Machine Vision is a technology that was introduced in the 1950s. However, it has grown in manifolds with its booming valuation of $3.9 billion in 2022. Moreover, it is also speculated to grow with a CAGR of more than 18% between 2023 to 2032. These numbers show the revolution and growth this remarkable technology has brought into various industries through its power of image processing.
Therefore, this is the ripe time to tap into the potential of this transformative Machine Vision Technology and automate your business processes. However, if you do not completely understand its concept and usage, read the article below till the end.
The Diverse Definitions of Machine Vision
Machine Vision is a technology where machines, like computers and robots, use special cameras to see and understand the world around them. It helps machines make decisions based on what they “see” by using images or videos. Think of it as allowing machines to see and analyze things as you do with your eyes. But with the help of computers and cameras.
According to the Automated Imaging Association, machine vision encompasses both industrial and non-industrial applications. This involves hardware and software to guide devices in executing their functions based on image capture and processing.
Applications of Machine Vision
Machine vision is used in various practical applications. Some of them are as follows:
1. Quality Control
Machine vision can inspect products on assembly lines for defects, ensuring that only high-quality items reach consumers. It can also spot flaws like scratches, dents, or missing parts. Machine vision is an important part of today’s quality check tool, most modern industries have static, and in-motion high speed machine vision systems installed where a naked eye makes quality check impossible. Image processing plays an important role backed with deep software algorithms.
2. Robotics and Automation
Robots can use machine vision to navigate and perform tasks in dynamic environments. They can locate and pick up objects, weld, or assemble parts with precision. System integrators with expertise integrating Robots with machine vision control systems can provide excellent solutions to a many routine tasks being performed with an assembly production floor.
3. Medical Imaging
In the field of healthcare, machine vision aids in diagnosing and analyzing medical images like X-rays and MRIs. This helps doctors detect diseases and abnormalities.
4. Agriculture
It is used to monitor crop health and optimize farming processes. Drones equipped with machine vision can survey fields, identifying areas that require attention, such as pests or irrigation. Recent drone technology imaging provides farms a firsthand overview of their crop.
5. Traffic and Security
Machine vision helps in traffic management and security systems. It can read license plates, identify faces, and detect unusual activities to enhance safety and surveillance. Modern cities within North America have installed vision system-based traffic light violators, which capture vehicles license plate crossing red traffic lights and generates automatic violation tickets to be mailed to drivers.
Components of a Machine Vision System
Machine vision systems comprise numerous components. Some of its components are:
1. Cameras
A wide range of cameras with varying interfaces, resolutions, etc., are available for machine vision systems. The choice of camera depends on the level of detail required and the specific application.
2. Smart Cameras
Smart cameras are essential when specific information needs to be extracted from images. They often contain communication interfaces for data transfer. Thus, these systems are capable of generating descriptions and making decisions.
3. 3D Cameras
3D cameras enable the depth of an item to be displayed, providing different perspectives and depth perception. It is used in such systems, so it can prove to be invaluable in certain applications.
4. Thermal Imaging Cameras
Thermal imaging cameras render images using infrared radiation. This allows machine vision systems to display heat patterns and identify areas of interest based on temperature differences.
5. Software and Processing
Software is a critical component of these systems. It allows operators to visualize data, program the hardware’s functions, and process images. Machine vision software packages and programs employ various digital image processing techniques to extract essential information and make decisions based on the data.
Image Processing Methods
Machine Vision systems employ a variety of image-processing methods to extract valuable information from captured images. These methods include
1. Stitching/Registration
Stitching or registration involves combining images from different cameras, especially when 2D and 3D cameras work together. This process aligns and merges multiple images into a single, coherent image. It is often used in applications like panoramic photography or robotics and industrial automation to create a complete view of an object.
2. Filtering
Filtering is about categorizing images and applying filters to match specific criteria. These criteria can be related to factors such as shape, size, material, or weight. This further allows the system to quickly identify or discard images that do not meet the desired specifications, helping in quality control and sorting tasks.
3. Thresholding
Thresholding is a method used to separate images into distinct portions based on a predefined grey value. This technique is commonly used to isolate specific areas or objects within an image. For instance, it can be applied to identify defects or to segment the foreground from the background.
4. Pixel Counting
Pixel counting involves counting the number of light or dark pixels in an image. This method is useful for tasks such as detecting labels or objects. It further allows the system to make decisions about the presence or absence of specific features or characteristics in an image.
5. Segmentation
Segmentation is the process of breaking up digital images into meaningful segments for easier analysis. It can be used to identify regions of interest within an image. This allows the system to focus on specific areas for object recognition and measurement.
6. Edge Detection
Edge detection involves spotting the edges of items and objects within an image. Machine learning techniques are often used to recognize various edges, as they are crucial for tasks like object contour extraction, defect detection, and pattern recognition.
7. Color Analysis
Color analysis in machine vision is all about identifying and assessing quality using color information. This method isolates features, items, or products based on their color. Further, it allows for color-based sorting, quality control, and recognition of objects based on their hues and shades.
8. Blob Detection and Extraction
Blob detection and extraction involve identifying discrete blobs of connected pixels in an image. These blobs represent regions with similar characteristics, such as color or intensity. This method is valuable in detecting errors or variances in a product, such as finding defects in manufactured items.
Conclusion
Machine vision is a transformative technology that offers countless opportunities in daily business operations. It has further assured to shape the future of numerous industries with its adaptability and continuous advancements, providing solutions for a wide range of applications and challenges.
