Development of sensors with CCD technology (Charge-Coupled Device) for data storage that can capture two-dimensional images. In 2009, Willard Boyle and George E. Smith received the Nobel Prize in Physics for this invention.
Machines with eyes wide open
Seeing machines are revolutionizing our lives because they can take on more and more tasks. The technology that teaches machines to see is called machine vision or image processing. For many years, the technology has been developing dynamically and opening up completely new perspectives.
The human being – often copied, never reached
The human body is a miracle. Many of its functions and possibilities are so mature that even with the most modern technology it is hardly possible to reproduce the performance of individual body parts or senses. Examples such as artificial hearts or joints as well as robot hands do not come close to the human role models despite all engineering skills. This also applies to one of the most important senses of human beings: vision.
Easy as pie yet incredibly difficult
Just a few weeks after birth, a baby perceives different colours and shapes. At just a few months old, it recognizes the faces of its parents and can distinguish them from other people. Soon thereafter, it can pick up individual building blocks from a pile, even if they are partially covered. Apples and pears are quickly distinguished as naturally as dogs and cats. Decisive for these abilities is not only the eye, which perceives the environment, but also the brain, which processes, interprets, understands and triggers corresponding reactions.
What is simple for an infant can be a sophisticated task for a machine vision system. If a robot has to recognize and grab the correct part from a box with unsorted objects, this is a demanding task for the technology and shows how highly developed human vision and judgment are.
Human and machine vision
In machine vision, the tasks performed by the eye and human brain are taken over by technology. Machine vision systems consist of at least one camera or image sensor with suitable optics, illumination and a processor on which the image data is processed by software.
Both, human beings and technical image processing systems do not actually see the object under observation, but only the reflections of light reflected from the object onto the eye or onto the camera. The iris of the human eye increases or decreases the diameter of the pupil to control the brightness of the incident light. In the camera, this function is taken over by the aperture.
In humans, light passes through the lens of the eye, which bundles the light and focuses it. In a technical system, this task is performed by one or more lenses in a camera. In humans, an image of the scene in question is created on the retina. Around 120 million rods are responsible for detecting brightness and around 6 million cones for detecting colors. An enormous performance, however, which industrial cameras achieve – and in some cases even exceed – in resolution as measured in pixels.
In humans, nerves transmit the image information from the eye to the brain, which processes and evaluates the data and, if necessary, triggers reactions from body parts or the storage of the recorded information. In machine vision systems, cables ensure data transmission between the camera, processing unit and the subsequent systems, which can then react on the basis of the calculated results.
From seeing to understanding
Despite many similarities between human and machine vision, there are great differences between the two worlds. The biggest difference is in understanding and interpreting image data. In the course of their lives, people learn the meaning of objects and situations that they perceive daily through their eyes. Human beings understand other people’s facial expressions, recognize animals or plants and much more. We capture and filter the constant flow of images mostly intuitively. We then react to every new situation on the basis of the experiences stored in our brain.
In contrast, a machine vision system only correctly identifies objects if they have been previously programmed or trained. Software algorithms exist for specific tasks, e.g. for the recognition of objects, patterns, errors, characters, colors, dots, edges and much more. Developers of machine vision systems need to know in advance what this system is supposed to accomplish and design it accordingly.
Development and History
Machine vision has developed into a key technology and has recorded a high increase in sales in recent years:
Milestones of machine vision
An overview of some of the most important development steps in machine vision technology:
The first CMOS sensors (Complementary Metal-Oxide Semiconductor) are developed. In contrast to CCD sensors, the charges generated by photons are converted into voltage in the pixel.
Interoperability is key: New international industry standards such as GenICam and GigE Vision greatly facilitate the integration of image processing components and make a significant contribution to industry growth. Further standards will follow.
The arm technology is finding its way into machine vision and enables cost and energy-saving applications which were previously PC-based. Embedded Vision expands machine vision’s market potential in the following years.
The first industrial machine vision systems are available.
The future begins in Germany: Vision Components launches the first smart camera. Image data is processed directly in the camera and only the measurement results are delivered as output.
Machine vision becomes key enabling technology in the context of Industry 4.0 – the next level of production.
According to VDMA, the German machine vision industry achieves a new sales record and surpasses the 2 billion mark € for the first time.
VISION – the leading trade fair for machine vision and international meeting place for the industry ever since – takes place for the first time.
The change from analog to digital camera technology in machine vision begins with the introduction of FireWire technology. In the years to come, the resolutions of digital cameras will increase and enable the implementation of higher quality standards in the customer industries.
While machine vision was previously limited to 2D applications, industrial sensors are now available that reliably deliver 3D data in one single image.
Machine vision goes deep learning. At VISION, the industry’s leading trade fair, standard software products demonstrate how this new technology can be used profitably for machine vision applications.
Which kind of “eye” is needed for a machine depends on its tasks: Microscopy, mechanical engineering, medicine, environmental protection, astronomy or football stadiums – the application possibilities are almost unlimited. But all systems work according to the same pattern: The captured images are analyzed, identified and evaluated with suitable software.
Vacuum cleaner with a sharp eye
Small and integrated everywhere – embedded vision is machine vision, which is directly embedded in all kinds of devices (mobile phones, cars, vacuum cleaners etc.). The devices can now “see” and are therefore smarter, better and safer.
Development and HistoryREAD MORE
10 reasons to use machine vision
Machine vision and seeing machines have developed at an above-average rate in recent years and have become indispensable in the modern industrial production field and in many parts of daily life.
1. High savings potential
Machine vision systems reduce costs. They often pay off after only a few months.
2. Maximum product quality thanks to 100 % quality checks
Producing quality non-stop, 24 hours a day, 7 days a week – expensive product recalls, product liability claims and image damagecan be avoided thanks to machine vision.
3. Safe production, reliable products
Machine vision guarantees safety – in the production process as well as in the end product.
Improved environmental protection through optimized use of energy and resources , , more efficient recycling and smooth material flow – machine vision makes it possible.
5. Stable and optimized processes
Recognizing trends and irregularities in production processes early on – machine vision paves the way for realizing the smart factory of the future.
6. Flexibility in production
Modern machine vision systems are flexible. Even batch size 1 becomes feasible.
Specific programming skills were perhaps previously required. Simple operation, easy setup and seamless integration into the production process have been commonplace for a long time now.
8. A technology serving people
With and for people, constantly striving for safety, quality and efficiency – in the factories and outside. The optimization of traffic flows, the perfect swing for golf, the training of doctors, the inspection of moles, waste separation and recycling and many other applications we all benefit from machine vision!
9. Higher productivity and competitiveness
Modern production is automated. Only with machine vision can companies sustainably secure their competitiveness, prevent the migration of key technologies, create qualified jobs and capture new markets.
10. Ergonomic workplaces
Monotonous and dull tasks are performed by a seeing machine – machine vision systems support workers, ensure a perfect human-machine interaction resulting in a more advanced and safe workplace.
The future becomes visible
Machine vision systems have benefited for years from the permanent performance improvements in sensor and processor technology, from improvements in the transmission of large data volumes and from innovations in algorithms. Due to the ongoing progress in these areas, the application possibilities for seeing machines are constantly expanding.
In addition to use in production, seeing machines will also enable innovative solutions in many other areas in the future. There is no shortage of ideas for future applications.
10 reasons to use machine visionREAD MORE
Enabling cars to see
Traditionally, the automotive industry has always been one of the pioneers in the use of innovative technologies. This industry has recognized the possibilities of image processing and uses them accordingly in their products, where it is playing an ever-increasing role: driver assistance systems are already ensuring increased safety.
In the future, cars and other mobile machines will become increasingly linked to networks and autonomous. This is only possible with image processing!
Dr. Peter SchlichtVolkswagen Group Research – Automated Driving
In times of increasing automation in production, design and quality assurance, machines, algorithms and entire factories become intelligent. In addition, with regard to highly automatic driving, intelligent interior monitoring and advanced driving assistance and comfort functions, our cars are becoming “seeing machines”. High demands on sensor accuracy and the efficiency and safety of computer-aided and intelligent image processing play a central role in Volkswagen’s efforts to continue producing first-class mobility products for its customers.
Look at that!
Fascinating numbers from the world of vision and image processing:
pixels is the resolution of today’s standard industrial cameras. The maximum possible resolutions are even higher.
Source: VDMA Machine Vision group
of all human sensory perceptions are optical stimuli.
per minute is the human eyelid closed. In the course of his life, an 80-year-old person blinks her eyelids more than 420 million times.
increase in ten years – turnover of the German machine vision industry doubled within the years 2008 to 2017.
Source: VDMA Machine Vision Brochure 2017/18
per second and more are recorded by special high-speed cameras, e.g. to make extremely fast processes visible.
Source: Mikrotron GmbH
per second is currently the usual frame rate for cinema films. The human eye generally no longer perceives delays at this speed.
rods perceive the brightness in the human eye. Around 6 million cones recognize colors.
Dr. Olaf MunkeltChairman of the Board of the VDMA Machine Vision division
Machine vision as a driver of Industry 4.0 has become an indispensable part of the modern factory and has already conquered our everyday lives. Whether traffic management systems, autonomous driving, waste separation and recycling or health care – machine vision makes it possible.
Director, VDMA Robotics + Automation
Anne Wendel is in charge of the VDMA Machine Vision group with around 115 member companies. Her work focuses on statistical analyses, standardization, marketing activities, public relations, trade fair policy as well as networking events and conferences.
PS Marcom Services
Peter Stiefenhöfer is the owner of the press office PS Marcom Services, which supports companies and institutions in the field of machine vision in their press work. Based on his engineering studies (production engineering) he has about 25 years of professional experience in machine vision and during this time worked as an editor for a trade journal and as a press and marketing manager in a leading European company in this field.