Simply put, machine vision is the use of machines instead of human eyes to perform measurements and judgments. The basic characteristic of machine vision systems is to improve production flexibility and automation. Correspondingly, machine vision has the following main application directions and scenarios.
It can also be divided into high-precision quantitative inspection (such as cell classification microscopic photos, dimensional and positional measurement of mechanical parts) and qualitative or semi-quantitative inspection without measuring equipment (such as visual inspection of products, identification and positioning of parts on assembly lines, defect detection, and assembly integrity detection).
Used to guide the operation and action of robots in a wide range of applications, such as picking up workpieces from a disorderly pile of workpieces from a hopper and placing them in a certain direction on a conveyor belt or other device. For operations and actions in a small range, tactile sensing technology is also required.
In industrial production processes, surface defects, impurities, and other issues that affect product yield are easy to occur. Visual effects can effectively identify defects and improve product yield.
The applicable scenarios mainly include the following two types:
Dangerous working environments that are not suitable for physical labor or situations where human vision is difficult to meet requirements.
In large-scale industrial production processes, artificial visual inspection of product quality is inefficient and not highly accurate. The use of machine vision detection methods can greatly improve production efficiency and automation.
Due to the many advantages of machine vision, in order to improve production efficiency and reduce errors in the production process, the manual links in industrial production are gradually being replaced by machines, and industry has also become one of the largest areas of application for machine vision.
In the production processes of upstream industries such as consumer electronics, automobiles, medicine, etc., machine vision systems and intelligent manufacturing are widely used in product size detection, defect detection, product identification, assembly positioning, etc.
In non-industrial fields, machine vision can be applied to agriculture, medicine, security, finance, and transportation. Machine vision greatly improves the level of agricultural automation and achieves functions such as sorting and quality inspection of agricultural products. It can be used for medical image analysis, and there are also mature applications in medicine and pharmacy. It can also be used for face recognition in the security and finance fields to perform identity verification tasks. In the transportation field, it can be responsible for tasks such as license plate recognition.
There are two important reasons why machine vision technology can be widely used in industrial manufacturing production:
Reliability principle. Compared with traditional human visual inspection, machine vision technology is based on the architecture of artificial intelligence, supported by data sensing and core algorithms, and is not affected by human subjective emotions, which can achieve high reliability for standardized batch products.
Economic principle. The application of machine vision products has obvious cost advantages and higher consistency requirements compared to manual replacement.
With the progress of technology and the decrease in application costs, the penetration rate of machine vision in the industry is constantly increasing, and the entire market is developing rapidly.
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