# Mechanical Assembly Monitoring Method Based on Semi-Supervised Semantic Segmentation
## 4 Experiments
### Ablation Experiments  
Ablation experiments are conducted to verify the role of each module in the proposed AdvSemiSeg-MA network. Combined with four innovative works, the performance of each module is verified on the assembly image dataset established in this study, and semi-supervised training is adopted. In the synthesized assembly depth image dataset, 1/8 of the training set is randomly selected as the labeled dataset, and the rest is used as an unlabeled dataset. In addition, this study focuses on the segmentation results of the assembly. Hence, background pixels are removed when calculating MIoU. The ablation experiments use the assembly image dataset to train the network, and there are five experiments in total: Experiment 1 (T1) uses the AdvSemiSeg network. Experiment 2 (T2) uses the AdvSemiSeg + ASFF network. Experiment 3 (T3) uses the AdvSemiSeg + ASFF + RFASPP network. Experiment 4 (T4) uses the AdvSemiSeg + ASFF + RFASPP + CoordConv network. Experiment 5 (T5) uses the AdvSemiSeg-MA network proposed in this study, i.e., based on Experiment 4, the convolution of the discriminator network is spectrally normalized and the depth is deepened. The results of each experiment on the assembly image dataset are presented in Tables 1 and 2. In this study, the AdvSemiSeg network in Experiment 1 (T1) is used as the baseline. The effect image of the various experiments on the assembly image datasets is shown in Figure 7.  
As shown in Table 1, by comparing the results of Experiments T1 and T2, it can be seen that after adding the AFF module, compared with the T1 network, the MIoU of the T2 network increases by 3.755%, the PA increases by 0.372%, the MPA increases by 1.537%, and the F1 increases by 1.199%. In Figure 7, comparing the segmentation results of Experiments T1 and T2, it can be observed that the application of the AFF multiscale output fusion method improves the boundary segmentation accuracy of the image. This indicates that the AFF fusion method can enable the network to make full use of the features of different scales and effectively fuse high-dimensional and low-dimensional features. Meanwhile, the comparison of the results of Experiments T2 and T3 indicates that after adding the RFASPP module, compared with the T2 network, the MIoU of the T3 network increases by 0.348%, the PA increases by 0.004%, the MPA increases by 0.368%, and the F1 increases by 0.011%. In Figure 7, comparing the local amplified image 1 in the segmentation results of T2 and T3, it can be observed that adding the RFASPP module can reduce the fault. This demonstrates that the RFASPP module can improve the ability of the model to extract deep features. Moreover, comparing the results of Experiments T3 and T4, it can be seen that after adding the CoordConv module, compared with that of the T3 network, the MIoU of the T4 network increases by 0.177%, the PA increases by 0.029%, the MPA increases by 0.096%, and the F1 increases by 0.023%. In Figure 7, comparing the local amplified images 1 and 2 in the segmentation results of Experiments T3 and T4, it can be observed that the addition of the CoordConv module can further reduce the fault. This indicates that the CoordConv module adding the position into the convolution makes the convolution have spatial position perception ability and enables the semantic segmentation network to be position-sensitive. Moreover, by comparing the results of Experiments T4 and T5, it can be seen that after adding SN to the discriminator network, compared with the T4 network, the MIoU of the T5 network increases by 0.203%, the PA increases by 0.016%, the MPA increases by 0.106%, and the F1 increases by 0.06%. In Figure 7, comparing the local amplified images 1 and 2 in the segmentation results of Experiments T4 and T5, it can be observed that deepening the discriminator network and adding spectral normalization can alleviate the problem that a single pixel is predicted as the background.