What is Machine Vision?

Simply watching a production process has long been a challenge for manufacturers. It’s impossible to have human eyes on production 24/7. But computer vision (CV) enables a computer to see and understand similar to the way humans do and continually “watch” operations. With machine vision systems that use CV and image-based analytics, manufacturing and practical applications, such as inspection, process control, safety, and robot guidance, are automated and transformed. Machine vision watching conveyors, robots, and other machines can log what they see and share that data with the people, processes, and other equipment and tools needed for safe, efficient operations.

The range of applications and value that machine vision provides to organizations is driving greater adoption. Grand View Research predicts that the market, valued at $12.29 billion in 2020, will grow at a CAGR of 6.9 percent through 2028.

New implementations also show that enterprises also see advantages in combining artificial intelligence (AI) with CV to create AI machine vision systems that not only classify what they see but that also become smarter over time and can even make decisions so that once manual workflows or interventions can become automated.

How AI Machine Vision Works

To make a machine vision system see, technology must be designed to progress through four basic steps:

  • Capture (Data Acquisition)

Machine vision systems collect data from sensors, cameras, or other devices and create a digital output of what they see from the physical world. Data can include granular details, such as the color, brightness, intensity, and light scatter, that help intelligent systems learn to understand what they see in all conditions.

  • Preprocessing

The next step is to transform data from images or video so that it can be processed. This step includes changing data to a digital format but can also involve changing features of the image, such as removing noise, changing the scale of the image, or extracting specific features such as lines, edges, points, or textures. 

  • Processing

Once data is properly formatted, the intelligent system identifies patterns, such as objects within an image or video, movement from frame to frame, or details of a scene. The system classifies these patterns and details and tracks them as processes repeat. Developers can build AI machine vision systems that count items, confirm the position and orientation of parts in an assembly, measure items, and more.

  • Act (Post Processing)

Finally, AI machine vision systems can use processed data to make decisions or take actions. For example, the system may determine that more processing is necessary. Or it could trigger a response, such as stopping an autonomous vehicle if an employee steps into its path or stopping a conveyor when a misassembled part could cause damage if it’s advanced to the next phase of production.

Typically, AI machine vision systems deliver the greatest value when they’re part of a holistic system rather than operating independently. The insights from analyzing AI machine vision data can benefit more than the conveyor system or the autonomous vehicle. Data from these systems can also inform decisions in production, inventory, purchasing and other areas of an organization — as long as there is a way to share data with them. Therefore, it’s most beneficial to use a vendor-agnostic solution that more easily bridges the information technology/operational technology (IT/OT) gap securely and efficiently.

Where Machine Vision Delivers Value

Although AI machine vision is far from reaching the saturation point in manufacturing, there are some commonly accepted use cases. One of the most prevalent is object detection. AI machine vision can locate objects in an image or video and organize or classify them. Enterprises can use a system designed for object detection to identify boxes and track where they are in a fulfillment center or enforce safety requirements by detecting whether people are wearing hard hats or other protective equipment in dangerous areas. AI systems can use the data that these systems collect to alert employees or managers on the floor that a package has been misplaced or deny them entry to a hazardous area if they aren’t wearing safety equipment.

Additional machine vision cases include:

  • Self-driving vehicles

Cameras that provide a 360-degree view around an autonomous vehicle capture the data it needs to stay within a lane, detect objects in its path, and understand traffic signs and signals. Then, AI uses the data to enable the vehicle to respond quickly to this data.

  • Robotics

Machine vision systems give robots eyes, enabling them to estimate distances, read barcodes or text via optical character recognition (OCR), and recognize objects.

  • Automated inspection of mass goods

Automating QA/QC processes with AI machine vision can increase accuracy – and ultimately, quality – with systems that work as accurately and consistently at the end of a shift as at the beginning, unlike most human inspectors. Additionally, automated inspection can inspect even the smallest details with speed and efficiency and free inspectors from repetitive tasks so they can focus on root cause analysis or other higher-value tasks.

  • Motion tracking of objects and people

AI machine vision can also track objects as they move through a process or people as they move through an area, alerting staff to potential bottlenecks or unsafe situations.

How Edge Computing Empowers Machine Vision

In most AI machine vision use cases, real-time data is essential. For example, if the system is designed to lock a door if a hazard is detected or to stop production if equipment damage is imminent, even the smallest amount of latency that sending data to the cloud would create is unacceptable.

Edge computing brings image or video processing close to the data source so that AI can use the data instantaneously. Keeping processing at the edge also solves bandwidth challenges that can occur when large volumes of data travel to the cloud. Additionally, it can overcome data privacy and compliance issues because you can limit the amount of data that travels outside your network.

Even though data processing can take place at the edge, you still want the benefits of sharing data from AI machine vision with your core system. ADLINK offers a full slate of solutions, including:

  • The ADLINK AI-enabled machine vision solutions include the all-in-one smart camera for easy and flexible deployment, and multiple-channel AI vision system to meet all types of application scenarios.
  • The ADLINK EVA (Edge Vision Analytics) software development kit provides added values to the AI-enabled vision by offering no-code/low code environment, ready-to-use reference codes and plugins, and compatibility of heterogeneous hardware, making it easier for users to develop the AI vision application.
  • The ADLINK machine vision offers full product spectrum, covering almost all popular image interfaces from Analog, HDMI to USB3 Vision. With established customer success, ADLINK machine vision products have connected more than 500, 000 units of cameras worldwide.
  • The ADLINK Edge platform is a simple to implement, vendor-agnostic solution that allows you to deploy machine vision as a part of an efficient, total IT ecosystem.
  • The ADLINK Data River runs across the entire network to allow data to flow when and where it’s needed through this scalable system.

Machine vision technology can provide your operation with advantages, including greater accuracy, quality control, efficiency, and the ability for human workers to focus on high-value tasks. To make your vision for a more competitive and profitable operation a reality via machine vision solutions, visit our Smart Manufacturing and AI Machine Vision Devices pages to learn more.

Kevin Hsu
Kevin Hsu

Manager, ADLINK IoT Solutions and Technology, Edge Vision Product Management Dept.

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