A machine vision system uses a machine to replace human eyes for various measurements and judgments. Machine vision is a very important research area in engineering and science. It is a comprehensive discipline that involves multiple fields, such as optics, mechanics, computer science, pattern recognition, image processing, artificial intelligence, signal processing, and optoelectronics integration.
With the development of industrial automation, the ability and application scope of machine vision have gradually improved and become popular. This is due to the rapid development of devices such as mother-son image sensors, CMOS and CCD cameras, DSP, and ARM embedded technology. The technologies of image processing and pattern recognition effectively promote the development of machine vision.
The performance and stability of the machine vision optics device components and the system itself, as well as the influence of the external environment, cannot be ignored for the effectiveness of machine vision detection.
The effectiveness of machine vision detection will be influenced by environmental light, and external light will increase the total light intensity on the tested object and add noise to image data output.
Ordinary optical filters can avoid the influence of environmental light to a certain extent and can change the light information entering the sensor. By using a high-brightness modulated light source, the exposure time and aperture of the sensor can be reduced, and the influence of environmental light can be minimized. Using an infrared camera for measurement can reduce the influence of visible light.
Temperature changes can also affect the effectiveness of machine vision detection. When the camera leaves the factory, it will mark the normal operating temperature range. Most industrial cameras can work between -5℃ and 65℃. Too cold or too hot will affect the normal operation of the camera.
For example, when the exposure time is long, or the ambient temperature is high, the temperature inside the camera will rise, causing dark current to appear in the circuit. It is the main noise source in the image sensor. Studies have shown that the dark current of a CCD chip increases exponentially every 8°C.
On the other hand, the tested object may also be affected by temperature changes. We all know that many objects will expand or contract with heat. Therefore, when measuring such objects, their length and volume will change.
Dust and dirt on the camera will certainly affect the final imaging. For machine vision detection, even small differences are worth noting. Especially when the sensor becomes gray, dark areas will gradually form on the photo.
If the air is too humid, water vapor will adhere to the LED or lens, and imaging will be affected.
Vibration may cause image blurring and distortion. However, most industrial cameras are shockproof. Robots and rail cables can make the camera move well without being affected by vibration.
Changes in power supply voltage will cause instability in the light source and produce noise that changes over time. Thereby affecting the accuracy of machine vision measurement, inspection, and detection.
Electromagnetic interference is an unavoidable interference factor in industrial inspection sites. The startup and stop of motor, transformer, and capacitor devices will cause surge currents, EFT electronic pulse interference, and a large amount of radiation; the movement of industrial equipment will also cause spatial discharge or contact discharge (ESD). The industrial camera belongs to the image sensor and works weakly, so it is particularly affected and needs to be protected by circuit.
The above are the environmental factors that affect the effectiveness of machine vision detection. Under extreme conditions, protective measures may need to be added to machine vision components. Under normal conditions, the industrial environment can directly use industrial cameras.
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