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
• Lighting
• 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
• Digital systems architecture
• Software
• Industrial engineering
• Human-computer interfacing
• Control systems
• Manufacturing
• Existing work practices and QA methods
Machine vision (MV System) 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 encompasses 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:
- One or more digital or analog camera (black-and-white or colour) with suitable optics for acquiring images
- Camera interface for digitizing images (widely known as a "frame grabber")
- A processor (often a PC or embedded processor, such as a DSP)
- (In some cases, all of the above are combined within a single device, called a smart camera).
- Input/Output hardware (e.g. digital I/O) or communication links (e.g. network connection or RS-232) to report results
- Lenses to focus the desired field of view onto the image sensor.
- Suitable, often very specialized, light sources (LED illuminators, fluorescent or halogen lamps etc.)
- A program to process images and detect relevant features.
- A synchronizing sensor for part detection (often an optical or magnetic sensor) to trigger image acquisition and processing.
- 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 digitalpixel) and places the image in computer memory so that it may be processed by the machine vision software. format (typically a two dimensional array of numbers, corresponding to the luminous intensity level of the corresponding point in the field of view, called
The software will typically take several steps to process an image. Often the image is first manipulated to reduce noise or to convert many shades of gray to a simple combination of black and white (binarization). Following the initial simplification, the software will count, measure, and/or identify objects, dimensions, defects or other features in the image. As a final step, the software passes or fails the part according to programmed criteria. If a part fails, the software may signal a mechanical device to reject the part; alternately, the system may stop the production line and warn a human worker to fix the problem that caused the failure.
Though most machine vision systems rely on black-and-white cameras, the use of colour cameras is becoming more common. It is also increasingly common for Machine Vision systems to include digital camera equipment for direct connection rather than a camera and separate framegrabber, thus reducing signal degradation.
"Smart" cameras with built-in embedded processors are capturing an increasing share of the machine vision market. The use of an embedded (and often very optimized) processor eliminates the need for a framegrabber card and external computer, thus reducing cost and complexity of the system while providing dedicated processing power to each camera. Smart cameras are typically less expensive than systems comprising a camera and a board and/or external computer, while the increasing power of embedded processors and DSPs is often providing comparable or higher performance and capabilities than conventional PC-based systems.
