Machine Vision stands at the forefront of innovation, transforming industries across the board.
It represents a cutting-edge field within computer vision that channels the power of refinement and imaging systems to enable machines to perceive, interpret, and comprehend visual information.
It represents a cutting-edge field within computer vision that channels the power of refinement and imaging systems to enable machines to perceive, interpret, and comprehend visual information. This technology has broad applications across multiple sectors, including manufacturing, healthcare, automotive, agriculture, and more. It can automate processes, enhance accuracy, improve efficiency, and enable real-time decision-making making it a significant component in the times of automation and smart systems.
What is machine vision?
Machine vision systems consist of electronic components such as cameras, lighting systems, and sensors along with specialized software that work together to analyze images.
Machine vision is a real-time method of inspecting components that is both rapid and accurate and can also picture and analyze every item coming down a high-speed line, ensuring great quality control.
Components of machine vision
Machine vision systems are practically composed of five components. When these components work together by playing their individual roles, they create a vision system capable of seamless functions.
- Lightning
- Lenses
- Image sensors
- Vision processing
- Communications
1. Lightning
The role of lightning is directly linked to illuminating the object and highlighting its different features to be viewed by the camera.
It is one of the significant aspects of machine vision systems, as the camera can’t inspect objects that are not seen. For the same, lighting parameters such as the distance of the light source from the camera and object, angle, intensity, brightness, shape, size, and lighting color must be optimized to spot the inspected attributes. In the same context, the object must be seen clearly by the camera when it is hit by light hence, its properties of surface must also be considered during lighting optimization.
The lighting can be provided with the help of LED, quartz halogen, fluorescent, and xenon strobe light sources, and on the other side it can be directional or diffusive as well.
2. Lenses
The lens captures the image and shares it with the image sensor in the camera. The work of the lens may change when it comes to optical quality and price because the lens used determines the quality and resolution of the captured image.
Probably system cameras come with two main types of lenses:
- Interchangeable lenses
- Fixed lenses
The right combination of lens and extension will capture the best image whereas a fixed lens as part of a stand-alone vision system generally uses autofocus, which could be either a mechanically adjusted lens or a liquid lens that can automatically focus on the part.
3. Image sensors
The job of the image sensor inside the machine vision camera is to convert the light captured by the lens into a digital image.
It typically utilizes a charged coupled device or complementary metal-oxide-semiconductor technology to translate photos into electrical signals. The output of image sensors is a digital image composed of pixels that show the presence of light in the areas that the lens has observed.
4. Vision processing
The vision processing is the mechanism for getting the information from a digital image, this process may take place externally in a PC based system or internally in a standalone vision system as well.
The vision processing is performed by software and involves several steps.
Step 1: An image is acquired from the sensor, sometimes pre-processing may be required to optimize the image and ensure that all the necessary features stand out.
Step 2: Secondly, the software locates the features, performs measurements, and compares them to the specifications.
Step 3: Finally, a decision is made and the results are communicated.
5. Communications
The communication system then quickly approves the decision made by the vision processing unit to specific machine elements. Once the machine elements get the information, the machine elements will intervene and control the process based on the output of the vision processing unit.
This mechanism is accomplished by data communication by a serial connection in the form of Ethernet.
How does machine vision work?
To understand the functioning of a machine vision system, let us go this way: Imagine a machine vision carrying out a common task, like inspecting a product.
In the first phase, the sensor identifies the presence of a product. When the sensor finds a product, it indicates the camera to take a picture and activates a light source to highlight important details.
Then a commonly used device frame-grabber converts the camera’s image into digital data, which is then stored in the memory of the computer. This data can be processed using the software.
For the image to be processed, computer software undertakes multiple steps. Like, the image is simplified into black and white. In the same frame, machine vision analyzes the image to spot defects and accurate components based on predetermined standards.
Once the analysis is complete, the product receives a pass or fail judgment based on the machine vision system’s evaluations.
Types of machine vision
| The types of machine vision |
|---|
| 1D vision system |
| 2D vision system |
| 3D vision system |
Machine vision encompasses various technologies and approaches used for machines to gain an understanding of visual information.
Some types of machine vision includes,
1. 1D vision system
1D vision systems, also known as line scan imaging systems, are a type of machine vision technology that primarily deals with capturing and analyzing one-dimensional images.
Instead of capturing a complete 2D image in a single shot, line scan cameras capture images line-by-line, often using a linear group of pixels. These cameras continuously scan objects, building a 1D image by stitching together individual lines.
2. 2D vision system
2D vision systems provide area scans that work well for discrete parts. 2D systems are available in a continually-expanding range of resolutions. Mainstream, general-purpose applications usually feature resolutions with an upper limit of around 5 MPixels.
Whereas a 2D area array system takes a two-dimensional snapshot of an object, 2D line scan systems build images line-by-line.
3. 3D vision system
A 3D vision system, also known as 3D machine vision, is a technology that enables machines to perceive and understand three-dimensional objects or environments.
Unlike traditional 2D vision systems that capture images in two dimensions, 3D vision systems capture depth information along with the height and width of objects, providing a more comprehensive understanding of the scene.
Benefits of machine vision system
Increased efficiency:
Machine vision systems and analysis processes reduce human error and speeds up tasks, leading to higher production rates and improved operational efficiency.
Improved quality control:
Machine vision systems ensure consistent and precise inspection, detecting defects, inconsistencies, or any errors in products or processes that may not be easily visible to the human eye.
Cost savings:
With the help of the machine vision system, it becomes very convenient to reduce errors and waste in manufacturing processes and can lead to cost savings by minimizing rework, scrap, and product recalls.
Enhanced accuracy and precision:
Machine vision provides highly accurate and repeatable measurements, leading to better quality assurance and adherence to specific standards.
Increased production flexibility:
The best part about the machine vision systems is that they can adapt to different products or components without re-programming extensively, allowing for quick setup and changeover between different production runs.
Real-time monitoring and feedback:
Having a machine vision system, it becomes easy to monitor and analyze in real-time prompt identification of issues, facilitating timely adjustments and reducing downtime.
Higher throughput:
Automation through machine vision allows for faster inspection and analysis, leading to increased output and higher productivity in manufacturing and assembly lines.
Data collection and analysis:
Machine vision systems collect vast amounts of data that can be analyzed for process optimization, predictive maintenance, and decision-making, providing insights for continuous improvement.
Applications of machine vision system
Object detection
Object detection in machine vision involves identifying and locating multiple objects within an image or a video frame. It’s a critical task that aims to precisely describe and classify various objects or entities available.
Measurement
Measurement in machine vision involves determining accurate dimensions, distances, angles, or other quantitative information from images captured by cameras or sensors.
Flaw detection
Flaw detection in machine vision involves the use of imaging systems to identify and highlight defects, abnormalities, or irregularities in products or materials.
Print defect identification
Print defect identification in machine vision involves using image processing techniques to detect and classify defects in printed materials, such as labels, packaging, documents, or other printed surfaces.
Identification
Identification in machine vision refers to the process of recognizing and distinguishing objects or patterns within images or videos captured by cameras or sensors.
Locating
In machine vision, locating refers to the process of identifying and determining the precise position of an object or a specific feature within an image or a scene.
Counting
Counting in machine vision involves the use of image processing techniques to accurately tally objects within an image or a series of images.
Color verification
Color verification in machine vision involves assessing and verifying the colors of objects or products within an image to ensure they meet specific color standards or criteria.
Part verification
Part verification in machine vision involves using a visual inspection system to verify the correctness and quality of manufactured parts or components.
Traceability
Traceability is the ability to track and trace products or components throughout the production or assembly process using visual information obtained through machine vision systems.
Pattern matching
Pattern matching in machine vision involves comparing a given pattern or image against a reference template to identify similarities or matches.
Barcode and optical character recognition
Utilized for reading and decoding barcodes, serial numbers, text, or characters on products, packages, labels, and documents in logistics, retail, and automation.
Medical imaging and diagnostics
Employed in medical applications for image analysis, diagnosis, and treatment planning using modalities such as X-rays, MRIs, CT scans, ultrasound, and endoscopy.
Which industries commonly utilize machine vision systems?
Machine vision systems are incredibly versatile and beneficial across a wide range of industrial sectors where repeated procedures, quality control, and precision are crucial.
Here is the list of industries that are highly reliable on Machine vision systems:
- FMCG industry
- Pharmaceutical industry
- Automobile and automotive industry
- Printing and packaging industry
Computer Vision vs. Machine Vision
Computer Vision :
Computer vision is a broader field that is enclosed by science and technology to enable computers to gain a high-level understanding of digital images or videos.
It focuses on replicating human vision capabilities using algorithms and models, one of the primary goals of computer vision is to enable machines to see and interpret the visual world, recognize objects, understand scenes, and extract meaningful information from images or videos.
Machine vision :
Machine vision is a specific application of computer vision technology primarily focused on industrial processes and automation.
It involves the use of vision systems, cameras, sensors, and software to perform automated inspection, measurement, guidance, and control within manufacturing or industrial environments.
