With the advent of the Industry 4.0 era, the demand for industrial automation and intelligence using the image and machine vision technology has begun to appear widely in various industries, and the machine vision industry will usher in a golden period of large-scale and rapid development.
The development of the machine vision industry also puts forward higher requirements for the implementation standards of machine vision. Whether it is the industrial Internet or hot topics such as intelligent manufacturing and flexible manufacturing, the core lies in the digitization and intelligent upgrading of the production process.
When traditional machine vision solves problems, it usually requires professionals to design image processing algorithms according to the actual situation, which is highly dependent on the level of debugging personnel and has poor stability. Nowadays, deep learning has been widely used in the field of machine vision. Through convolution operation, a large amount of data training is used to automatically generate the most suitable detection logic for the product, which complements the detection ability of traditional algorithms.
Machine Vision Application In Manufacturing Industry
It is mainly used to measure whether the size of parts and various products is qualified. In addition to using industrial cameras for two-dimensional size measurement, three-dimensional size measurement can now be achieved using structured light, 3D TOF, and other technologies. Provide high-precision monitoring of the basic feature size and assembly effect of the product.
The application of vision in measurement, on the one hand, reduces the need for manpower measurement and lowers labor costs; on the other hand, vision measurement has the characteristics of high precision, and the possibility of mismeasurement and misjudgment is extremely low.
Image recognition, simply put, is to use machine vision to process, analyze and understand images, and to recognize various objects and targets. It has very powerful functions. At present, it mainly recognizes various targets such as people and vehicles. In the industrial field, there are often identification requirements for signs with clear information, such as OCR, one-dimensional codes, and two-dimensional codes.
Identifying the identification of clear information helps to improve production efficiency and reduce production costs. Most of the commercial scenarios of image recognition still belong to the blue ocean, and the potential has yet to be developed. At the same time, most of the picture data is in the hands of large Internet companies, and the data resources of start-up companies are scarce.
In industrial applications, machine vision is used to locate parts or products. This positioning application mostly assists robots or other actuators to achieve related actions. Generally speaking, positioning can assist the robot to achieve painting, gluing, grasping, welding and other actions.
In the machine vision application link, the object sorting application is a link established after recognition and detection. The image is processed through the machine vision system, and the product sorting is realized by combining the use of the robotic arm.
In the past production line, materials were placed in the injection molding machine manually, and then the next step was carried out. Now it is using automated equipment to divide materials, in which machine vision systems are used to capture product images, analyze images, and output results, and then put the corresponding materials in fixed positions through robots, so as to realize intelligent, modern, and efficient industrial production. automation.
Artificial intelligence technology can quickly retrieve and query structured video content information such as people, vehicles, and objects. This application makes it possible for the public security system to search for criminals in complicated surveillance videos. In transportation hubs where a large number of people flow, this technology is also widely used for crowd analysis, prevention and control, and early warning.
Food packaging and pharmaceutical industry applications
Machine vision has a wide range of applications in the field of food packaging. It can be used to detect bottle classification and liquid level measurement, label, lid, box inspection, and bottle shape, size, sealing, and integrity. Food packaging is an important guarantee of food quality. It can protect food from contamination during circulation, improve quality, and avoid safety accidents.
Machine vision has made great achievements in many aspects such as pharmaceutical packaging, quality inspection, and control, helping the pharmaceutical industry to accelerate the process of modernization and intelligence. At present, in the detection links such as counting pills, coding, lack of pills in the blister plate, drug incompleteness and fragments, installation instructions, code recognition, etc., the inspection content of machine vision is rich, stable, and accurate, which meets the needs of the packaging line of the pharmaceutical industry that often changes packaging products. need.
Image and Video Editing
At present, there are many applications and machine learning algorithms on the market to process images, which can realize automatic repair, beautification, transformation effects, and other operations on pictures. And more and more favored by users.
Automobile manufacturing industry
Originally, the quality of automobile manufacturing mainly relied on three-coordinate measurement, which was low in efficiency, long in time, seriously insufficient in data volume, and could only be measured offline. Machine vision introduces non-contact measurement technology, and gradually develops into online measurement systems such as fixed online measurement stations and robot flexible online measurement stations, which can strictly monitor body size fluctuations and provide data support.
The machine vision inspection system can perform manufacturing process inspection, automatic tracking, traceability, and control of products, including obtaining body part codes through optical character recognition (OCR) technology to ensure the traceability of parts throughout the manufacturing process. Presence or absence to ensure the integrity of component assembly, and visual technology to identify product surface defects or processing tool defects to ensure product quality.
Consumer Electronics Industry
In the field of consumer electronics, machine vision is mainly used in PCB/FPC AOI inspection, parts, and machine appearance inspection, assembly guidance, and other applications, and more and more new application scenarios are emerging.
After the circuit board is removed from the printing device, or cleaned in the cleaning agent, and returned to the production line after repair, the online vision technology provided by machine vision can directly find the existing defects after the printing operation, ensuring that the operator can In addition, PCB was able to deal with related issues in a timely manner. In addition, when a defect is found, it can effectively prevent the defective circuit board from being delivered to the back end of the production line, thereby avoiding rework or scrapping. The operator can get feedback in time to clarify whether the printing process in operation is operating well and achieving the purpose of preventing defects, which is very important for improving production efficiency and yield.
With the popularity of automobiles, automobiles have become a very large application direction of artificial intelligence technology, but for now, there is still a long way to go before the technology is mature enough to fully realize autonomous driving/unmanned driving. However, using artificial intelligence technology, there are more and more functions and applications for driving assistance in cars. Most of these applications are realized based on computer vision and image processing technology.
Machine Vision Industry Chain
The industrial chain of the machine vision industry is mainly composed of upstream components, midstream equipment, and downstream application markets. Upstream components usually include light sources, industrial lenses, industrial cameras, image acquisition cards, software, and algorithm platforms, among which key software and hardware such as industrial lenses, cameras, acquisition cards, and software algorithm platforms are key value components of machine vision.
The equipment in the middle reaches of the industry assists enterprises in guiding, identifying, testing, measuring, and other intelligent manufacturing-related applications for products. These devices can then be widely used in downstream markets such as electronics and semiconductor manufacturing, food and beverage, automotive, and pharmaceuticals.
More than half of the downstream demand market for machine vision in my country is composed of electronics and electrical appliances, accounting for 52.90%, followed by semiconductors, accounting for 10.30%. In addition, the more widely used downstream markets include automobiles, printing and packaging, and food processing, accounting for 8.80%, 5.50%, and 4.90% respectively.
The system cost of machine vision consists of the costs incurred in parts manufacturing, software development, assembly integration, and maintenance processes, among which parts are the main components, accounting for nearly half of all cost values. Parts production and software development are the core business scope of upstream enterprises, and the two together account for as much as 80%.