Intelligent brightness detection method based on digital imaging technology

Abstract

The invention belongs to the technical field of testing, particularly relates to an intelligent detection method for brightness based on a digital imaging technology, and aims to solve the problems that a traditional brightness measuring instrument is large in size, expensive, complex to operate, poor in brightness measuring accuracy and stability and the like. According to the method, digital imaging equipment with adjustable parameters, high resolution and wide dynamic range is selected, an adaptive lens and an optical filter are matched, a response function of the digital imaging equipment is constructed, a conversion relation between a gray value and the apparent brightness is established, and the apparent brightness is measured. The method specifically comprises the steps of setting parameters of digital imaging equipment, building a relation between a shooting standard light source and a target, collecting an image aiming at a target area, converting gray scale space correction and calculating the apparent brightness. In the process, the measurement precision is improved by setting white balance, image preprocessing, multiple times of image fusion shooting and the like. The method is easy and convenient to operate, low in cost, high in adaptability and capable of effectively improving measurement accuracy and stability.

Description

Technical Field

[0001] The invention belongs to the field of testing technology, and in particular relates to an intelligent brightness detection method based on digital imaging technology. Background Art

[0002] Brightness measurement has key applications in many fields. For example, in astronomy, accurate measurement of the brightness of celestial bodies helps to study the properties and evolution of celestial bodies. In lighting engineering, accurate measurement of the brightness of the environment can optimize lighting design, improve visual comfort and energy efficiency. Traditional brightness measurement instruments, such as professional photometers, have high measurement accuracy, but have disadvantages such as large size, high price and complex operation, which limits their widespread application in some scenarios.

[0003] With the rapid development of digital imaging equipment technology, its resolution and dynamic range are constantly improving, making it possible to use digital imaging equipment to measure brightness. However, there are still some problems with the current brightness measurement methods based on digital imaging equipment. On the one hand, the imaging characteristics of digital imaging equipment will be affected by many factors such as lighting conditions and shooting parameters, resulting in poor accuracy and stability of the measurement results. On the other hand, the existing measurement methods are not perfect in data processing and calibration, and it is difficult to meet the needs of high-precision measurement.

[0004] Therefore, there is an urgent need for a brightness measurement method based on a digital imaging device that can improve measurement accuracy and stability and is easy to operate. Summary of the invention

[0005] The present invention proposes an intelligent brightness detection method based on digital imaging technology. The purpose of the present invention is to provide a brightness measurement method that can improve measurement accuracy and stability and is easy to operate.

[0006] The technical solution of the present invention is as follows: A method for intelligent brightness detection based on digital imaging technology specifically comprises the following steps: S1. Select digital imaging equipment with adjustable parameters, high resolution and wide dynamic range, equipped with suitable lenses and filters; S2, using a standard brightness light source to set different brightness levels, fixing the parameters of the digital imaging device to collect images, and fitting to construct a response function of the digital imaging device; S3, photographing a standard target with known brightness in a similar actual environment, processing the image and establishing a conversion relationship between grayscale value and brightness; S4, aim the digital imaging device at the target area, manually adjust the parameters according to the lighting and environment, and collect clear and normally exposed image data; S5, convert the image data into grayscale space, calibrate according to the response function, calculate the target average grayscale value, and substitute it into the relationship to obtain the apparent brightness.

[0007] It should be noted that in S1, a digital imaging device with adjustable parameter function is selected. The digital imaging device has high-resolution imaging and wide dynamic range response capabilities, and can manually set the sensitivity, shutter speed and aperture value. The digital imaging device is equipped with an adaptive optical lens, and a lens with a suitable focal length is selected according to the measurement scene requirements. A filter is installed at the front end of the optical lens. The filter is used to filter light of a specific band to enter the imaging system of the digital imaging device.

[0008] It should be noted that in S2, a standard brightness light source is used, and the brightness of the light source is set to a series of different levels of values ​​L. At each brightness level, the sensitivity, shutter speed, aperture value and other parameters of the digital imaging device are kept constant, and images of the standard brightness light source are collected. The average pixel grayscale value G of the standard brightness light source area is extracted from each image. By analyzing and fitting these brightness values ​​and the corresponding grayscale values, a digital imaging device response function G=f(L) is constructed, where G is the pixel grayscale value and L is the actual brightness of the light source.

[0009] It should be noted that in S3, under conditions similar to the actual measurement environment, multiple standard targets with known brightness V are photographed, the photographed standard target images are processed, and the average grayscale value G of each standard target image is obtained. Through data processing and modeling, a conversion relationship between grayscale value and brightness is established, V=g(G), where V is brightness and G is pixel grayscale value.

[0010] It should be noted that in S4, the prepared digital imaging device system is aimed at the target measurement area, and the sensitivity, shutter speed, and aperture value of the digital imaging device are manually adjusted according to the light intensity and environmental characteristics of the target area, so that the captured image is clear and the exposure is normal, and the image data of the target area is collected.

[0011] It should be noted that in S5, the collected image data is converted from the RGB color space to the grayscale space, and based on the pre-calibrated digital imaging device response function G=f(L), each pixel value in the grayscale image is corrected to obtain a corrected grayscale image, and the average grayscale value G of the target area in the corrected grayscale image is calculated; G is substituted into the established grayscale and brightness conversion relationship V=g(G) to calculate the brightness value V of the target area.

[0012] Wherein, in said S1, the white balance mode of the digital imaging device is set to ensure accurate color reproduction of the image; In said S2, preprocessing is performed on the collected standard brightness light source image, including denoising and image enhancement, so as to improve the accuracy of the response function of the digital imaging device; In S4, the same target area is photographed multiple times and image fusion processing is performed to reduce the influence of image noise on the measurement result.

[0013] The beneficial effects of the present invention are: The present invention constructs a digital imaging device response function and a conversion relationship between grayscale value and brightness, and performs image correction processing, thereby effectively reducing the influence of the imaging characteristics of the digital imaging device on the measurement result and improving the measurement accuracy. Compared with traditional professional measuring instruments, the present invention uses a common digital imaging device as a measuring tool, which is easier to operate, reduces the measurement cost, and improves the generalizability of the measurement method. It can also flexibly adjust the digital imaging device parameters and select a suitable lens according to the requirements of different measurement scenarios to adapt to a variety of measurement environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0015] Figure 1 is a flow chart of an embodiment of the present invention. DETAILED DESCRIPTION

[0016] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] Example 1 like Figure 1 As shown, this embodiment proposes a brightness intelligent detection method based on digital imaging technology, including the following steps: S1. Select digital imaging equipment with adjustable parameters, high resolution and wide dynamic range, equipped with suitable lenses and filters; S2, using a standard brightness light source to set different brightness levels, fixing the parameters of the digital imaging device to collect images, and fitting to construct a response function of the digital imaging device; S3, photographing a standard target with known brightness in a similar actual environment, processing the image and establishing a conversion relationship between grayscale value and brightness; S4, aim the digital imaging device at the target area, manually adjust the parameters according to the lighting and environment, and collect clear and normally exposed image data; S5. Convert the image data to grayscale space, calibrate according to the response function, calculate the target average grayscale value, and substitute it into the relationship to obtain the apparent brightness.

[0018] Among them, in S1, a digital imaging device with adjustable parameter function is selected. The digital imaging device has high-resolution imaging and wide dynamic range response capabilities, and can manually set the sensitivity, shutter speed and aperture value. The digital imaging device is equipped with an adaptive optical lens, and a lens with a suitable focal length is selected according to the measurement scene requirements. A filter is installed at the front end of the optical lens. The filter is used to filter light of a specific band to enter the imaging system of the digital imaging device; In S2, a standard brightness light source is used, and the brightness of the light source is set to a series of different levels of values ​​L. At each brightness level, the sensitivity, shutter speed, aperture value and other parameters of the digital imaging device are kept constant, and images of the standard brightness light source are collected. The average pixel grayscale value G of the standard brightness light source area is extracted from each image. By analyzing and fitting these brightness values ​​and the corresponding grayscale values, a digital imaging device response function G=f(L) is constructed, where G is the pixel grayscale value and L is the actual brightness of the light source; In S3, under conditions similar to the actual measurement environment, multiple standard targets with known brightness V are photographed, and the photographed standard target images are processed to obtain the average grayscale value G of each standard target image. Through data processing and modeling, a conversion relationship between grayscale value and brightness V=g(G) is established, where V is brightness and G is pixel grayscale value; In S4, the prepared digital imaging device system is aimed at the target measurement area, and the sensitivity, shutter speed, and aperture value of the digital imaging device are manually adjusted according to the light intensity and environmental characteristics of the target area, so that the captured image is clear and properly exposed, and image data of the target area is collected; In S5, the collected image data is converted from the RGB color space to the grayscale space, and each pixel value in the grayscale image is corrected based on the pre-calibrated digital imaging device response function G=f(L) to obtain a corrected grayscale image, and the average grayscale value G of the target area in the corrected grayscale image is calculated; G is substituted into the established grayscale and brightness conversion relationship V=g(G) to calculate the brightness value V of the target area; In S1, the white balance mode of the digital imaging device is set to ensure accurate color reproduction of the image; In S2, the collected standard brightness light source image is preprocessed, including denoising and image enhancement, to improve the accuracy of the response function of the digital imaging device; In S4, the same target area is photographed multiple times and image fusion processing is performed to reduce the influence of image noise on the measurement result.

[0019] Specifically, the following operations are included: Select a digital imaging device with manual adjustment of parameters. Its resolution should meet the requirements of the measurement scene and the dynamic range should be wide enough to adapt to different lighting conditions. Select a digital imaging device with a resolution of [X] megapixels and a dynamic range of [X] dB. The digital imaging device can manually set the sensitivity between [ISO range], the shutter speed between [shutter speed range], and the aperture value between [f-number range]; Select an appropriate optical lens based on the distance and viewing angle of the measurement scene. If the measurement scene is a small area at a close distance, select a fixed-focus lens with a focal length of [specific focal length value 1]. If the measurement scene is a large area, select a zoom lens with a [focal length range]. Install a suitable filter at the front end of the optical lens. If the target to be measured emits light of a specific wavelength, select a narrowband filter with a central wavelength of [specific wavelength value] and a bandwidth of [bandwidth value] to filter out light of a specific wavelength band to enter the imaging system of the digital imaging device; At the same time, according to the lighting conditions of the measurement environment, set the white balance mode of the digital imaging device, such as automatic white balance, daylight white balance, cloudy white balance, etc., to ensure accurate image color reproduction.

[0020] Place a standard brightness light source at a suitable location and set its brightness to a series of different levels. These brightness values ​​should cover the brightness range that may be encountered in actual measurements. Fix the sensitivity, shutter speed, aperture value and other parameters of the digital imaging device to a set of appropriate values, with the sensitivity set to ISO [specific ISO value], the shutter speed set to [specific shutter speed value], and the aperture value set to f [specific aperture value]. At each brightness level, use the digital imaging device to collect images of a standard brightness light source; For each collected image, the image processing algorithm is used to extract the average pixel grayscale value G of the standard brightness light source area. The standard brightness light source area can be determined by threshold segmentation, morphological processing and other methods, and then the grayscale average value of all pixels in the area is calculated; By analyzing and fitting these brightness values ​​L and the corresponding grayscale values ​​G, mathematical methods such as linear regression and polynomial fitting can be used to construct the digital imaging device response function G=f(L). During the fitting process, in order to improve the fitting accuracy, the collected standard brightness light source image can be preprocessed, and Gaussian filtering can be used for denoising to remove noise interference in the image. Histogram equalization and other methods can be used for image enhancement to improve the contrast and clarity of the image.

[0021] Under conditions similar to the actual measurement environment, such as the same lighting conditions and background environment, photograph multiple standard targets with known apparent brightness V. The materials and surface characteristics of these standard targets should be representative to cover different types of objects that may be encountered in actual measurement. Process each captured standard target image, and obtain the average grayscale value G of each standard target image by an image processing method similar to that in S2; Through data processing and modeling, multiple linear regression, neural network and other methods can be used to establish the conversion relationship V=g(G) between grayscale value and visual brightness. If the multiple linear regression method is used, it can be assumed that the conversion relationship is V=aG+b. By performing regression analysis on the collected data, the values ​​of coefficients a and b can be determined.

[0022] Aim the prepared digital imaging device system at the target measurement area and observe the light intensity and environmental characteristics of the target area. If the light in the target area is strong, appropriately reduce the sensitivity, aperture value or shutter speed. If the light in the target area is weak, do the opposite. Manually adjust the sensitivity, shutter speed, and aperture value of the digital imaging device, and make the captured image clear and properly exposed by viewing the image effect on the display screen of the digital imaging device in real time. To reduce the impact of image noise on the measurement results, the digital imaging device can be set to shoot the same target area multiple times, shooting [X] times; The image fusion processing of the images obtained by multiple shots can be performed by using a weighted average method to perform a weighted average on the grayscale values ​​of the corresponding pixels in each shot image to obtain the fused image data.

[0023] The collected fused image data is converted from RGB color space to grayscale space. The common conversion formula can be used: G=0.299R+0.587G+0.114B, where R, G, and B are the red, green, and blue component values ​​of the image pixels, respectively, and G is the converted grayscale value. Based on the pre-calibrated digital imaging device response function G=f(L), each pixel value in the grayscale image is corrected. For each pixel grayscale value G in the grayscale image, the corrected grayscale value G=f(G) is calculated by the response function to obtain a corrected grayscale image; Calculate the average grayscale value G of the target area in the corrected grayscale image. The target area can be determined by a method similar to that in S2, and then the corrected grayscale average value of all pixels in the area is calculated; Substitute G into the established grayscale and brightness conversion relationship V=g(G), and finally calculate the brightness value V of the target area.

[0024] Through the above detailed implementation steps, the brightness measurement method based on the digital imaging device of the present invention can accurately and simply measure the brightness of the target area. In practical applications, the parameters and processing methods in each step can be appropriately adjusted and optimized according to specific measurement requirements and scene characteristics to obtain better measurement results.

Claims

1. A method for intelligent brightness detection based on digital imaging technology, characterized in that: The following steps are involved: S1. Select digital imaging equipment with adjustable parameters, high resolution and wide dynamic range, equipped with suitable lenses and filters; S2, using a standard brightness light source to set different brightness levels, fixing the parameters of the digital imaging device to collect images, and fitting to construct a response function of the digital imaging device; S3, photographing a standard target with known brightness in a similar actual environment, processing the image and establishing a conversion relationship between grayscale value and brightness; S4, aim the digital imaging device at the target area, manually adjust the parameters according to the lighting and environment, and collect clear and normally exposed image data; S5. Convert the image data to grayscale space, calibrate according to the response function, calculate the target average grayscale value, and substitute it into the relationship to obtain the apparent brightness.

2. The intelligent brightness detection method based on digital imaging technology according to claim 1 is characterized in that: In S1, a digital imaging device with adjustable parameter function is selected. The digital imaging device has high-resolution imaging and wide dynamic range response capabilities, and can manually set the sensitivity, shutter speed and aperture value. The digital imaging device is equipped with an adaptive optical lens, and a lens with a suitable focal length is selected according to the measurement scene requirements. A filter is installed at the front end of the optical lens. The filter is used to filter light of a specific band to enter the imaging system of the digital imaging device.

3. The intelligent brightness detection method based on digital imaging technology according to claim 2 is characterized in that: In S2, a standard brightness light source is used and the brightness of the light source is set to a series of different levels L. At each brightness level, the sensitivity, shutter speed, aperture value and other parameters of the digital imaging device are kept constant, and images of the standard brightness light source are collected. The average pixel grayscale value G of the standard brightness light source area is extracted from each image. By analyzing and fitting these brightness values ​​and the corresponding grayscale values, a digital imaging device response function G=f(L) is constructed, where G is the pixel grayscale value and L is the actual brightness of the light source.

4. The intelligent brightness detection method based on digital imaging technology according to claim 3 is characterized in that: In S3, under conditions similar to the actual measurement environment, multiple standard targets with known brightness V are photographed, the photographed standard target images are processed, and the average grayscale value G of each standard target image is obtained. Through data processing and modeling, a conversion relationship between grayscale value and brightness is established, V=g(G), where V is brightness and G is pixel grayscale value.

5. The intelligent brightness detection method based on digital imaging technology according to claim 4 is characterized in that: In S4, the prepared digital imaging device system is aimed at the target measurement area, and the sensitivity, shutter speed, and aperture value of the digital imaging device are manually adjusted according to the light intensity and environmental characteristics of the target area to make the captured image clear and properly exposed, and collect image data of the target area.

6. The intelligent brightness detection method based on digital imaging technology according to claim 5 is characterized in that: In S5, the collected image data is converted from the RGB color space to the grayscale space, and each pixel value in the grayscale image is corrected based on the pre-calibrated digital imaging device response function G=f(L) to obtain a corrected grayscale image, and the average grayscale value G of the target area in the corrected grayscale image is calculated; Substitute G into the established grayscale and brightness conversion relationship V = g (G) to calculate the brightness value V of the target area.

7. The intelligent brightness detection method based on digital imaging technology according to claim 6 is characterized in that: In S1, the white balance mode of the digital imaging device is set to ensure accurate color reproduction of the image.

8. The intelligent brightness detection method based on digital imaging technology according to claim 7 is characterized in that: In S2, the collected standard brightness light source image is preprocessed, including denoising and image enhancement, to improve the accuracy of the response function of the digital imaging device.

9. The intelligent brightness detection method based on digital imaging technology according to claim 8 is characterized in that: In S4, the same target area is photographed multiple times and image fusion processing is performed to reduce the influence of image noise on the measurement result.

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