In the field of machine vision, light sources are very important element. They not only have a direct effect on the quality of the images but also have an informed influence on the performance and stability of the entire system. What are the light sources used in machine vision? This article explains the following aspects in detail.

　　1 Image quality improvement

The type and quality of light sources have a direct impact on the clarity and color reproduction of images. In machine vision, white light sources are generally used because they have high brightness, good color rendering, and provide enough light to capture detailed information about objects. By changing the intensity and color temperature of the light source, we can also control the contrast and saturation of the image and thus further improve the quality of the image.

　　2 Improving the stability of the system

　　The selection and configuration of light sources also have an important influence on the stability of image processing systems. For example, if the light source is too strong, it may cause reflection and glare problems, which will affect the clarity of the image; if the light source is too weak, the image may be blurred and cannot accurately capture the information of the object. Therefore, suitable light source types and parameters must be selected on the basis of specific application scenarios and requirements to ensure the stability and reliability of the system.

　　3 Supporting various application scenarios

　　In different application scenarios, we may need to use different types of light sources. For example, in a dark room, we need to use infrared lamps or guided lamps as light sources; in outdoor areas, we need to use fluorescent lamps or xenon lamps, etc.|By flexibly adjusting and switching light sources, we can adapt to different application scenarios and meet the needs of various complex visual tasks.

　　4 Support deep learning algorithms

　　In recent years, deep learning technology has been widely applied in the field of machine vision. Among them, lighting estimation is an important part of deep learning model training. By estimating the illumination intensity of each pixel in the image, we can provide more accurate feature information for deep learning models, thereby improving their performance and generalization ability. Therefore, the important role of light sources in machine vision is also reflected in their support for deep learning algorithms.