Machine vision is the use of machines to replace human eyes for measurement and judgment. In the modern industrial automation production process, various measurements, inspections, and identifications are involved, such as the inspection of automotive parts dimensions. In the past, these tasks were all completed manually. We often see hundreds or even thousands of inspection workers behind the factory's production line, which adds huge labor and management costs to the factory, but still cannot guarantee a 100% inspection pass rate (i.e. "zero defects"); In addition, for some dangerous working conditions that are not suitable for manual operation or situations where artificial vision cannot meet the requirements, human eyes cannot detect them at all, so machine vision has emerged. It is particularly suitable for measurement, inspection, and identification in large-scale production processes, with the largest application industries including automotive, electronics and electrical, pharmaceutical, food, beverage, packaging, etc.
Introduction to Machine Vision
As its name suggests, machine vision technology is the use of visual sensors to endow devices with independent visual acquisition, analysis, and processing capabilities. The system collects binary images with a certain depth of field. After the image is digitized, the system analyzes the color and contrast of the target object in the area pre-defined and selected by the user, and then checks the shape, size, position, color, and completeness of the target object, and outputs the results. The available output methods include digital output, RS232 port output, and exchanging information with other intelligent devices through Profibus fieldbus and Ethernet.
Machine vision, as its name suggests, can be imagined as a sensor with eyes. Machine vision is the science and technology that studies the use of computers to simulate biological external or macroscopic visual functions. The primary goal of machine vision systems is to create or restore real-world models from images and then understand the real world. The content of machine vision research includes input devices, low-level vision, mid-level vision, high-level vision, and system architecture.
Application areas of machine vision: Machine vision can be used for teaching recognition and positioning, product inspection, mobile robot navigation, remote sensing image analysis, medical image analysis, security identification, monitoring and tracking, national defense systems, and other occasions.
The earliest example of humans using machine assisted inspection can be traced back to 1870. Thanks to the help of cameras, people understood the movement sequence of the four hooves of horses while running. Nowadays, machine vision using computers as an important inspection method has been widely applied in developed countries.
Due to its ability to quickly calculate numbers, analyze colors, measure lengths, areas, angles, and track moving objects. For a tricky measurement problem, using machine vision to solve it may be the best approach. For example, in the field of healthcare, he can quickly (0.1 seconds) test the color of reagents to determine whether a certain indicator is normal and print the results.
All we need to do is put the sample into the testing slot and quickly take out the results, and then print them out. Comparing colors manually is a time-consuming process.
Because consumers do not care about what went wrong, but rather about the final use effect, many industrial products require machine vision for final quality inspection (whether the paint is painted properly, the text is unclear, the trademark is not properly attached, or the display matches the buttons).
Machine vision applications are widely used in modern automated production processes, such as condition monitoring, finished product inspection, and quality control, due to the ability of machine vision systems to quickly obtain large amounts of information, as well as their ease of automatic processing and integration with design and processing control information. The characteristic of machine vision systems is to improve the flexibility and automation level of production. In some hazardous work environments that are not suitable for manual operation or where artificial vision is difficult to meet requirements, machine vision is commonly used to replace artificial vision; At the same time, in large-scale industrial production processes, using manual vision to inspect product quality is inefficient and inaccurate. Using machine vision inspection methods can greatly improve production efficiency and automation level. Moreover, machine vision is easy to achieve information integration and is the fundamental technology for realizing computer integrated manufacturing.
In short, with the maturity and development of machine vision technology itself, it can be expected that it will be increasingly widely applied in modern and future manufacturing enterprises.
