Machine Vision is a great stepping stone in the pursuit of achieving complete industrial automation. In simple terms, it allows machine systems to “see” their surroundings as humans do. By allowing robots to perceive physical objects through vision, we enable them to perform autonomous tasks. Since the 1980s, machine vision has rapidly transformed modern manufacturing processes. Machines can now perform critical functions like product inspection, measurement, positioning, and sorting.
Definition of Machine Vision
Machine vision is a combination of imaging and automatic inspection technologies. It captures and processes image data in real-time. Machines can then analyze, verify, measure, and process workpieces. As a vision system, it contains several imaging components. Machine vision systems contain optical lenses, lighting systems, cameras, controllers, and software. With these components, a machine vision system typically monitors its surroundings continuously. Assembly line automation and robotic product inspection have transformed the industry.
How Machine Vision Works
A vision system has both hardware and software components. They come together to process visuals. Machine vision occurs by the following scheme:
Capturing the Image
The vision system may contain one or more digital cameras or sensors. They are mounted on the movable arm, conveyor line, or some other component. Cameras capture images of the work area from different angles.
Image Pre-processing
This stage involves noise removal, adjusting lighting, and correcting distortion. The raw image may contain any number of irregularities, making image processing difficult. It cleanses the image and enhances features of interest. Pre-processing makes image analysis more efficient.
Attribute Extraction
The pre-processed image is supplied to the machine vision software. It uses machine learning algorithms to extract significant features. Edges, corners, and angles are identified and categorized. All of this is made possible by edge detection and segmentation algorithms.
Identifying Patterns
Machine learning works on the principle of pattern recognition. The analyzed image is labeled based on millions of similar training set inputs.
Categorization and Confirmation
ML algorithms classify and evaluate objects on the basis of training data. If it passes all tests, it is approved for further processing. If the algorithm detects a fault, it is sent back for modification or discarded.
Key Components of a Machine Vision System
Machine vision systems comprise several key components. Each component fulfills a significant role.
Illumination Tools
Lighting is significant for vision systems because high-quality images require illumination. Manufacturers specify the wattage and lumens of light necessary for proper camera saturation.
Optical Lens
An optical lens obtains the image of the workpiece. It transfers the image to the camera sensor as light.
Camera Sensor/ Capture Board
Camera sensors receive the captured image in the form of light. They convert this image into a digital form through semiconductors or charge-couple devices. The physical image is translated into a digital form, represented by pixels.
Processor
The digital image is analyzed by deep learning software. The processor undertakes this step. The processor, or the computer, extracts the key features of the object from the image.
Communication
The components of the vision system interact with each other through this channel. The communication component generally includes i/o signals, serial connections, and wireless connections.
Types of Vision Systems
2D Vision Systems
When one hears of machine vision, they often assume that it is 3D vision. However, 2D vision systems are the most widely used tools for inspection and pattern recognition. The software of 2D vision systems analyzes images in two dimensions. One of the most popular applications of 2D vision is barcode and QR code scanning. 2D vision systems are cost-effective, dynamic, and straightforward.
3D Vision Systems
3D machine vision systems analyze depth through multiple sensors. They interpret captured information as a 3D point cloud. Recurrent techniques: structured light, stereo vision, laser triangulation, and time-of-flight. 3D vision has immense application in robotics, surface analysis, automation, and product inspection.
Smart Camera-Based Vision Systems
Intelligent camera-based vision systems can fulfill your fundamental measurement and inspection requirements. They are simple, affordable, and readily available. Intelligent camera setups generally involve an optical lens of fixed focal length and lighting. They are compact and offer seamless integration into existing measurement infrastructure.
Compact Vision Systems
As you might understand from the name, compact vision systems are a smaller version of PC-based systems. They contain a graphical processing unit that routes to a peripheral terminal for display. Compact vision systems are ideal for lightweight applications and scenarios. In these cases, portability is key.
PC-Based Vision Systems
The majority of machine vision systems use a computer as the central processor for all components. An industrial PC is responsible for communicating with peripherals. It processes images and manages automation. PC-based systems are reliable solutions for factories with high automation and output requirements.
Multispectral Imaging
Machine vision systems with multispectral imaging can capture a vast amount of information. They go beyond the typical RGB light patterns, capturing light outside of the visible spectrum. If you need to analyze near-infrared or ultraviolet, this is an excellent solution.
Hyperspectral Imaging
Hyperspectral imaging vision systems obtain wavelengths in hundreds of narrow, adjacent bands. It is an advanced version of multispectral imaging. Vision systems with this technology can identify material properties and characteristics.
Variable Magnification Lenses
Have you ever faced the hassle of requiring different zoom levels for your products? Variable magnification lenses solve this very problem. By providing modifiable magnification values, they can increase the range of products you analyze.
Machine Vision in AI
The interplay of machine vision and artificial intelligence has reshaped industrial operations. You are probably aware of the applications of deep learning algorithms in large language models. AI brings the power of neural networks to machine vision. It means that factories can do advanced processing on-site. With the development of edge computing, vision systems can make autonomous decisions on-site. Dead pixel detection is an application. Machine vision systems can also identify welding faults. Another processing-intensive application is fabric analysis.
Machine Vision in Robotics
Pick-and-place robots and automated performance arms are essential components of modern factories today. Assembly line automation would not be possible without machine vision. It showcases the impact of vision systems in robotics. Intelligent vision robots can perform tasks typically handled by human workers. It means less human effort, risk of injury, and inefficiency. Robots can sort shapes, identify defects, and manage inventory autonomously. They are driving factory productivity to new heights, all thanks to machine vision.
Applications of Machine Vision
Quality Control
Detecting manufacturing defects in electronics, automotive, and pharmaceutical products is a time-consuming process. Historically, companies have invested millions of dollars in worker wages for product inspection. With automated assembly lines and robotic workers, costs have been reduced. Productivity, on the other hand, has skyrocketed. Inspection systems are assisted with machine vision. They excel in pass/fail checks, measurement, and tracking. They enable assembly lines to function without breaks.
Measurement
2D and 3D vision systems accurately measure object dimensions, that too almost instantaneously. It has allowed factory operations to become more streamlined. In many cases, this means that workshops can remove entire measurement stations!
Automation
One of the appreciable advantages of machine vision is the possibility of automation. They can perform repetitive tasks such as assembling and inspecting. These previously cost time and effort. Now, they can be easily done through intelligent robots. Not only does this increase productivity, it frees up human workers for valuable decision-making tasks.
Recognition
Machine vision greatly simplifies product identification. Industries leverage edge computing and cloud databases. A 2D vision system can scan a simple barcode to reveal product details. Through technologies like OCR, vision systems can read product details on their packaging. Furthermore, machine vision systems can perform these tasks simultaneously for multiple workpieces.
Emerging Use-cases
Due to the immense value provided by machine vision systems, more industries are absorbing them. In recent years, we have seen a shift in critical sectors like agriculture, medicine, and automotive. Machine vision forms the backbone of automated irrigation, crop inspection, and harvesting systems. Medical robots rely on machine vision for operational and surgical tasks. The rise of self-driving cars is another significant application of machine vision.
Advantages of Machine Vision
In the rapidly developing industrial landscape, machine vision is a core driver of progress. It benefits manufacturers by improving product quality, productivity, and equipment effectiveness. It is reshaping the supply chain by introducing high-speed tracking. Wherever it is implemented, it reduces waste, overcomes worker shortages, and improves processes. Reducing the dependence on manual labor not only adds value to lives but also improves worker safety.
Conclusion
Machine vision has become a staple technology in the industry for good reason. The immense benefits it provides can be compared to a few other technologies. However, it is crucial to remember that it is not a silver bullet to solve bad administrative practices. It is necessary to keep in mind core business objectives. Factory infrastructure and operational procedures are equally important. Machine vision brings together all these components of a good business. It delivers immense benefits when implemented the right way. For more questions on machine vision and how you can apply it to your business, reach out to our professional team.