Machine vision (MV) is the application of computer vision to industry and manufacturing. Whereas computer vision is mainly focused on machine-based image processing, machine vision most often requires also digital input/output devices and computer networks to control other manufacturing equipment such as robotic arms. Machine Vision is a subfield of engineering that incorporates computer science, optics, mechanical engineering, and industrial automation. One of the most common applications of Machine Vision is the inspection of manufactured goods such as semiconductor chips, automobiles, food and pharmaceuticals. Just as human inspectors working on assembly lines visually inspect parts to judge the quality of workmanship, so machine vision systems use digital cameras, smart cameras and image processing software to perform similar inspections.
Machine vision systems are programmed to perform narrowly defined tasks such as counting objects on a conveyor, reading serial numbers, and searching for surface defects. Manufacturers favour machine vision systems for visual inspections that require high-speed, high-magnification, 24-hour operation, and/or repeatability of measurements. Frequently these tasks extend roles traditionally occupied by human beings whose degree of failure is classically high through distraction, illness and circumstance. However, humans may display finer perception over the short period and greater flexibility in classification and adaptation to new defects and quality assurance policies.
Computers do not 'see' in the same way that human beings are able to. Cameras are not equivalent to human optics and while people can rely on inference systems and assumptions, computing devices must 'see' by examining individual pixels of images, processing them and attempting to develop conclusions with the assistance of knowledge bases and features such as pattern recognition engines. Although some machine vision algorithms have been developed to mimic human visual perception, a number of unique processing methods have been developed to process images and identify relevant image features in an effective and consistent manner. Machine vision and computer vision systems are capable of processing images consistently, but computer-based image processing systems are typically designed to perform single, repetitive tasks, and despite significant improvements in the field, no machine vision or computer vision system can yet match some capabilities of human vision in terms of image comprehension, tolerance to lighting variations and image degradation, parts' variability etc.
Components of a machine vision system
A typical machine vision system will consist of several among the following components:
1. One or more digital or analog camera (black-and-white or colour) with suitable optics for acquiring images
2. Camera interface for digitizing images (widely known as a "frame grabber")
3. A processor (often a PC or embedded processor, such as a DSP)
4. (In some cases, all of the above are combined within a single device, called a smart camera).
5. Input/Output hardware (e.g. digital I/O) or communication links (e.g. network connection or RS-232) to report results
6. Lenses to focus the desired field of view onto the image sensor.
7. Suitable, often very specialized, light sources (LED illuminators, fluorescent or halogen lamps etc.)
8. A program to process images and detect relevant features.
9. A synchronizing sensor for part detection (often an optical or magnetic sensor) to trigger image acquisition and processing.
10. Some form of actuators used to sort or reject defective parts.
The sync sensor determines when a part (often moving on a conveyor) is in position to be inspected. The sensor triggers the camera to take a picture of the part as it passes beneath the camera and often synchronizes a lighting pulse to freeze a sharp image. The lighting used to illuminate the part is designed to highlight features of interest and obscure or minimize the appearance of features that are not of interest (such as shadows or reflections). LED panels of suitable sizes and arrangement are often used to this purpose.
The camera's image is captured by the framegrabber. A framegrabber is a digitizing device (within a smart camera or as a separate computer card) that converts the output of the camera to digital format (typically a two dimensional array of numbers, corresponding to the luminous intensity level of the corresponding point in the field of view, called pixel) and places the image in computer memory so that it may be processed by the machine vision software.
Commercial and open source machine vision software packages typically include a number of different image processing techniques such as the following:
* Pixel counting: counts the number of light or dark pixels
* Thresholding: converts an image with gray tones to simply black and white
* Segmentation: used to locate and/or count parts
o Blob discovery & manipulation: inspecting an image for discrete blobs of connected pixels (e.g. a black hole in a grey object) as image landmarks. These blobs frequently represent optical targets for machining, robotic capture, or manufacturing failure.
o Recognition-by-components: extracting geons from visual input
o Robust pattern recognition: location of an object that may be rotated, partially hidden by another object, or varying in size
* Barcode reading: decoding of 1D and 2D codes designed to be read or scanned by machines
* Optical character recognition: automated reading of text such as serial numbers
* Gauging: measurement of object dimensions in inches or millimeters
* Edge detection: finding object edges
* Template matching: finding, matching, and/or counting specific patterns
In most cases, a machine vision system will use a sequential combination of these processing techniques to perform a complete inspection. E.g. A system that reads a barcode may also check a surface for scratches or tampering and measure the length and width of a machined component.
Applications of machine vision
The applications of Machine Vision (MV) are diverse, covering areas of endeavour including, but not limited to:
* Large-scale industrial manufacture
* Short-run unique object manufacture
* Safety systems in industrial environments
* Inspection of pre-manufactured objects (e.g. quality control, failure investigation)
* Visual stock control and management systems (counting, barcode reading, store interfaces for digital systems)
* Control of Automated Guided Vehicles (AGVs)
* Automated monitoring of sites for security and safety
* Monitoring of agricultural production
* Quality control and refinement of food products
* Retail automation
* Consumer equipment control
* Medical imaging processes (e.g. Interventional Radiology)
* Medical remote examination and procedures
* Vision for Humanoid or Robot also called Robot Vision
* Provide Artificial Visual Sensing for the blind (e.g. Super Vision System, Artificial Eye System)
Machine vision systems are widely used in semiconductor fabrication; indeed, without machine vision, yields for computer chips would be significantly reduced. Machine vision systems inspect silicon wafers, processor chips, and subcomponents such as resistors and capacitors.
In the automotive industry, machine vision systems are used to guide industrial robots, gauge the fit of stamped metal components, and inspect the surface of the painted vehicle, weld seams, engine blocks and many other components for defects.
Though machine vision techniques were developed for the visible spectrum, the same processing techniques may be applied to images captured using imagers sensitive to other forms of spectra such as infrared light or x-ray emissions.
Machine Vision is concerned with the engineering of integrated mechanical-optical-electronic-software systems for examining natural objects and materials, human artifacts and manufacturing processes, in order to detect defects and improve quality, operating efficiency and the safety of both products and processes. It is also used to control machines used in manufacturing. Machine Vision necessarily involves the harmonious integration of elements of the following areas of study
• Mechanical handling
• Optics (conventional, fibre optics, lasers, diffractive optics)
• Sensors (video cameras, UV, IR and X-ray sensors, laser scanners)
• Electronics (digital, analogue and video)
• Signal processing
• Image processing
• Digital systems architecture
• Industrial engineering
• Human-computer interfacing
• Control systems
• Existing work practices and QA methods