## 1 Introduction
[1] addressed the problem of inaccurate positioning of traditional steel tapes by designing an automatic calibration device for steel tapes, simplifying the recognition process with a mechanical structure, and designing three different recognition algorithms based on the pictures of different types of tapes to adapt to different types of steel tapes. The errors of the three algorithms were within the allowable range after testing. However, this proposal is only applicable to changes in the length of the residual-input tube of the plasma bag, and cannot address changes in the input-tube position. [PERSON] et al. [2] addressed the problem of inaccurate measurement of solder by capturing images from two different perspectives, which achieved a higher accuracy when compared with single-perspective methods. This method is suitable for image processing with non-overlapping features and can improve accuracy by using multiple perspectives. However, taking samples from the side of the plasma bag means the plasma bag will overlap with the residual-input tube in the camera, which will be challenging to implement in this study. [PERSON] et al. [3] used machine vision and probabilistic neural networks to classify pure color, colored, and recycled woolen fabrics based on their color, addressing the problem of difficulty recognizing similar colors in existing visual processing algorithms. [PERSON] et al. [4] evaluated various features of printed circuit board holes by using image registration, geometric image transformation, labeling, and matching methods to create an AOI system. The system evaluated a series of features including missing holes and incorrectly positioned holes. Its measurement accuracy was superior to existing technologies. In addition to research on the visual aspects, there are different algorithms designed for detection based on various working conditions and requirements in order to adapt to a wider range [5]. For instance, multiple cameras, viewpoints, and dimensions can be employed to record surfaces and directly identify foreign objects [6]. Regarding visual applications in welding, some researchers have established a weld seam coordinate system by extracting feature points and calibrating them using algorithms to enhance measurement efficiency and accuracy. Others have used machine vision to perform adaptive correctionsfor the height variation of welds caused by the thermal deformation of the welded workpiece, which significantly improves the welding quality [7; 8]. In addition, deep learning combined with machine vision has been shown to achieve good results in irregular feature defect detection, enabling the detection of very small scratches [9; 10]. This approach has also been applied in many food inspection scenarios, where color or texture features are used to identify food quality through a combination of various prediction models and algorithms [11; 12]. Moreover, two different paths, namely machine vision and deep learning, have been separately verified to detect defects, and it has been pointed out that each approach has its advantages and disadvantages for different types of defects [13]. Finally, vision and servo system integration can be employed to maintain a stable camera level status by simultaneously decoupling the machine kinematics and imaging geometry for control [14]. Domestic and international scholars have conducted many studies covering various aspects regarding machine-vision-based systems, such as precision, color, shape, and position. However, there has been no research on the recognition of the residual-input tube end of a plasma bag. Therefore, this study focuses on the cutting process of the residual-input tube end of a plasma bag and explores the process requirements for cutting the input tube end. Based on the display results, the execution mechanism is driven to cut the end of the input tube. Finally, a residual-input tube-end cutting system for plasma bags is successfully designed.