1. Introduction to the Concept of Machine Vision In the process of production, humans face limitations in their own capabilities, thus inventing and creating many intelligent machines to assist or replace humans in completing tasks. Intelligent machines can simulate human functions, perceive the external world, and effectively solve problems that humans cannot solve. Humans perceive the external world mainly through sensory organs such as vision, touch, hearing, and smell, with approximately 80% of information acquired through vision. Therefore, endowing machines with human-like visual capabilities is extremely important for intelligent machines. In modern industrial automated production, various inspection, measurement, and part identification applications are involved, such as dimensional inspection of automotive parts and integrity inspection of automated assembly, automatic component positioning on electronic assembly lines, printing quality inspection of beverage bottle caps, and barcode and character recognition on product packaging. These applications share the common characteristics of continuous mass production and extremely high requirements for appearance quality. Typically, highly repetitive and intelligent tasks like this can only be completed through manual inspection. We often see hundreds, even thousands, of inspection workers performing this process behind modern factory assembly lines. This significantly increases labor and management costs for the factory, yet still cannot guarantee a 100% pass rate (i.e., "zero defects"). In today's competitive business environment, even a 0.1% defect rate is unacceptable. Sometimes, tasks like precise and rapid measurement of minute dimensions, shape matching, and color recognition simply cannot be performed continuously and stably by the human eye, and other physical quantity sensors are largely ineffective. At this point, people began to consider combining the speed, reliability, and repeatability of computers with the high intelligence and abstraction capabilities of human vision, gradually forming a new discipline—machine vision. Machine vision is the science and technology that studies how to use computers to simulate the macroscopic visual functions of organisms. In simple terms, it's about using machines to replace the human eye for measurement and judgment. First, a CCD camera converts the captured target into an image signal, which is then transmitted to a dedicated image processing system. Based on pixel distribution and information such as brightness and color, this signal is converted into a digital signal. The image system performs various operations on these signals to extract the target's features, such as area, length, quantity, and position. Finally, based on preset tolerances and other conditions, the system outputs results such as size, angle, offset, number, pass/fail, presence/absence, etc. Machine vision is characterized by automation, objectivity, non-contact operation, and high precision. Compared to general image processing systems, machine vision emphasizes accuracy, speed, and reliability in industrial environments. Machine vision is a relatively new and rapidly developing research field. Research began in the 1950s with statistical pattern recognition of two-dimensional images. In the 1960s, Roberts began research on three-dimensional machine vision. In the mid-1970s, the MIT Artificial Intelligence Laboratory officially launched a course on "Machine Vision." Starting in the 1980s, a global research boom began, and machine vision experienced rapid development, with new concepts and theories constantly emerging. Currently, machine vision remains a very active research field, with related disciplines including image processing, computer graphics, pattern recognition, artificial intelligence, and artificial neural networks. 2. System Composition and Classification of Machine Vision A typical machine vision system generally includes the following components: light source, lens, CCD camera, image processing unit (or image capture card), image processing software, monitor, communication/input/output unit, etc. The output of the vision system is not an image or video signal, but rather the detection result after processing, such as dimensional data. The host computer, such as a PC or PLC, obtains the detection result in real time and commands the motion system or I/O system to execute corresponding control actions, such as positioning and sorting. Based on the operating environment, machine vision systems can be classified into PC-based systems and PLC-based systems. PC-based systems utilize their openness, high programming flexibility, and user-friendly Windows interface, while also having a lower overall system cost. The system includes a high-performance image capture card, typically capable of connecting multiple lenses. Regarding supporting software, there are several levels from low to high, such as DLLs for C/C++ programming under Windows 95/98/NT, ActiveX controls providing graphical programming environments for VB and VC++, and even object-oriented machine vision configuration software under Windows, which users can use to quickly develop complex and advanced applications. In PLC-based systems, vision functions more like an intelligent sensor. The image processing unit operates independently of the system, exchanging data with the PLC via a serial bus and I/O. System hardware typically utilizes high-speed dedicated ASICs or embedded computers for image processing, while system software is stored in the image processor. Configuration of menus displayed on a monitor is achieved through a simple device similar to a game keyboard, or software is developed on a PC and then downloaded. PLC-based systems are characterized by high reliability, integration, miniaturization, high speed, and low cost. 3. Applications of Machine Vision Systems Currently, the application of vision systems is booming internationally, while in China, industrial vision systems are still in the conceptual introduction stage. Leading companies in various industries, having solved the problem of production automation, have begun to focus on measurement automation. Machine vision is highly suitable for measurement, inspection, and identification in mass production processes, such as: part assembly integrity, assembly dimensional accuracy, part machining accuracy, position/angle measurement, part identification, characteristic/character recognition, etc. Its largest application industries are: automotive, pharmaceutical, electronics and electrical, manufacturing, packaging/food/beverage, and medical. Examples of applications include checking the machining precision of car dashboards, rapid positioning of electronic components on high-speed pick-and-place machines, checking the number of pins, identifying characters printed on IC surfaces, checking capsule wall thickness and appearance defects in capsule production, checking the number and breakage of balls in bearing production, identifying production dates on food packaging, and checking the placement of labels. Here are some examples: Production line and assembly line quality inspection: Checking printing accuracy, shape and size inspection, position inspection, surface inspection. Speed: Dynamic or static target inspection, inspection speed (throughput) up to: 10 items/second . The most stringent quality inspectors during bottling: Bottle classification, label inspection, defect identification, filling level measurement. Supports dynamic inspection, throughput up to: 25 items/second. Component measurement: Length measurement: accuracy up to 1/1000 mm. Angle measurement, area measurement. Metric unit output. Supports dynamic or static inspection, throughput up to: 25 items/second. Integrity inspection: - Ensures labels match the actual items. Label printing accuracy. Shape contour inspection, surface inspection, code recognition. Can use strobe or continuous light source. Supports dynamic or static inspection. Throughput: 10 items/second. Detecting toothpaste tube edge burrs: Rotating the object to identify its position. Detecting burrs or other obstructions at the tube opening. Overall image evaluation via asynchronous triggers . Monitoring adhesive during pigment box production: Solution: Before placing pigment blocks, monitor the amount of glue injected into each compartment of the pigment box to avoid injecting too much or too little . Checking if the drive shaft is correctly installed and the coding is consistent . Solution: Automatic detection of code presence and the position of seals and clips. Applying machine vision systems can significantly reduce inspection costs, improve product quality, and accelerate production speed and efficiency. As a high-precision, non-contact measurement solution, vision systems involve optical and image processing algorithms and are highly specialized products. In the entire measurement and control system, they often need to cooperate with motion control systems to complete position correction and feed control. Furthermore, when performing synchronous and continuous inspection of multiple processes on a production line, the vision system must have distributed networking capabilities. The combination of machine vision with advanced technologies such as motion control and network communication is changing the face of industrial automation production. Currently, system integrators like Beijing Sitong Motor Co., Ltd. have emerged in China with technical backgrounds in motion control, machine vision, and network communication. Their professional technical support and service capabilities make them a bridge between original suppliers and end users. For packaging companies, those who recognize the trend of technological development and are the first to implement it will undoubtedly be at the forefront of the competition.