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Machine vision industry chain, who is the next dark horse

Release time:2024-08-13Hits:

The domestic machine vision industry chain is sorted out and simple. We can divide the machine vision industry chain into bottom developers (core components and software providers), integration and software service providers (secondary development), and core components and software can be subdivided into light sources, lenses, industrial cameras, image acquisition cards, image processing software, etc. In the current cost composition of the entire machine vision system, parts and software development account for 80%, which is the absolute core link and value acquirer in the industrial chain. 

 

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 From the perspective of technical barriers: 

 1) Software is the main barrier, and the underlying algorithm library is the core. At present, it is a monopoly of foreign-funded enterprises, mainly cultivated by decades of automation processes abroad. The domestic automation process is not long, most software algorithms are still in the R&D stage, and the applications are not well done well. 

 2) Application-level technology is also very critical, mainly to master Know-How in different application environments and make adaptive products. 

 At present, there are four main types of market participants in the domestic machine vision industry: International Integrated Automation Company, International Professional Machine Vision Company, Domestic Professional Machine Vision Company and Domestic Automation Equipment Company. Among them, at the bottom-level developer level, international enterprises still dominate. Domestic companies are more likely to lay out at the secondary development level with lower added value (in the form of system integration and assembly and production automation machines), and on this basis, they gradually try to go upstream core links. 

 

 Cost composition of machine vision system 

 Light source: Light source is the most fully localized link. The quality of light sources lies in contrast, brightness and sensitivity to position changes. The machine vision industry mainly uses LED light source products. At present, there is no universal machine vision lighting device, and there is a personalized solution for each specific application instance to achieve the best results. At present, the localization of the light source industry is high, and the competition is relatively fierce. ▲Internal and external light source participating enterprises 

 Lens: Domestic enterprises with low-end lenses are competitive, and high-end lenses basically rely on imports. The basic function of the lens is to realize beam modulation and complete signal transmission on the photosensitive surface of the image sensor. Industrial lenses can be mainly divided into fixed-focus lenses, fixed-fold lenses, telecentric lenses, continuous magnification lenses, etc. Different lenses are applied to different industrial sites according to requirements, and the price gap is also large. 

 Industrial cameras: Industrial cameras are mainly imported from Europe and the United States, and domestic brands are gradually imported from the low-end market. Industrial cameras are the core components of industrial vision systems. Their essential function is to convert optical signals into electrical signals, requiring higher transmission power, anti-interference and stable imaging capabilities. 

 Image acquisition card: Image acquisition card is relatively complete and mature in China, also known as video capture card. This part is usually a card plugged into a PC. This capture card connects the camera to the PC. It obtains data from the camera (analog signal or digital signal), and then converts it into information that the PC can process. 

 Image processing software: Image processing software is basically monopolized by foreign enterprises, and domestic enterprises have a layout in secondary development. Industrial vision software performs various operations on digital signals to extract the characteristics of the target, and then control the equipment movements on site according to the discriminant results, and automatically complete the collection, display, storage and processing of images. At present, the more popular development mode is "software platform + visual development package". The development package is based on the software platform to encapsulate various commonly used image processing algorithms. Software engineers can directly call the encapsulated algorithms to realize various complex image processing functions and reduce the difficulty and workload of secondary development. 

 System integration: Domestic manufacturers are developing rapidly on the integration side, especially in some fields where foreign capital has not yet been laid out, or in non-standard automation fields such as 3C. Domestic integration manufacturers simply carry out a small profit margin for secondary development. After completing a good layout downstream of an industry, they will try to gradually develop and extend the underlying upstream to import and replace core software and hardware. 


 Machine vision is the most important downstream. 

 Machine vision is widely used in electronics and semiconductors, automobile manufacturing, food packaging, pharmaceuticals and other fields, among which electronics and electronics are the most important application fields of machine vision at present. 

 1. Downstream Application Field - Electronics 

 According to the data of the Forward-looking Research Institute, the electronics industry contributes to nearly 50% of the demand for machine vision, mainly for high-precision manufacturing and quality testing such as wafer cutting, 3C surface detection, touch screen manufacturing, AOI optical detection, PCB printing circuit, electronic packaging, screen printing, SMT surface mounting, SPI tin paste detection, semiconductor alignment and recognition. In the case of the iPhone, more than 70 systems are needed throughout its production process. In the future, global demand in consumer electronics such as smartphones, tablets and wearable devices is expected to explode. 

 Taking the 3C industry as an example, we judge that the machine vision demand in the industry will continue to grow rapidly in the future. The main demand comes from several aspects: 1) The progress of visual technology (many glass and screen defect detection technology cannot be realized at present) promotes the widening of the applicable field; 2) With the gradual mid-to-high-end domestic smartphones and the increase in the profit margin of mobile phone manufacturers, the application of visual detection in domestic mobile phone production lines is expected to be promoted. 

 2. Downstream Application Field - Automobile 

 According to the data of the Forward-looking Research Institute, the automotive industry contributes about 15% of the demand for machine vision, which is mainly used in the manufacturing process of almost all systems and components such as body assembly detection, panel printing quality detection, character detection, precision measurement of part size, workpiece surface defect detection, free surface detection, gap detection, etc. At present, a production line is equipped with more than a dozen machine vision systems. In the future, with the higher requirements for detection of automobile quality control, automotive intelligence and lightweight trends, the demand for machine vision technology will gradually increase. 

 For example, the 3D vision system can measure the gap and align each vehicle with high accuracy, and fully detect all the doors and bodies of the assembly. The 3D vision system can also help chassis manufacturers automate the shelving, removal and detection of body plates in the shelves, and detect defective components on the shelves before the automatic equipment picks up defective components, thus reducing the welding of defective components. 

 3. Downstream Application Field - Pharmaceutical 

 According to the data of the Forward-looking Research Institute, the pharmaceutical industry has contributed about 7% of the demand for machine vision, which is mainly used in bottle packaging defect detection, capsule packaging quality detection, particle detection, production date coding detection, pill color recognition and sorting, etc. At present, there are 1-2 sets of machine vision systems on the assembly lines of most enterprises, and the actual demand should be at least five. In the future, with the acceleration of automation, upgrading and transformation of the pharmaceutical industry, the penetration rate will continue to increase. 

 For example, in the post-pharmaceutical packaging detection process, machine vision can be used to quickly and accurately detect whether the object is intact. By setting an image sensor, the picture information of the packaged object can be obtained, and each pillicle or bottle can be detected and compared through preset area parameters. In this way, the broken pillicles or missing bottle packaging will be detected and passed correctly and normally.  

 4. The Downstream Application Field - Food 

 Food and packaging are also important downstream fields of machine vision applications. They are mainly used for high-speed detection, appearance packaging detection, food packaging leak detection, appearance and internal quality testing, sorting and color selection. The dosage of a single production line varies greatly among different products. At present, machine vision is widely used in large food enterprises (such as Yili and Mengniu), but the overall penetration rate in the industry is not high. 

 For example, food sorters are widely used in the European fresh goods market, and multiple cameras are generally used to capture the entire surface image of the product. When the product is basically circular, there is a mechanism in the loophole to allow the product to rotate under the camera. Shapes can be sorted according to the maximum diameter and minimum diameter, proportional relationship, etc. The color is generally determined based on the entire surface that has been scanned. Identification methods such as simple percentage, strength histogram, definition of maximum or minimum area, etc.


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