Machine Vision Penetrates Multiple Fields, Enhancing the Development of Smart Factories

Machine Vision Penetrates Multiple Fields, Enhancing the Development of Smart Factories

Machine vision technology has not only found success in a variety of fields but is also expanding its range of applications. It has moved from the initial use in the electronics manufacturing and semiconductor production industries to areas like packaging, automotive, transportation, and printing.

Application in Daily Life

Machine vision technology has permeated daily life. It can be found in facial recognition features in beauty apps, facial album sorting, facial recognition for identity verification in payment apps, facial recognition in storage lockers, and industrial robots for object recognition and logistics robots for obstacle avoidance.

Global Market Growth

According to a report from a commercial news site in Dublin on June 22, the global machine vision market is rapidly growing. By the end of 2025, the market value is expected to exceed $19.2 billion. The technology has spread from its initial use in electronic manufacturing and semiconductor industries to other sectors such as packaging, automotive, transportation, and printing.

Features of Machine Vision Systems

Machine vision systems are known for increasing production flexibility and automation. They are used in hazardous work environments or where manual visual inspection is insufficient. In large-scale industrial production, machine vision inspection can greatly improve efficiency and automation.

With the gradual reduction in the cost of machine vision, the emergence of more solutions, stronger hardware, and smarter algorithms, coupled with the Internet of Things (IoT) connecting all devices, machine vision is set to become an excellent data collection tool.

Image processing equipment captures, collects, and exchanges data, which can be transmitted through the production process to trigger more intelligent actions, like detecting equipment wear, maintaining factory facilities, and alerting managers to order replacement parts in advance. In the future, machine vision components will be smaller, while speed, precision, and resolution will significantly improve, leading to a rapid increase in adoption rates.

Machine Vision in Smart Factories

While network systems are the foundation for future smart factories, their success relies on data collection, processing, and analysis, where machine vision plays a crucial role. It quickly captures large amounts of information and processes it automatically. In automated production processes, machine vision systems are used extensively for monitoring working conditions, inspecting finished products, and quality control.

IHS predicts that by 2019, industrial mobile applications will grow fourfold, reaching $200 million. These applications include using mobile devices for monitoring, managing workflows, maintaining equipment, and integrating systems. Managers can monitor machine vision system images from smartphones and quickly respond to app notifications.

Future of Smart Factories

Smart factories will be the trend of the future, where machines use self-optimization, self-setup, and artificial intelligence to complete complex tasks. This approach is cost-effective and improves the quality of products and services.

Machine vision technology, once adopted, will become the core of smart factories. Its reliance on communication networks and smart information organization networks will make it the "eyes" of the factory, allowing robots to flexibly control production processes and reduce human error.

The machine vision industry will gradually integrate with embedded computer vision (ECV). With the availability of low-cost embedded image processors in industrial environments, new integrated automation solutions could emerge, such as robotic arms with multiple embedded imaging devices.

Currently, the main machine vision interfaces include CameraLink, CameraLinkHS, CoaXPress, GigEVision, and USB3Vision. GenICam provides a general program interface for all cameras, independent of hardware interface technology.

However, as machine vision software research deepens, higher technical requirements are arising. The existing machine vision systems on the market are facing challenges due to development difficulties, usage complexities, and sales issues. This makes it harder to meet industry demands.

Global users still lack a matching understanding of the rapid development of visual technology, presenting a challenge for industry growth. Additionally, the integrated circuits upon which the system relies are quite complex, posing a hurdle for manufacturers, limiting smoother market growth.

On a positive note, the growing manufacturing industry in the Asia-Pacific region is focusing more on machine vision-related research activities, with increased spending expected to stimulate growth in the Asia-Pacific market, which could drive changes in the global market. Whether machine vision applications in robotics will become mainstream or fade into the background is something we should watch with anticipation.