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Machine vision refers to the use of cameras, video cameras, and other sensors, combined with machine vision algorithms, to endow intelligent devices with the functions of human vision for tasks such as object recognition, detection, and measurement. According to the application fields and characteristics of subdivided technologies, machine vision can further be divided into two categories: industrial vision and computer vision, which correspondingly can be classified into two application fields: intelligent manufacturing and smart living.
Industrial vision and computer vision have certain differences in terms of functional goals, hardware requirements, algorithm focus, and industry maturity.
In terms of functional goals, industrial vision mainly addresses repetitive tasks such as positioning, measuring, and inspecting workpieces that previously required human vision; the main task of computer vision is to endow intelligent robots with vision, utilizing distance measurement, object calibration, and recognition to achieve recognition and judgment of external positional information, image information, etc.
In terms of hardware requirements, industrial vision has relatively higher requirements, needing to select industrial cameras based on frame rate, resolution, and other specifications according to its needs; while computer vision, except for a few special cases, generally has lower requirements for cameras or video cameras.
In terms of algorithm focus, industrial vision algorithms often emphasize improving accuracy; while computer vision algorithms are relatively more complex, focusing on or employing mathematical logic or deep learning methods for object calibration and recognition.
In terms of industry maturity, industrial vision is already relatively mature, with widespread applications in measurement and inspection in industries such as semiconductors and packaging; while computer vision, overall, is still in its early stages, with numerous startups emerging.
In fact, machine vision, as an important branch of artificial intelligence, has begun to have a significant impact on society in collaboration with other technologies. Although there are varying degrees of intersection among the various cutting-edge technologies in the field of artificial intelligence, making it difficult to subdivide, the artificial intelligence industry can be categorized based on the main technologies employed into: deep learning, machine vision, natural language processing, speech recognition, situational awareness computing, pattern recognition, etc. Among these, deep learning, machine vision, and natural language processing are the three main fields with the most participation from high-quality enterprises, and they are also the areas where people have conducted the most exploration and practical application in artificial intelligence, yielding the richest results.
Deep learning, machine vision, and natural language processing represent the three main functions of computer intelligence development, respectively teaching machines to think, observe the external world, and understand text. As a foundational functional technology, machine vision is a prerequisite for the autonomous action of robots, enabling computer systems to observe, recognize, and judge the external environment, effectively endowing robots with vision, which plays an extremely important role in the development of artificial intelligence.
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