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    2021-10-21 17:38:33
    Understanding Machine Vision and Visual Sensor


    Topics covered in this article:

    What is machine vision?

    Composition of machine vision systems

    Characteristics of machine visual

    Working principle of visual sensors

    Development history of visual sensors

    Implementation of visual sensors

    Comparison of photoelectric sensors and visual sensors

    Applications of machine vision and visual sensor


    What is machine vision?

    The machine vision is an apparatus that automatically receives and processes an image of a real object by optical device and non-contact sensor to obtain the desired information or for controlling the movement of the robot.

    The machine vision is to use the machine instead of the human eye to measure and judge. The machine vision system converts the taken target into an image signal through the machine vision product (ie, the image pickup apparatus, divided CMOS and CCD). The image signal is then transmitted to a dedicated image processing system. According to the information such as pixel distribution, brightness, and color, the image signal is converted to a digitized signal. The imaging system performs various operations to extract the features of the target, and in turn, according to the result of the discrimination, the image is controlled.

    Composition of machine vision systems

    Camera and the lens

    This part belongs to the imaging device, and the usual visual system consists of a set or more such imaging systems. If there is a multi-channel camera, it may be exchanged by the image card to acquire image data. It is also possible to obtain multi-camera channel data simultaneously by simultaneous control. According to the required camera, the camera may be the output standard monochrome video (RS-170 / CCIR), the composite signal (Y / C), the RGB signal, may also be a non-standard progressive scan signal, line scan signal, high-resolution signal, etc...

    machine vision system.jpg

    machine vision system

    Light source

    As an auxiliary imaging device, the quality of imaging quality can often play a crucial role, and various shapes of LED lights, high-frequency fluorescent lamps, fiber-optic halogen lamps are easily obtained.

    Sensors

    Usually, it is in the form of a fiber switch, a proximity switch, etc., to determine the position and state of the subject, and inform the image sensor to perform the correct acquisition.

    Image capture card

    It’s usually in the form of a plug card in the PC. The main job of the image capture card is to deliver images output to the computer host. It converts analog or digital signals from the camera into a certain format of the image data stream, and it can control some of the parameters of the camera, such as trigger signals, exposure/integration time, shutter speed, and so on. Image capture cards typically have different hardware structures for different types of cameras, while also have different bus forms, such as PCI, PCI64, Compact PCI, PC104, ISA, etc.

    PC platform

    The computer is the core of a PC visual system, and the processing of image data and most of the control logic is completed. For the application of the detection type, the CPU of the higher frequency is usually required to reduce the time of processing. At the same time, in order to reduce the interference of industrial field electromagnetic, vibration, dust, temperature, etc., industrial-grade computers must be selected.

    Vision processing software

    The machine vision software is used to complete the processing of the input image data. Then the result can be obtained by a certain calculation. The result of this output may be a pass / fail signal, coordinate position, string, etc. Common machine vision software appears in the form of C / C ++ image library, ActiveX control, and graph-based programming environment, etc., which can be dedicated (for example, only for LCD detection, BGA detection, template alignment, etc.), or general-purpose (Including positioning, measurement, bar code/character identification, spots detection, etc.).

    Control unit (including I / O, motion control, level conversion unit, etc.)

    Once the visual software completes image analysis (unless used for monitoring), it is immediately necessary to communicate with the external unit to complete control of the production process. Simple control can directly utilize the I / O from the partial image acquisition card. Relatively complex logic/motion control must rely on an additional programmable logic control unit/motion control card to achieve the necessary action.

    Characteristics of machine visual

    Precision

    Since the human eye has physical limitations, there is a significant advantage in the accuracy of the machine. Even if the human eye relies on a magnifying glass or a microscope to detect the product, the machine will still be more accurate because its precision can reach a thousandth of inches.

    Repeatability

    The machine can complete the test work in the same way without feeling tired. In contrast, there is a slight difference in the human eye, even if the product is exactly the same.

    Speed

    Machine vision detects high-speed moving objects.jpg

    Machine vision detects high-speed moving objects

    The machine can detect the product faster. In particular, when detecting high-speed objects, such as production lines, the machine can improve production efficiency.

    Objectivity

    Human eye detection has a fatal defect, which is the subjectivity brought by emotions. The test results will change as the workers' mood, and the machine does not have an angry and sorrow, and the results of the test are naturally very reliable.

    Cost

    Since the machine is faster than people, an automatic detection machine can undertake several people's tasks. Moreover, the machine does not need to stop. It can work continuously, so it can greatly improve production efficiency.

    Working principle of visual sensors

    The visual sensor calculates the feature amount (area, gravity, length, position, etc.) of the object, and output data and determination results by image processing of images captured by the camera.

    The visual sensor has thousands of pixels that capture light from a whole image. The clearness of the image is usually measured by the resolution, represented by the number of pixels. Therefore, no matter how distance to target numbers or few centimeters, the sensors can "see" very delicate target images.

    After capturing images, the visual sensor compares them to the reference image stored in memory to make an analysis.

    Robot 3d vision and ai image detection.jpg

    Robot 3d vision and ai image detection

    The visual sensor is the core of the machine vision system and is the source of the maximum environment characteristics. It is necessary to accommodate components of various optical, mechanical, electron, and sensor, etc. of contour measurements and should be small and lightweight.

    Visual sensors include lasers, scanning motors, scanning mechanisms, angular sensors, linear CCD sensors, and their drivers and various optical components.

    Development history of visual sensors

    The visual sensor is in the late 1950s, and its development is very rapid. It is one of the most important sensors in the robot. The robot visual is initiated in the 1960s, which is later developed to deal with tables, chairs, table lamps, and other indoor scenery. After the 1970s, some practical visual systems have emerged, such as the application of integrated circuit production, precision electronics assembly, beverage can pack, etc. In addition, with the development of this discipline, some advanced ideas come from artificial intelligence, psychology, computer graphics, graphics processing, and other fields.

    The role of machine vision is to obtain the desired information from the three-dimensional environmental image and construct a clear and meaningful description of the observation object. Visual includes three processes: image acquisition, image processing, and image understanding. Image acquisition converts a three-dimensional environment image into an electrical signal through a visual sensor. Image processing refers to a transformation of an image to an image, such as feature extraction. Image appreciation, an environment description is given on a process. The core device of the visual sensor is the camera or CCD, the camera tube is an early product. OZD is developed later. The current CCD can be automatically focused.

    Implementation of visual sensors

    The visual sensor is a non-contact type. It is a combination of television cameras and other technologies, which is the most stable sensor in many sensors of the robot.

    The visual sensor of the robot has three measurement methods

    First, Directly process the brightness 6-point image of the dark and light images captured by the TV camera. Digitalize the brightness information, usually 4-10 bits, as 64 × 64-1024 × 1024 pixel output processing portions. Then, use various known algorithms to interpret the lines to identify the process. The difficulty of this image processing method is to handle huge output data. As the visual of the robot, it is often simplified into a dual value, and then the dedicated processing device is quickly processed.

    Second, the dark shadow image is dual value and reprocessing.

    Third, measure the switch and position of objects based on distance information. The approach adopted by the method has a variety of solutions such as triangular measurement and three-dimensional vision methods using two TV cameras.

    Comparison of photoelectric sensors and visual sensors

    The visual sensor gives larger flexibility to the machine designer compared to the photoelectric sensor. In the past, multiple photoelectric sensors are used, and now you can use a visual sensor to verify multiple features. The visual sensor can test much more area and achieve better target position and direction flexibility. This allows visual sensors to be widely welcomed in some applications that are only available on the photoelectric sensor. Traditionally, these applications require expensive accessories, as well as accurate motion controls that ensure that the target object is always in the same location and posture.

    vision sensor.jpg

    vision sensor

    The visual sensor provides unparalleled flexibility for the application. For example, switching of production processes (switched from a single bag into an ice cream bucket) may only take only a few seconds and can be done remotely. Additional inspection conditions can be easily added to this app.

    Applications of machine vision and visual sensor

    As long as you need to identify, feature judgment, and detection of objects, machine vision can be drawn. Today, in the fields of agriculture, industry, medicine, machine vision technologies have been widely used due to their non-contact, fast speed, high precision, and strong on-site anti-interference ability.

    For example, in agricultural production, some work is to judge the appearance of crops or agricultural products, such as fruit quality detection, fruit maturity discrimination, crop growth conditions, and weed identification. These prior relying on people's visual identification and judgment in the past. But now they can be replaced by machine visual technology, thereby achieving agricultural automation and intelligence. For example, an intelligent grading production line that can be dynamically and real-time detection of apple quality. On the production line, the three cameras uniformly collect Apple surface information at once, and the acquisition information is combined through the computer intelligent control system to grade Apple. However, some experts say that due to complex and variability, and non-structural properties of the farmland environment, the application of machine vision in agricultural production is not yet mature, still needs further improvement.

    Machine vision production line.jpg

    Machine vision production line

    In industrial environments, machine visual applications are mature and play a major role in improving industrial production flexibility and automation. In addition, in the case where the dangerous working environment or artificial vision is difficult to meet the requirements, the machine vision is used to replace the safety of the operation. Checking product identification techniques on the pipeline, the image recognition system of the label printing quality, the image recognition system of the board welding quality defect is applied to the engineering area for successful examples of the machine vision system. Print packaging, automotive industry, semiconductor materials, food production, etc. are all application directions of machine vision in the industrial sector.

    In the process of exploration and collection, colored smelting, machine vision technology is also available. The mineralization is an important part of mineral resource processing, and the high and low mineralization level directly affects mineral resources recycling. In recent years, mineral surface characteristic monitoring technology based on machine vision has caused high concern in research institutions in industrial development. According to the data, the EU jointly developed a number of universities and companies, launched the "Machine Visual Bubble Structure and Color Characterization" project. South Africa, Chile, and other countries also apply the machine vision to the flotation monitoring of graphite and platinum metal. In China, major progress has also made significant progress on flotation monitoring of coal and nickel.

    Machine vision technology can also be applied to intelligent transportation, security, medical equipment, etc. In the field of the medical field, machine vision can assist doctors in medical imaging, such as X-ray perspective, nuclear magnetic resonance image, CT image, etc. In the field of scientific research, machine vision can be used for material analysis, biological analysis, chemical analysis, and life science analysis, such as automatic classification and counting of blood cells, chromosome analysis, cancer cell identification, etc.

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