Machine vision is an innovative technology that enables automated devices to “see,” aka carry out a visual inspection. This technology relies on machine vision optics to capture visual details about the environment. Its integrated software and hardware then process the details and use them to make decisions in practical applications. Machine vision can pick out aspects such as:
- The shape and size of an object,
- The orientation of an object,
- The appearance of an object, e.g., color,
- The presence of defects in an object, and
- The presence or absence of a component in an object, etc.
A good example of machine vision would be quality control in an assembly line. The machine can assess if the products meet the set criteria and flag those that do not. That way, a manufacturer can avoid releasing faulty products into the market. Another example would be robotic harvesting.
The machine can determine which crops are ready for harvest, sort them, and harvest the right ones. It can also identify weeds and insects, ensuring the harvest meets the requirements.
These versatile systems can also perform the following tasks:
- Recognizing handwriting, optical characters, patterns, objects, materials, and signatures,
- Analyzing components in circuit boards,
- Counting items, and
- Analyzing currencies.
With the integration of artificial intelligence, these systems can quickly analyze a wide range of images.
Optics Used in Machine Vision
Machine vision relies on an array of optics to capture and process images. These include:
Lenses in machine vision serve the role of locating the image details, maintaining focus on the object, and highlighting the contrast in the image. The lenses used must visualize the contrast even when the focal length and the sensor position remain unchanged. An easy way to increase the contrast is to use filters that let the lenses filter colors.
The details captured by the lenses are subject to recreation by the software. Thus, the choice of lenses matter, and one must pay attention to the following:
- The field of view, which is the image area covered by the lenses,
- The working distance between the object and the lenses, and
- The depth of field, which determines how much working distance one can have,
Diffraction and distortion affect the final image results even when the correct lenses are used. These issues are often compensated for by using software to correct these issues. Even so, the focus should be on choosing the proper lenses.
The sensor processes the image from the camera and converts it into a digital format, aka pixels. The size of the sensor ultimately determines its active area, which you can measure in the horizontal dimension. You achieve primary magnification by determining the sensor size and field of view ratio. The rule of thumb is that the larger the sensor, the better the image quality.
Moreover, sensors affect the image resolution, which is the machine vision’s ability to reproduce images in detail. When you use small sensors, the system will likely miss out on the fine details. But a larger sensor would achieve this with ease. Even so, you must use the right zoom and distance to view the object distinctly.
3. Other Components
The other components of machine vision are as follows:
- Lighting: Light illuminates the object or environment, enabling the system to capture its details. Thus, the better the lighting, the better the image quality,
- Camera: The lens and the sensor fall under this section and play the part of capturing and digitizing the image, and
- Processor: The machine vision system relies on a processor to run the software that extracts the details captured.
The last piece of the puzzle is the connection which integrates the machine optics, lighting, and processor to pave the way for image creation.