The ultimate goal of vision software is to count, measure, identify, or inspect the object and compare it to the criteria set by the object’s developer. Along with this, workers can use a simplified interface that allows them to check the progress and success rate of production.
Different machine vision systems focus on certain tasks. One of the most widespread tasks that they undertake is that of quality control.
Quality control can mean a lot of things, and in the context of machine vision, it most often refers to the overseeing of proper sorting of goods on a production line and inspection of goods and parts.
Machine vision systems that assess products based the quality of shape, size, material, or any other programmed factor include vision inspection systems, optical inspection systems, and laser inspection systems. All do so with consistency, speed, repetition, and magnification.
Overseeing Proper Sorting Machine vision inspection systems can help with the fast and accurate filling and packaging of prescription medications, other pharmaceutical goods, and pulp and paper.
Inspecting Products for Quality In terms of inspecting products for quality, two of the biggest fields that employ machine vision systems are the electronics industry and the automotive industry. Within these industries, manufacturers make especially great usage out of AGV (automated guided vehicle) equipment to inspect their products.
Inspecting Production Tools for Quality As well as inspecting finished products, these industries use machine vision systems to inspect the parts used to make machine vision products themselves. Such parts and components include die casts, molds, and tools. Machine vision solutions routinely inspect them under high magnification.
Human Safety and Security
Another focus of machine vision systems is human safety and security.
Recycling and Waste Management In recycling and waste management facilities, machine vision technology spares the workforce from having to sort contaminated or dangerous materials.
Airport Security At the airport, machine vision systems help scan and sort baggage.
Fraud Preventions At banks and point-of-purchase locations, machine vision systems can detect counterfeit bills.
In addition, machine vision technology helps with performance tasks like labeling, food processing, textile machining, and facial recognition.
James J. Gibson developed the first machine vision system, a 2D imaging system, in the 1950s. His goal was to develop technology that could recognize statistical patterns and help achieve optical flow. Optical flow, also known as optic flow, is a concept that Gibson developed in the 1940s. It is the pattern of perceived surface, edge, and object motion in a visual scene that is a result of relative motion between said scene and an observer.
Then, in 1960, a PhD candidate at MIT named Larry Roberts wrote his thesis on the extraction of 3D geometric info from 2D images. Based on his thesis, a whole generation of researchers began studying image processing. Collectively, they came up with 3D machine imaging.
The next push for machine visioning came about in the 1970s, when MIT began offering a machine vision class, held in their Artificial Intelligence Lab. Students in this class were able to study and develop useful visioning processes, such as edge detection. In 1978, a young professor there named David Marr created the first computer visioning program, with which machine operators could build 3D representations of their 2D drawings.
After Marr’s breakthrough, machine vision systems quickly made their way from the research rooms of MIT to actual factory floors. There, manufacturers first used them to read data like codes, letters, numbers, and symbols. After that, computer engineers invented the first smart cameras. In the 1990s, they updated smart cameras, and added digital signal processing, or DSP.
As engineers and researchers made improvements and furthered automation, machine vision systems became less expensive, easier to use, and more practical. Today, they are invaluable. According to experts, by the year 2022, the machine vision market will be worth around $15.46 billion (globally). Thanks to machine vision, images and processes in the industrial, residential, and commercial worlds alike continue to become more accessible and more accurate.
How It Works
The many steps of object and image processing include stitching/registration, filtering, thresholding, pixel counting, segmentation, edge detection, color analysis, blob discovery and manipulation, neural net processing and deep learning, pattern recognition, optical character recognition, barcode, filtering, and gauging/metrology.
Often, these processes end with a comparison against target values to determine a “pass or fail” “go/no go” result.
Stitching, or registration, is a step during which the vision system combines images (2D or 3D) that are next to each other, in order to stitch together a clearer picture.
Thresholding is a machine visioning process during which it separates image sections. It does so using a set gray value, as well as by changing the image sections from grayscale to black or white.
Pixel Counting involves counting pixels, which are tiny samples of an original image, and the smallest usable working points in an image built from points. During pixel counting, the program tallies the number of light or dark pixels.
Segmentation makes an image easier to study by separating it into digital segments.
Edge detection is used to create an outline or sketch of an image by finding and mapping out its edges.
Color analysis software isolates image features using color for image analysis. In doing so, it can identify objects, parts, and products, as well as define their quality and determine their features.
Blob discovery and manipulation (or extraction) is a machine vision task during which it checks an image for blobs of connected pixels. These pixels, which are different than regular separate pixels, are called image landmarks. During inspection, they appear as dissimilar regions, like gray holes in a black image.
Neural net processing and deep learning are two similar machine vision processes. Their goal is to learn about systems over time as operators feed them new data, so that they can eventually make multivariable decisions. They are supposed to work like developing brains, which develop new neural pathways as they gain new experiences.
Pattern recognition software is designed to recognize (match, add, etc.) image patterns, whether they are rotated, partially obscured, or differing in size.
Optical character recognition programming automatically recognizes and reads certain texts, such as serial numbers.
Barcode reading systems are designed specifically to scan and read barcodes. They recognize it by visually scanning it and comparing it to barcode values stored in their system. If the code matches, they allow the process to continue, and if it does not, they alert the user or system to try again.
Filtering, also known as morphological filtering, is a machine visioning process that processes digital images with the help of lattice theory. Note: A lattice is an abstract structure studied in abstract algebra. It is composed of a partially ordered set. Every two elements of this set have a one-of-a-kind supremum (upper bound) and a one-of-a-kind infimum (greatest lower bound).
Gauging/metrology processes measure object dimensions. They may do so using any number of measurement units, such as pixels, centimeters, or millimeters.
Optical inspection systems use machine vision to inspect objects and images like serial numbers on products at a store checkout line, and finished products on an assembly line.
Optical sorting systems work like optical inspection systems, except their goal is to sort products.
Laser inspection systems perform machine vision applications using a combination of laser beams and photoelectric sensors. Uniquely, after scanning an image or item, they can generate a 3D reproduction. Laser inspection systems can inspect images and items for a wide range of applications, like parts counting, product defect detection (down to the microscopic level), serial number scanning, and barcode scanning.
Smart cameras obtain extra high-quality images using a combination of processing circuitry and imaging software. They are portable, but because they don’t come with much storage space, users usually use them in conjunction with a main system.
CCD cameras convert photons into electrical or digital images using CCD chips, or charge-coupled device chips. Once the machine vision cameras have converted the photons into images, operators can upload said images onto a computer image file.
Magnetic imaging systems are designed using magnetic materials. They develop visual representations of products using their magnetism, along with an x-ray type sensor.
Robotic vision systems are machine vehicles, like AGVs, that traverse an industrial space. They are semi-autonomous and move using limited sight provided to them by computer vision.
Individual machine systems are often quite different from one another, as they serve diverse applications, but generally speaking, they all work using the same basic components. These components are vision sensors, lighting, an image capture device or digital camera, a computer processor, and special image processing software.
The most common way that vision sensors are used is in a scanning-based triangulation method wherein vision sensors activate the first step of vision sensing and the acquisition of an image, by detecting the presence and position of a product or component.
Lighting and Imaging Device
When the vision sensor recognizes that the product or component is in place, the imaging device or camera is triggered, along with the lighting, with which it is frequently synchronized. This synchronous action produces a digital image that illuminates features of interest.
The imaging device can be combined with the computer processor or it may be separate. If connected, the combined unit is called a smart sensor or smart camera. If not, a specialized intermediate hardware device, called a frame grabber, is used to collect and convert the imaging device’s output and enter it as data into the computer system.
After this, the machine vision image processing software steps in.
Machine vision systems are an excellent investment for a number of reasons. First, they can magnify their “eyes” time and time again, so that they can get a much better look at small images than their human counterparts ever could. For this reason, an inspection using machine vision is likely to be much more accurate than an inspection by a human. In addition, machine vision systems work much more efficiently than humans, whether they are interpreting blobs or sorting products.
Design and Customization
To create the best systems possible, system designers carefully consider application requirements and more. Application requirements and specifications will determine things like system camera quantity, data storage capacity, processor speed, and level of automation.
In addition, for optimal performance, engineers take care to make sure that the product speed and system inspection rate are compatible with one another. To avoid the possibility of distortion by establishing the proper program parameters, they will also often use calibration targets or take test samples.
They may also use a prototype to create a computerized model of an inspection system, complete with exact materials and surface features. Likewise, designers will decide whether you require a separate system or a machine vision integrator approach.
With this basic order of events and necessary components in mind, engineers can develop application-specific custom machine vision systems and products.
Safety and Compliance Standards
Some of the most widely used machine vision service standards are those put out by the European Machine Vision Association (EMVA). The EMVA puts out standards regarding transparent data presentation, and exact measurement procedures. In addition, they publish programming that is adaptable to a wide range of systems. With it, users can easily share data and make changes to their systems.
In partnership with EMVA are standards organizations from around the globe, including China (CMVU), Japan (JIIA), Germany (VDMA), and North America (AIA).
In addition to the EMVA, machine vision system operators can use GigE Vision, the global standard camera interface created by AIA. With it, operators can conduct inexpensive and low gigabit consuming communication over an ethernet.
Things to Consider
Because it is so lucrative, the machine vision service market is flooded with would-be suppliers. For the best and most reliable machine vision products and machine vision services, you need to partner with an experienced supplier that you can trust. Such a supplier must be able to produce for you solutions that help you carry out your applications, all the while staying within your budget.
To help you in your search for the perfect machine vision solution, we’ve put together a list of some of the best machine vision suppliers we know. You can find their info by scrolling towards the middle of this page. Check out their detailed services and solutions, and based on your application requirements, pick out three or four suppliers with which you’d like to speak. Then, reach out to each of them to go over your application. After you’ve spoken with each supplier, determine which one offers the best vision solutions for you, and go with them. Happy hunting!