# Mechanical Assembly Monitoring Method Based on Semi-Supervised Semantic Segmentation
## 1 Introduction  
Mechanical assembly is an important step in the processing of mechanical products, and assembly quality directly affects the quality of the product. At present, the manufacturing industry produces large-scale customized products, which makes the assembly process very cumbersome. When operators are faced with extremely complex assembly work, ignoring a small detail may lead to assembly errors and reduce product quality. Vision-based monitoring is an effective and efficient monitoring method, which has been widely used in industry because of its noncontact and nondestructive detection capability [1]. [PERSON] et al. [2] realized the monitoring of the driver's state based on the image of the driver. [PERSON] et al. [3] used a vision-based action recognition method to monitor the behaviors related to autism spectrum disorders (ASDs). [PERSON] et al. [4] used images of a micro tool and image processing methods to monitor tool wear. [PERSON] et al. [5] employed an industrial camera to photograph the surfaces of inner and outer surfaces of cylindrical bores and classified the captured surface images and combined them with an integrated learning approach to monitor the wear of milling operations. Additionally, vision-based monitoring can also be used for the assembly monitoring of mechanical parts. As an approach for real-time monitoring of mechanical assembly, assembly monitoring can effectively avoid problems such as assembly errors caused by operator fatigue and other factors and ensure the assembly quality of products. At present, various methods have been proposed for assembly monitoring. For the finishing of large parts, [PERSON] et al. [6] proposed a computer vision-based manual assembly monitoring system to monitor the assembly process. [PERSON] et al. [7] developed a monitoring system for human-machine collaborative assembly based on 3D hand pose estimation, and the system was experimentally verified on the assembly lines for power protectors. [PERSON] et al. [8] presented a mechanical assembly monitoring scheme based on change detection, which can monitor the parts with changes in the assembly process from multiple angles and segment them. [PERSON] et al. [9] designed an assistance system for assembly based on object detection, and experiments were conducted on an explosion-proof tubular lamp. The results indicate that the use of this system effectively reduces assembly errors. [PERSON] et al. [10] proposed an object-detection-based recognition system for tools, components, and assembly actions to ensure product quality. Most of the above assembly monitoring methods use object detection. However, when the parts of the assembly are small and dense, it will be difficult to perform object detection on each part. In this case, the method of semantic segmentation can be used instead of object detection for assembly monitoring. [PERSON] et al. [11] have proposed a fully supervised semantic segmentation network for monitoring the mechanical assembly process. Because the parts of the assembly are dense and of the same color, assemblers are prone to misassemble or missing assembly during the assembly process, especially for small parts. In response to this problem, inspired by the literature [11], this paper uses the semantic segmentation method to identify the pixels of each part of the assembly image and annotates the pixels of different parts with different colors. This facilitates the identification of parts during the assembly process and helps the assembler to check for missing or misplaced parts in the assembly sequence. However, in the training process of semantic segmentation, a large number of accurate per-pixel labeling operations are required. To reduce the number of labels to save costs, this paper applies a semi-supervised method. Compared with the fully supervised method, the semi-supervised method adopts a mixture of a small amount of labeled data and a mass of unlabeled data.