Machine Vision

Machine vision, also known as machine image processing, has been around for over 70 years. Research at the time aimed to reconstruct three-dimensional images from two-dimensional images. Today, machine vision is a precise technology and includes methods for modulating visual information from the environment that can be processed digitally. Similar to an eye, but with significantly higher speed and precision. Machine vision technology is based on digital image processing and analytical algorithms that enable decision-making based on the captured image material.

Machine vision is usually based on one or more cameras, in addition to special software and hardware that process the captured images. These systems are capable of performing a variety of complex tasks, including the inspection of products on production lines, the recognition and sorting of objects, the guidance of autonomous robots, the analysis of medical images, and numerous other applications.

Machine vision is particularly applicable where rapid and precise interpretation of an extremely large volume of visual data is required, beyond the human eye. It is also used in environments that are too dangerous for humans. It is a crucial factor in many areas of automation and plays an essential role in the ongoing development of Industry 4.0, autonomous vehicles, and many other technological advancements.

Deep learning is a branch of machine learning that is based on artificial neural networks. These networks are inspired by the structure and function of the human brain, and consist of several layers of artificial neurons. Each layer processes and transforms the data in a specific way so that complex patterns and correlations can be recognized. Machine vision and deep learning merge to form machine learning and enable the digitalization of companies and processes.

Machine vision is a technology that specializes in capturing and processing visual information from the environment. Machine vision cameras or intelligent cameras use special optics to capture movements, shapes, quantities, or colors – for example, of objects on a conveyor belt. This visual data is crucial for monitoring and controlling production processes.

The effectiveness of image processing cameras depends heavily on the quality of the lighting. A well-lit environment is essential for the camera lens in order to capture clear images. Once captured, the images are converted into electrical signals using frame grabbers or 3D sensors. These signals are then processed in IT systems, where the image information is further processed to make it usable for machine processing and analysis.

The foundations of machine vision were developed in the 1950s, when scientists began to explore the possibilities of computers understanding visual data. One of the first milestones was in the 1960s when Larry Roberts, one of the pioneers of machine vision, wrote his doctoral thesis at MIT on 3D reconstructions from 2D images. This work is often regarded as one of the starting points for the development of algorithms and techniques used in machine vision.

Further progress was made in the 1970s, particularly with the introduction of simpler machine vision systems for industrial applications such as inspection and quality control on production lines. The 1980s saw a significant increase in commercial applications aided by advances in digital image processing and computer hardware.

As computer technology continued to improve, high-resolution cameras became available and computing power increased in the 1990s and 2000s. Machine vision thus became more accessible and powerful. Today, it is a key technology in many industries and is being driven forward by developments in artificial intelligence and machine learning.

The most commonly used systems include 2D image processing systems, which are primarily used in pattern recognition. They are ideal for environments in which objects and scenes need to be captured on a flat, two-dimensional plane.

3D image processing systems are used for tasks in which spatial accuracy plays a role. These capture information in multiple dimensions and are ideal for precise measurement and inspection purposes. Intelligent camera-based systems use built-in cameras and specialized software to handle a variety of inspection tasks independently, while compact vision systems are valued for their ability to integrate seamlessly into existing production environments.

PC-based systems harness the power of computer processing to enable more complex image analysis and sophisticated inspections. Multispectral imaging, which captures images in different wavelengths, offers enhanced analysis capability over traditional 2D imaging. Hyperspectral imaging goes one step further and captures a wider range of wavelengths, enabling even more detailed analysis.

In addition, variable magnification lenses with customizable magnification levels offer additional flexibility when performing various inspection tasks. Together with the aforementioned technologies, they open up extensive possibilities for industrial image processing, from simple identification to complex quality control. They are crucial for optimizing production processes.

In terms of offerings, the machine vision market is divided into three segments: hardware, software, and services. According to a report by the India & U.S. based market research and consulting company “Grand View Research”, in 2023, the hardware segment held 62 percent of the global market share. Within the product segment of the global market for machine vision, the sub-segment “PC-based systems” had a market share of over 54 percent.

Among the applications for machine vision, the quality assurance and inspection market segment had the largest share of the global market in 2023 at over 51 percent. In terms of end-user industry verticals, the automotive industry had the largest global market share for machine vision solutions at 19 percent in 2023.

• In the digitalization of industry, machine vision is used for the quality control of products. Cameras and image processing systems check components for defects or deviations from specified standards. The topic of occupational safety for workers also falls within this area.
• In the digitalization of logistics, robots use machine vision to understand their environment and carry out logistics tasks. Machine vision tracking and computer vision tracking are growing areas of application.
• In the digitalization of traffic, machine vision helps with the monitoring and control of traffic flow, as well as the recognition of traffic signs and the monitoring of vehicles.
• In the field of public safety, machine vision is used for facial recognition, detecting suspicious activity, and for monitoring public and private areas. Computer vision in the smart city is a slow-growing market.
• In the digitalization of healthcare, machine vision is used to analyze images from diagnostic imaging procedures such as X-rays, MRI scans, and other medical images. Machine vision and deep learning can also develop models that can learn from large volumes of data and make predictions or decisions.
• The digitalization of agriculture can be further developed with machine vision. For example, it can be used to monitor plant growth, pest infestation, and the ripeness of fruit in order to identify the right time to harvest and optimize treatment methods.

Industrial image processing systems are used in many industries to solve complex tasks.

In electronics production, these systems are used for the inspection and quality control of components in the production line. For example, they check the quality of assembled components. This can range from checking the dimensions of an object, to detecting surface defects and irregularities. In document management, optical character recognition (OCR) is used to extract text from images, both in printed and handwritten form, including the recognition of signatures.

In the automotive industry, image processing systems make an important contribution to the safety of self-driving cars by recognizing objects on the road. They also support quality control in production by checking the correct labeling of product packaging, for example. In automated production lines, robots use machine vision to recognize, sort, and to precisely handle parts. This improves the efficiency and accuracy of processes such as assembly, packaging, and palletizing.

In the digitalization of healthcare, image processing systems are used to support diagnosis by analysing detailed scans and images of patients, e.g. in radiology or pathology. These systems are also indispensable in materials testing to detect faults, defects, and impurities in various materials, which is particularly important in the pharmaceutical industry when checking tablets for manufacturing defects.

In the food industry and the digitalization of agriculture, image processing systems help to sort products according to size, color, or quality. This ensures that only standard-compliant products are sold. They also play an important role as an authentication solution for currencies by detecting counterfeit banknotes.

Machine vision and wireless IoT technologies have a lot in common and can often be used in synergy, in different applications. Here are some of the key connections and synergies between these two technologies:

• Machine vision and IoT generate large volumes of data that need to be analyzed and interpreted to gain useful information and insights. IoT devices collect data from their environment via sensors, while machine vision specifically collects visual data from images or videos. Both technologies use advanced algorithms and machine learning to process this data and make decisions.
• In autonomous systems, such as robotics or autonomous vehicles, machine vision is used to understand and navigate the environment. These systems are often connected to the Internet of Things (IoT), which enables wireless communication and interaction with other devices and systems. The combination of both technologies enables more efficient and intelligent autonomous navigation and decision-making.
• IoT devices often use edge computing to perform data processing tasks directly at the edge of the network, reducing latency and improving efficiency. Machine vision can also benefit from edge computing, especially in real-time applications such as industrial automation where fast decisions are required. By processing image data directly on the device, response time can be improved and network load reduced.
• In industrial automation and condition monitoring, IoT and machine vision can work together to create advanced monitoring and control systems. For example, an IoT-connected camera can be used to monitor production lines, while IoT sensors provide data on machine conditions, temperature monitoring, or other relevant parameters. The integration of this data enables the comprehensive monitoring and optimization of processes.
• Both machine vision and IoT play a key role in the development of smart cities and smart homes by providing intelligent monitoring and management solutions. Cameras equipped with machine vision can be used for traffic monitoring or security, for example, while IoT devices can be used to control lighting, energy management, and other home technologies.

Machine vision systems can detect objects and entire environments with the utmost precision. Like a hawk or an eagle, they can detect the smallest deviations from a great distance. At the same time, in some applications, they have an impressive field of vision of almost 360 degrees, comparable to that of a dragonfly. They see and measure far beyond human capabilities. They also never get tired or distracted and are ready for action 24/7.

They are also able to analyze images at lightning speed and make informed decisions. Machine vision can inspect thousands of parts per minute. These systems automate counting processes, quality checks, and monitoring tasks. Precise quality inspection can reduce downtime. The machines work faster because they require less maintenance, so companies can meet their production deadlines consistently and easily.

In addition, machine vision systems are cost-effective, configurable, and adaptable, making them suitable for a wide range of applications and changing conditions. They can be quickly reprogrammed or adapted to different product or process requirements.

The use of artificial intelligence is of crucial importance in autonomous driving, as AI in companies enables real-time decisions and predictive driving, in conjunction with image processing systems. Other visionary fields of application include the recognition of traffic signs or pedestrians in video recordings of autonomous vehicles. This capability is of great importance not only for autonomous vehicles, but also for surveillance cameras and industrial inspection systems.

A particularly impressive example of the power of this combination can be found in healthcare. AI-supported image processing systems make it possible to control surgical robots that, in some cases, exceed the capabilities of human surgeons. AI algorithms can be used in medical imaging to automatically identify anomalies in X-ray images or MRI scans, for example. AI systems learn to identify relevant features in the data and continuously improve their accuracy.

The combination of machine vision and AI leads to a significant increase in process automation. In production lines, for example, these technologies can be used to carry out quality checks. Automated defect detection and sorting takes place without human intervention. In addition, AI enables image processing systems to adapt to new or changing conditions. To do this, the algorithms are modified based on feedback or new data.

Machine vision already impresses with its speed, accuracy, and reliability. In combination with AI, this technology can increase its performance even further and enable completely new application scenarios in various economic sectors.

The combination of machine vision and OPC UA enables the seamless integration of visual data into the overall industrial automation system. Machine vision systems can capture the images and information necessary for quality control, precision measurements, or for robotic guidance. OPC UA standardizes the way this information is collected, transmitted, and used, and ensures interoperability between different devices and systems. This is particularly important in complex production environments where machines and systems from different manufacturers and technologies need to work together.

In addition, OPC UA enables secure and reliable communication in real time, which is critical for machine vision applications as decisions often need to be made in milliseconds. In automated manufacturing, for example, an anomaly detected by machine vision can be processed immediately and transmitted to the control system, which can then react accordingly to avoid production errors.

Robots and machine vision work together to create highly automated and efficient systems in various industries that significantly improve the functionality and efficiency of processes. Together they form the ‘Robot Vision’ team. By integrating machine vision, robots can obtain precise information about the position, orientation, and type of objects, enabling them to grasp, sort, or manipulate objects in a targeted manner. This capability is particularly useful in production environments, where robots can correctly position components or reject faulty parts, for example.

In addition, mobile robotics applications such as autonomous vehicles and drones use machine vision to interpret visual data for navigation. They continuously capture images of their surroundings, which are analyzed to identify obstacles, plan routes, and navigate safe paths of movement. In manufacturing, the combination of robots and machine vision enables precise assembly or welding work and, at the same time, continuous quality control by checking dimensions and inspecting surfaces for defects.

In interactive applications, such as in service robotics or in medical areas, robots use machine vision to recognize people and react to their gestures, which enables adaptive and sensitive interaction between humans and machines. Machine vision also improves the adaptability and learning ability of robots. Machine learning enables them to recognize new objects and refine their handling techniques in order to react more efficiently to changes in their environment.

This synergy of robotics and machine vision creates intelligent systems that are able to perform complex and variable tasks with high precision and minimal human supervision, leading to a significant increase in productivity and flexibility in automated processes.

OCR is a special application within machine vision that aims to recognize printed or handwritten text from a digital image and convert it into a format that can be processed by word processing programs. OCR systems are highly sophisticated and can recognize complex layouts, distinguish fonts, and even decipher damaged or distorted text. This technology is used in a variety of scenarios that include scanning documents, reading license plates, automatically capturing data from forms and invoices, as well as capturing data in systems to assist blind and visually impaired people.

The integration of OCR into machine vision systems significantly expands the functionality of these technologies In industrial production. OCR systems can be used to read product labels, verify serial numbers, or check expiration dates, for example.

In an automated warehouse, OCR and machine vision, or OCR and computer vision can work together to identify and sort packages by recognizing and processing the information on shipping labels. In traffic surveillance, OCR systems can help identify vehicles by reading license plates, which is useful for toll systems, road safety, and law enforcement.

The combination of these two technologies therefore offers multiple opportunities to optimize workflows, reduce manual input, and improve the accuracy of data capture systems in many industrial and commercial applications.