Machine vision system analysis
A typical vision system generally includes a light source, an optical system, a camera, an image processing unit (or image acquisition card), an image analysis processing software, a monitor, a communication/input/output unit, and the like.
â– Image acquisition
The acquisition of the image is actually the conversion of the visualized image and intrinsic features of the measured object into data that can be processed by the computer, which directly affects the stability and reliability of the system. A light source, an optical system, a camera, an image processing unit (or an image capture card) are generally used to obtain an image of a measured object.
â– light source
The light source and the important factors that affect the input of the machine vision system, because it directly affects the quality of the input data and at least 30% of the application effect. Because there is no universal machine vision lighting device, for each specific application example, the corresponding lighting device must be selected in order to achieve the best results. Many industrial machine vision systems use visible light as the light source, mainly because of the easy availability of visible light, low price, and ease of operation. Several commonly used visible light sources are white flag lamps, fluorescent lamps, mercury lamps, and sodium lamps. However, one of the biggest drawbacks of these light sources is that light energy cannot be stabilized. Taking the fluorescent lamp as an example, in the first 100 hours of use, the light energy will drop by 15%, and the light energy will continue to decrease with the increase of the use time. Therefore, how to make the light energy stable to a certain extent is an urgent problem that needs to be solved in the process of practical application. On the other hand, ambient light will change the total light energy emitted by these light sources onto the object and cause the output image data to be noisy. In general, the method of adding protective screens is used to reduce the influence of ambient light. Due to the above problems, in today's industrial applications, for some highly demanding inspection tasks, invisible light such as X-rays, ultrasonic waves, etc. is often used as a light source.
Illumination system composed of light sources can be classified according to its irradiation methods: back-illumination, forward-direction illumination, structured light and flash light illumination. Among them, back-illumination is that the object to be measured is placed between the light source and the camera. Its advantage is that it can obtain high-contrast images. Forward illumination is the light source and the camera is located on the same side of the measured object. This way is easy to install; Light illumination projects a grating or a linear light source onto the object to be measured, and demodulates the three-dimensional information of the object to be measured according to the distortions generated by them. The strobe illumination irradiates high-frequency light pulses onto the object. The scanning speed is synchronized with the strobe speed of the light source.
â– Optical system
For machine vision systems, the image is the only source of information, and the quality of the image is determined by the proper selection of the optical system. In general, errors due to poor image quality cannot be corrected by software. Machine vision technology combines optical components with imaging electronics and uses computer control systems to resolve, measure, classify, and detect components that are passing through automated processing systems. Machine vision systems can usually detect 100% of the processed product without slowing down the production line. This ability is very important as more and more manufacturers are demanding â€œ6-sigmaâ€ (less than 3 million effective units) results in order to be more competitive in todayâ€™s quality-conscious market. . In addition, these systems can work very well with Satisfaction Process Control (SPC).
The main parameters of the optical system are related to the format of the photosensitive surface of the image sensor, and generally include: aperture, field of view, focal length, F number, and the like.
The camera is actually a photoelectric conversion device that converts the optical image received by the image sensor into an electrical signal that can be processed by the computer. The photoelectric conversion device is the core device that constitutes the camera. At present, typical photoelectric conversion devices are vacuum camera tubes, CCDs, CMOS image sensors, and the like.
The vacuum television camera tube consists of a camera target and an electron gun sealed in a glass tube cover. The imaging target converts the illuminance distribution of the input optical image into the two-dimensional spatial distribution of the corresponding pixel charge on the target surface, mainly accomplishes the photoelectric conversion and charge storage tasks; the electron gun completes the scanning and picking process of the image signal. The TV camera tube imaging system has features such as high definition, high sensitivity, wide spectrum, and high frame rate imaging. However, because the TV camera tube is a vacuum tube device, its weight, volume, and power consumption are relatively large.
CCD is the most commonly used image sensor in machine vision. It integrates photoelectric conversion, charge storage, charge transfer, and signal reading. It is a typical solid-state imaging device. The outstanding characteristic of CCD is that the charge is used as the signal, and the device is different from the current or voltage signal. Such an imaging device forms a charge packet by photoelectric conversion, and then transfers and amplifies the output image signal under the action of a driving pulse. A typical CCD camera consists of an optical lens, a timing and synchronization signal generator, a vertical driver, and an analog/digital signal processing circuit. The following figure shows the block diagram of the CCD camera. As a functional device, CCD has the advantages of no burn, no hysteresis, low voltage operation and low power consumption compared with vacuum tubes.
The development of CMOS (Complementary metal Oxide Semiconductor) image sensors first appeared in the early 1970s. In the early 1990s, with the development of VLSI manufacturing technology, CMOS image sensors have developed rapidly. The CMOS image sensor integrates a photosensor array, an image signal amplifier, a signal reading circuit, an analog-to-digital conversion circuit, an image signal processor, and a controller on a single chip, and also has the advantage of local random pixel programming accesses. Currently, CMOS image sensors are widely used because of their good integration, low power consumption, wide dynamic range, and almost no smearing of output images.
Image processing and analysis
In the machine vision system, the light signal received by the camera's main functional light-sensitive element is converted into a voltage amplitude signal output. To obtain the digital signals processed and recognized by the computer, the video information needs to be quantified. The image capture card is an important tool for quantifying video information.
â– Image acquisition/processing card
The image capture card mainly completes the digitization of analog video signals. The video signal is first filtered by a low-pass filter and converted into an analog signal that is continuous in time; according to the requirements of the image resolution of the application system, the sample/hold circuit is used to sample the video signal of the border in time and the video is sampled. The signal is converted to a discrete analog signal; it is then converted by the A/D converter to a digital signal output. While the image acquisition/processing card has the function of analog-to-digital conversion, it also has the functions of analyzing and processing video images, and at the same time can effectively control the camera.
Image processing software
In the machine vision system, the processing technology of visual information mainly depends on image processing methods. It includes image enhancement, data encoding and transmission, smoothing, edge sharpening, segmentation, feature extraction, image recognition and understanding. After these processes, the quality of the output image is improved to a considerable degree, which not only improves the visual effect of the image, but also facilitates computer analysis, processing, and recognition of the image.
Application of machine vision system
The machine vision system is an effective way to realize the precision control, intelligence, and automation of instrumentation and equipment. It can be called the â€œmachine eyeâ€ of modern industrial production. Its greatest advantages are:
(1) Achieve non-contact measurement. Neither the observation nor the observed damage will occur, which will increase the reliability of the system.
(2) has a wider spectral response range. Machine vision can use special photosensors to observe the world that humans can't see, thus extending the human visual range.
(3) work long hours. It is difficult for humans to observe the same object for a long time. Machine vision systems can perform observation, analysis, and identification tasks for long periods of time and can be used in harsh working environments.