Machine vision system analysis and the effects of shutter stains and scratches
Machine vision technology is an important branch of computer science. It integrates optical, mechanical, electronic, computer software and hardware technologies, including computer, image processing, pattern recognition, artificial intelligence, signal processing, opto-mechatronics, etc. Fields. Since its inception, it has been more than 20 years old. Its functions and applications have been gradually improved and promoted with the development of industrial automation, especially the current digital image sensors, CMOS and CCD cameras, DSP, FPGA, ARM and other embedded. The rapid development of technologies such as technology, image processing and pattern recognition has greatly promoted the development of machine vision.
In short, machine vision is the use of machines instead of the human eye for various measurements and judgments. On the production line, people who make such measurements and judgments will produce errors and errors due to fatigue, personal differences, etc., but the machine will continue to work tirelessly and steadily. In general, machine vision systems include lighting systems, lenses, camera systems, and image processing systems. For each application, we need to consider the speed of the system and the processing speed of the image, whether to use color or black and white cameras, to detect the size of the target or to detect whether the target is defective, how much the field of view needs, how high the resolution needs, how much contrast needs to be Wait. From a functional point of view, a typical machine vision system can be divided into: an image acquisition part, an image processing part, and a motion control part.
The main working process of a complete machine vision system is as follows:
1. The workpiece positioning detector detects that the object has moved to the center of the field of view of the camera system, and sends a trigger pulse to the image acquisition portion.
2. The image acquisition part sends a start pulse to the camera and the illumination system according to the preset program and delay.
3. The camera stops the current scan, restarts a new frame scan, or the camera waits before the start pulse arrives, and initiates a frame scan after the start pulse arrives.
4. The camera opens the exposure mechanism before starting a new frame scan, and the exposure time can be set in advance.
5. Another start pulse turns on the light, and the turn-on time of the light should match the exposure time of the camera.
6. After the camera is exposed, the scanning and output of one frame of image is officially started.
7. The image acquisition part receives the analog video signal and digitizes it by A/D, or directly receives the digital video data digitized by the camera.
8. The image acquisition section stores the digital image in the memory of the processor or computer.
9. The processor processes, analyzes, and identifies the image to obtain measurement results or logic control values.
10. The processing result controls the operation of the pipeline, performs positioning, corrects motion errors, and the like.
As can be seen from the above workflow, machine vision is a relatively complex system. Because most system monitoring objects are moving objects, the matching and coordinated actions of the system and moving objects are particularly important, so strict requirements are imposed on the operating time and processing speed of each part of the system. In some applications, such as robots, flying object guidance, etc., there are strict requirements on the weight, volume and power consumption of the entire system or part of the system.
The advantages of machine vision systems are:
1. Non-contact measurement will not cause any damage to the observer and the observer, thus improving the reliability of the system.
2. It has a wide spectral response range, such as infrared measurement that is invisible to the human eye, which extends the visual range of the human eye.
3, stable work for a long time, it is difficult for humans to observe the same object for a long time, while machine vision can make measurement, analysis and identification tasks for a long time.
The field of application of machine vision systems is becoming more widespread. It has been widely used in industries such as industry, agriculture, national defense, transportation, medical care, finance, and even sports, entertainment, etc. It can be said that it has penetrated into all aspects of our life, production and work.
In specific application environments where machine vision systems are used for inspection, there will be continuous feed applications or intermittent feed applications where the target will stop for a period of time. At this time, it is necessary to know how fast the target can be detected, the number of objects, and the maximum number of detections per minute. These data can be calculated according to the processing speed of the vision system.
Its calculation method is as follows:
Maximum number of detections per minute = 60 (sec.) å¤„ç† Vision system processing speed (sec.)
For example: if the processing speed of the vision system is 20ms,
Then the maximum number of tests per minute = 60sec. ÷ 0.02sec. = 3000TImes/min. (= 50 TImes/sec.)
However, the actual processing speed will vary depending on the camera type and detection settings of the vision system. Although most simple applications can run at 20ms, it is best to test the test with the actual target in a specific application.
If there are certain requirements for the processing speed of the vision system in a specific application, the following calculation methods can be used:
The required processing speed of the vision system (ms) = 1 (sec.) ÷ required number of detections (TImes/sec.) x 1000
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