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Definition
Machine vision optics are optics and optical elements (illuminators, lenses, mirrors, prisms, and other optical elements) that are designed and built to enable visual inspection to be carried out in an automated manner, i.e., by a machine. Visual inspection (usually done for almost any industrially produced product) consists of checks on various aspects of the condition or state of an object of interest. Some examples of inspected quantities include:
- Shape, size, dimensional stability
- Correct position and orientation in space
- Optical properties, such as color and appearance
- Presence or absence of defects
- Presence or absence of expected component parts (e.g., in a circuit board assembly)
For more information on machine vision optics, one useful and extensive reference is "Machine Vision - Automated Visual Inspection: Theory, Practice and Applications", Beyerer, Puente Leon, Frese, Springer Verlag, 2016.
Robotic vision sensor camera system in a medicine factory
How Do Machine Vision Optics Work and What Problem Do They Solve?
Using illumination, lenses and sensors, machine vision optics capture information relevant to a task that will be carried out by a computer. Many tasks can potentially be tackled with machine vision systems, such as detection of cancer, classification of fruits into different grades, inspection of pass/fail on a production line, estimation of tumor volume, determination of crop ripeness, etc.
Additionally, industrial inspection makes use of machine vision to measure the specific size and dimension of test objects, to check for product quality. This saves the task of needing to measure each part manually, which is especially time consuming in mass production environments.
What Industries and Applications Would Machine Vision Optics Be Good for?
Almost every manufacturer involved in production and delivery of goods to the market benefits from visual inspection of product quality. Therefore, machine vision and the provision of automatic inspection for various needs can support a wide range of industries and applications.
Recycling centers use spectroscopy to sort plastics according to type, and to collect the different types in different bins.
Agricultural supply chains can make use of machine vision. For example, with types of lettuce moving on a conveyor belt, the system can help sort the light green lettuce varieties from the dark green varieties, which are then packaged separately.
Industries such as medical distribution, pharmaceuticals, automotive hardware and electrical supply, consumer electronics mass production, aviation, avionics, and the supply chains supporting them make use of machine vision for production inspection tasks.
Barcode machine vision technology for sushi industrial production line conveyor belt in the food factory.
How Do You Develop and Model an Optical System for Machine Vision?
To develop and model an optical system for machine vision, the first step is to define the requirements. Often the starting point includes requirements for a specific focal plane to be used in the imaging system, with a certain sensitivity. This has implications on the illumination system, including the light source, power, and collection efficiency. The reflectivity of the inspected surface (how much light is reflected into the imaging path) affects the development of the illumination system.
On the imaging system side, key factors include:
- The feature size to be resolved on the inspected part
- The numerical aperture needed for resolution, depth of focus, and signal level
- The stand-off distance between the inspected part and the lens
- The area (field of view) of the inspected part to be imaged
- Polarization characteristics of the object
Also, there may be system-level metrics dealing with more general aspects of the system performance. For example:
- Requirements for high signal to noise ratio, including requirements for high speed camera function and exposure
- Hyper-centric imaging of parts with acceptable distortion and large viewing angles for sidewalls of units under test can create unique illumination requirements
- Requirements to have very large (or narrow) part scales can drive unique requirements as well
- Detector array characteristics like pixel pitch and chief ray angle limitations (one input into telecentricity)
- Telecentricity requirements so that a change in focus does not change the object size.
- Spectral range, sample wavelengths, and wavelength weights
- Magnification
- Permissible distortion
- Envelope constraints
- Weight constraints (if any)
- Production quantities
- Cost goal
Machine vision systems use image signal processing (ISP) to perform visual inspection. Design considerations for the ISP routines include:
- Prior knowledge of the object to be identified. This is beneficial for designing and training the system so that the detection algorithms are optimized for a particular object ensemble
- Knowledge of imaging system characteristics, such as resolution and field of view, and image sensor characteristics including pixel array dimensions and pixel size
- Illumination conditions of the scene
- Image processing techniques such as edge detection and segmentation
- Object or feature detection methods
For the overall system, other questions to be determined are the overall envelope and the cost.
It is crucial to design an optical system that captures as much information that is relevant to the task as possible, while being aware of how the image processing methods can modify or improve system performance. Design parameters may include structured illumination, coherence of the light sources, reflectivity, absorption, scattering, spectral response, phase contrast, polarization property, radiometry, etc.
To aid the development of a machine vision system, 六合彩直播开奖 offers LightTools for illumination design, CODE V for optical system design, and ImSym as the platform that provides and end-to-end model of the system including lens system, detector characteristics, and image signal processing methods.
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