Machine vision is one of the key components for industrial automation and intelligent plant. First, the tide of Industry 4.0 swept the world, manufacturing intelligence will inevitably lead to the upgrading of production equipment; second, China’s demographic dividend advantage will gradually weaken, manufacturers tend to machine generations, reduce labor costs; Third, the upgrading of consumption levels, consumers The quality of the product puts higher demands on the manufacturer, which makes the manufacturer increase the quality control. The three interlocking, for the machine vision market, bring new opportunities for development in the field of intelligent manufacturing.
Many North American machine vision vendors say that these high-end visual products are difficult to open up in China more than a decade ago. In recent years, domestic manufacturing merchants are in the process of transformation and upgrading, and they gradually have purchasing intentions for these high-end visual products. Since the domestic machine vision technology market capacity has increased, how do you view the current machine vision market from the perspective of industrial users?
In the industrial field, machine vision acts as the “eye” of production equipment, solves the work that the human eye cannot identify and detect, and realizes the economic benefits of good efficiency and low cost. It is the starting point and the foothold of the development of visual technology. Then, for the customer’s point of view, manufacturers are more inclined to high precision, high accuracy, large field of view automatic zoom detection, and the rapid introduction of software.
The first is the high precision and high accuracy of detection. This is the most basic and direct purpose of replacing the human eye with a machine. Manual inspection is prone to fatigue, resulting in the poor quality of work, and in the field of precision manufacturing, machine vision has an absolute advantage over the human eye. At present, the measurement and judgment of machine vision are very mature. For example, the visual system of Techtronics’ vision, in the robot application of Liqun Automation, has an accuracy rate of over 97%.
Some factories produce large objects, which is a big challenge for visual inspection. In the past, for large-field inspection, two methods were used, one was to cover with two readers, and the other was to use a high-resolution camera, plus light source, lens, and some custom software to operate. These two large-field inspections require professional engineers to perform debugging and maintenance. From the perspective of customer’s use and maintenance costs, it is not ideal, so it is difficult to deploy on a large scale. Cognex’s DATAMAN370 series of code readers launched this year are in line with customers’ needs for large-field inspection, and have been successfully applied in PCD board, packaging, printing, and other fields.
From the perspective of the use of visual technology software, whether the developer can quickly introduce the system will determine the acceptance of the customer to a certain extent. For the system, the simpler the better. The use of the customer’s vision system should be more inclined to “foolish” operation and development. For example, Chuangshi Technology develops the visual platform CKVsionSDK, which modularizes many functions, such as BLOB analysis, arithmetic operation, logic operation, color matching, and other functions into multiple modules. The customer quickly imports the module into the application according to their own needs.
Machine vision is a comprehensive technology whose main markets are in the electronics manufacturing, automotive, pharmaceutical, food and packaging machinery sectors. According to the data of the Prospective Research Institute, the electronics manufacturing industry accounted for 46.57% in the machine vision market, followed by the automotive industry, accounting for 31.02%. In addition, the penetration rate of logistics, food, packaging, printing and other industries have also increased year by year.


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