Among the various kinds of information that people acquire from nature, visual acquisition is the highest, accounting for about 80% of the total information. With the development of information technology, human visual function is gradually given to computers, robots or other intelligent machines. Machine vision, which is currently in the industry's windfall, is one such technology, which realizes automatic detection and analysis applications through image processing, including automatic detection, process control and robot navigation. Currently, machine vision (MV) technology has been productized. Vision sensors, lenses, high-speed cameras, light sources, vision software, image acquisition cards, vision processors, etc. are becoming more and more sophisticated. In industrial automation environments, machine vision is getting more and more attention from the industry and is being used in a large number of applications such as self-driving cars, food production, packaging and logistics, robotics and drones.
When it comes to machine vision, technicians may understand a lot, this article tries to explain the truth that should be known about machine vision from four aspects one by one.
Truth 1: Machine Vision ≠ Computer Vision
Machine vision is a device that automatically receives and processes images of real objects by means of optical devices and non-contact sensors in order to obtain required information or control the movement of a robot. In operation since the 1950s, the technology really took off and grew in popularity from 1980 to 1990. Over the decades, machine vision has accumulated various definitions for what it is and how it works.
The Automated Imaging Association (AIA) offers a broader definition, which is that machine vision encompasses all industrial and non-industrial applications in which a combination of hardware and software provides operational guidance for a device to perform functions based on image capture and processing. SearchEnterpriseAI, on the other hand, offers a narrower definition of machine vision, calling it "the vision capability of a computer" that uses one or more cameras, analog-to-digital converters (ADCs), and digital signal processing (DSPs) to transmit the resulting data to a computer or robot controller.
In practice, machine vision often needs to work in conjunction with other advanced technologies, including natural language processing, robotic process automation (RPA), artificial intelligence (AI), and machine learning (ML), to realize the "vision" capabilities required for automation. You can think of machine vision as the eyes of automation, AI and ML as the brain, and RPA as providing the "keyboard player" needed to get the job done. The adoption of automation has accelerated in recent years, and it's critical for organizations to remain competitive in their industries. If you think of automation as a "digital workforce" at work, all of these "digital employees" would be blind without the addition of machine vision.
Computer vision has also been a big hit in the industry in recent years, so how does it relate to machine vision? On a macro level, machine vision is a systems engineering discipline that integrates and applies existing technologies in new ways to solve real-world problems. Computer vision, on the other hand, is a form of computer science that is not realized through tangible hardware such as vision devices such as cameras fixed to robots.
More specifically, machine vision is the body of a system, while computer vision is the intelligence of the system, the brain that processes the information. Without computer vision, machine vision will not work. Machine learning, deep learning, and neural networks are three techniques used to process items at a faster rate through a machine vision system. These three techniques can be used to expand machine vision's understanding of what is to be localized, making it a valuable asset to machine vision. As computer vision technology advances, the possibilities for potential machine vision applications increase accordingly.
It is worth noting that machine vision and image processing are likewise two different concepts; image processing is a process that outputs an image, whereas machine vision systems can detect and classify a wide variety of objects and items in a wide range of industries, including automotive, electronics and semiconductors, food and beverages, road and vehicular traffic or Intelligent Transportation Systems (ITS), medical imaging, packaging, labeling and printing, pharmaceuticals, and television broadcasting. Machine vision-based technologies are becoming central to the creation of automation.
Truth 2: Hardware and software developments have led to advances in machine vision
Machine vision is the eye of industrial automation. Its main workflow is: the system converts the captured targets into image signals by machine vision products (e.g. camera, CMOS or CCD), and then transmits the image signals to a dedicated image processing system. Based on information such as pixel distribution, brightness and color, the image signals are then converted into digital signals that ultimately enable machines (robots or other industrial tools) to perform industrial tasks such as manufacturing and quality verification.
Machine vision is a key element of Industry 4.0 and is helping industrial automation systems in a variety of ways, such as increasing efficiency by improving inventory, detecting faulty products, and improving manufacturing quality. To accurately mimic human perception, machine vision requires the help of a range of devices and software. The continuous development of these hardware and software technologies is further driving the evolution of machine vision technology.
01 Smart Camera
A camera is the main device used to inspect an object or item in a machine vision system. Sometimes, multiple cameras may need to be installed at a particular inspection point to ensure that every detail can be properly inspected. When a machine vision system needs to capture and extract application-specific information from an image, this is where the support of a smart camera is required. Smart cameras usually contain all the necessary communication interfaces and can be connected to Wi-Fi or a server in order to transmit the captured image data. As a powerful tool, deep learning enables system designers to quickly automate complex and subjective decisions while effectively improving product quality and capacity.
02 3D Camera
A 3D camera can show the depth of the detected object in an image to show different angles of the image. By using a 3D camera in a machine vision system, a different perspective and depth perception will result. Time-of-flight (ToF) cameras are 3D cameras that use the time-of-flight principle to measure distance.ToF imaging technology allows it to perform 3D imaging without scanning the object.The technology typically covers distances from a few meters to about 40 meters at a maximum of 100 images per second, with a distance resolution of about 5 to 10 millimeters, and a lateral resolution of about 200 x 200.
Historically, ToF has often been viewed as a less accurate 3D sensing technology due to some questions about its accuracy. Of course, in recent years many headline companies have developed high-resolution products of up to 1.3 megapixels, and high-precision ToF cameras for machine vision systems can significantly improve production flexibility and automation.
Texas Instruments' OPT8241 time-of-flight (ToF) sensor combines ToF sensing with an analog-to-digital converter and programmable timing generator (TG), which delivers 320 x 240 resolution images at frame rates up to 150 fps. The built-in TG controls reset, modulation and readout of the digitized sequence. At the same time, the TG is programmable, providing the flexibility to optimize various depth perception performance metrics such as power, motion robustness, signal-to-noise ratio, and environmental cancellation.
03 Vision Sensors
Vision sensors are at the heart of a machine vision system and are the source of maximizing the characteristics of the environment, with the core devices being image sensors such as CCDs and CMOS. These higher resolution vision sensors are typically able to produce images that contain more pixels, very helpful in improving image quality and making it easier to recognize visual details.
CCD sensors have long been the dominant technology for capturing high-quality, low-noise images. However, CCD sensors are expensive to manufacture and therefore generally more expensive and consume much more power than CMOS sensors. Today, CMOS sensor technology has advanced to the point where it can quickly approach the quality and functionality of CCD technology, at a lower price, in a smaller size, and with lower power consumption.CMOS cameras typically have a higher frame rate than CCD cameras, a critical feature for machine vision systems that rely on real-time image processing for automation or image data analysis. In addition, CMOS sensors are more sensitive to infrared wavelengths than CCD sensors, and CMOS chip and camera makers take advantage of this to capture infrared light, providing additional imaging capability for image recognition. On balance, CMOS sensors may be more suitable for machine vision applications.
04 Light Source
As an auxiliary imaging device, the light source often plays a crucial role in imaging quality. LED lighting products, for example, offer greater flexibility with adjustable angles and additional wavelengths for a more consistent spectral response. With a wide range of wavelengths and shapes of light sources available on the market, product selection is not difficult.
05 Image Capture Card
An image acquisition card usually comes in the form of a computer plug-in card whose main job is to transfer the image output to the host computer. Image acquisition cards are required to convert analog or digital signals from the camera into a stream of image data in a specific format, and can also control some of the camera's parameters, such as trigger signals, exposure/integration times, shutter speeds, and so on. Image acquisition cards usually have different hardware structures for different types of cameras, as well as different bus forms, such as PCI, PCI64, Compact PCI, PC104, ISA, and so on.
06 Vision Processing Software
Machine vision software is used to complete the processing of input image data, and then through certain calculations can get the required results. General-purpose machine vision software comes in the form of C/C++ image libraries, ActiveX controls, and graphic-based programming environments, etc. It can be specialized, e.g., only for LCD inspection, BGA inspection, template alignment, etc., or general-purpose, including localization, measurement, barcode/character recognition, speckle detection, etc.
Truth 3: The machine vision market is growing rapidly, with the automotive industry taking credit for it
The value of machine vision in automation lies in its ability to quickly and efficiently capture and process large numbers of documents, images and videos, in quantities and at speeds that far exceed human capabilities.
Wide application prospects and huge market potential determines that machine vision is bound to be a growing market, Markets and Markets data show that the market size of machine vision is expected to grow from $10.7 billion in 2020 to $14.7 billion in 2025, at a compound annual growth rate of 6.5%.
According to grand view research, the global machine vision market size was 13.23 billion in 2021 and is expected to grow at a compound annual growth rate (CAGR) of 7.7% from 2022 to 2030. The demand for vision-guided robotic systems in the automotive, food & beverage, pharmaceutical & chemical, and packaging sectors is the major driver for market growth. Among these, the automotive industry remains the largest adopter of machine vision systems globally, with a revenue share of over 15.0% from the automotive industry in 2021, and is expected to continue to grow steadily in the coming years.
U.S. Machine Vision Market Trends by Industry, 2020 - 2030
Truth 4: Machine Vision Will Make a Big Difference in Robotics Applications
There are many opportunities for machine vision to expand in terms of market scope and applications. These opportunities require some imagination, which means that machine vision is not just about replacing a technician's eyes, but about leveraging robots to accomplish tasks that technicians cannot. Machine vision gives robots the ability to "see" in real time and in high detail, allowing them to make decisions based on a comprehensive view of an object or environment. Today, robots are being used more and more in the world. When robots are equipped with machine vision, it gives them greater accuracy, orientation and understanding, the ability to grasp objects more accurately, place objects with greater precision, and perform more complex tasks faster.
Machine vision is becoming increasingly important in robotics applications, and according to a recent report by the Association for the Advancement of Automation (A3), the robotics and machine vision market has achieved substantial growth in the second quarter of 2021 as compared to 2020. Industrial robots are already widely used, and with the advent of collaborative robots and the rapid development of 3D machine vision, they will be used more often in combination.
Machine vision embodies a technological capability, as do other capabilities such as automation, machine learning, deep learning, and neural networks. It is a capability that can be integrated into other technologies and processes to benefit the industry and improve business efficiency. Robots today have increasingly built-in machine vision, which enables them to perform more complex tasks. These tasks would not be possible without machine vision telling the robot exactly where an item is located. Machine vision is the key to unlocking the full potential of automation, adding more intelligence to smart automation.