the AdvSemiSeg-MA network proposed in this study achieves the best segmentation effect on the synthesized assembly depth image dataset. As shown in Table 4, the experimental results indicate that the MIoU, PA, and F1 of the proposed AdvSemiSeg-MA network on the synthesized assembly depth image dataset reaches 97.728%, 99.886%, and 99.401%, which is 3.605%, 0.227%, and 0.961% higher than the current STOA semi-supervised semantic segmentation network (U2 PL), respectively. On the synthesized assembly depth image dataset, the approach used in this paper outperforms the current SOTA semi-supervised semantic segmentation network. This validates the effectiveness of the methods adopted by the proposed AdvSemiSeg-MA network, including the multiscale feature fusion method, the dilated convolution pyramid module RFASPP that imitates the human receptive field, the CoordConv module that allows convolution to obtain position information, and the spectral normalization method that improves the training stability. These modules and methods help to improve the segmentation accuracy on the assembly image dataset.