Intelligent Detection Method of Apparent Brightness Based on Digital Imaging Technology

Abstract

The present invention belongs to the field of testing technology, and specifically relates to an intelligent detection method for visual brightness based on digital imaging technology. Aiming at the problems of large volume, high cost, complex operation of traditional visual brightness measurement instruments, as well as poor accuracy and stability of visual brightness measurement, this method measures visual brightness through steps such as selecting a digital imaging device with adjustable parameters, high resolution and wide dynamic range, matching an adapter lens and a filter, constructing a response function of the digital imaging device, and establishing a conversion relationship between gray values and visual brightness. Specifically, it includes setting parameters of the digital imaging device, photographing a standard light source and establishing a relationship with the target, aligning the target area to acquire images, converting to the gray space for correction and calculating the visual brightness. During the process, the measurement accuracy is also improved by setting white balance, image preprocessing and fusing multiple captured images. This method is easy to operate, low in cost and strong in adaptability, and can effectively improve the measurement accuracy and stability.

Description

Technical Field

[0001] The present invention belongs to the field of testing technology, and particularly relates to a method for intelligent detection of apparent brightness based on digital imaging technology. Background Art

[0002] The measurement of apparent brightness has key applications in many fields. For example, in the field of astronomy, the accurate measurement of the apparent brightness of celestial bodies helps to study the properties and evolution of celestial bodies. In lighting engineering, accurately measuring the apparent brightness of the environment can optimize lighting design, improve visual comfort and energy utilization efficiency. Traditional apparent brightness measurement instruments, such as professional photometers, although having high measurement accuracy, have disadvantages such as large volume, high price, and complex operation, which limit their wide application in some scenarios.

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

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

[0005] The present invention proposes a method for intelligent detection of apparent brightness based on digital imaging technology. The purpose of the present invention is to provide an apparent 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 detection of apparent brightness based on digital imaging technology specifically includes the following steps:

[0007] S1. Select a digital imaging device with adjustable parameters, high resolution and wide dynamic range, match an appropriate lens and install a filter;

[0008] S2. Set different brightness levels with a standard brightness light source, fix the parameters of the digital imaging device to capture images, and fit and construct the response function of the digital imaging device;

[0009] S3. Take a known apparent brightness standard target in a similar actual environment, process the image to establish the conversion relationship between the gray value and the apparent brightness;

[0010] S4. Align the digital imaging device with the target area, manually adjust the parameters according to the lighting and environment, and collect clear and normally exposed image data;

[0011] In S5, the image data is converted to the grayscale space, corrected according to the response function, the average grayscale value of the target is calculated, and the visual brightness is obtained by substituting it into the relationship.

[0012] 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. An appropriate optical lens is equipped for the digital imaging device, 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, and the filter is used to screen light of a specific wavelength band into the imaging system of the digital imaging device.

[0013] 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 level values L. At each brightness level, parameters such as the sensitivity, shutter speed, and aperture value 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 response function G = f(L) of the digital imaging device is constructed, where G is the pixel grayscale value and L is the actual brightness of the light source.

[0014] It should be noted that in S3, under conditions similar to the actual measurement environment, multiple standard targets with known visual brightness V are photographed, the images of the photographed standard targets are processed, the average grayscale value G of each standard target image is obtained, and through data processing and modeling, a conversion relationship V = g(G) between the grayscale value and the visual brightness is established, where V is the visual brightness and G is the pixel grayscale value.

[0015] It should be noted that in S4, the prepared digital imaging device system is aligned with the target measurement area. According to the light intensity and environmental characteristics of the target area, the sensitivity, shutter speed, and aperture value of the digital imaging device are manually adjusted to make the photographed image clear and the exposure normal, and the image data of the target area is collected.

[0016] It should be noted that in S5, the collected image data is converted from the RGB color space to the grayscale space. Based on the pre-calibrated response function G = f(L) of the digital imaging device, 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 through the response function, so as to obtain the 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 conversion relationship g(G') = V' between the grayscale and the visual brightness, and the visual brightness value V' of the target area is calculated.

[0017] Among them, in the step S1, the white balance mode of the digital imaging device is set to ensure accurate color reproduction of the image;

[0018] In the step S2, the collected standard brightness light source image is preprocessed, including denoising processing and image enhancement processing, to improve the accuracy of the response function of the digital imaging device;

[0019] In the step 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.

[0020] The beneficial effects of the present invention are as follows:

[0021] By constructing the response function of the digital imaging device and the conversion relationship between the gray value and the visual brightness, and performing calibration processing on the image, the present invention effectively reduces the influence of the imaging characteristics of the digital imaging device on the measurement result, improves the accuracy of the measurement. Using a common digital imaging device as a measurement tool, compared with traditional professional measurement instruments, the operation is more convenient, the measurement cost is reduced, the popularization of the measurement method is improved, and the parameters of the digital imaging device can be flexibly adjusted and appropriate lenses can be selected according to different measurement scenario requirements to adapt to a variety of measurement environments. Description of the Drawings

[0022] The present invention will be further described in detail below in conjunction with the drawings and specific embodiments.

[0023] Figure 1 It is a flowchart of an embodiment of the present invention. Specific Embodiments

[0024] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of the present invention.

[0025] Embodiment 1

[0026] As Figure 1 shown, this embodiment proposes a visual brightness intelligent detection method based on digital imaging technology, including the following steps:

[0027] S1. Select a digital imaging device with adjustable parameters, high resolution and wide dynamic range, equip it with an appropriate lens and install a filter;

[0028] S2. Set different brightness levels with a standard brightness light source, fix the parameters of the digital imaging device to collect images, and fit and construct the response function of the digital imaging device;

[0029] S3. Take pictures of known visual brightness standard targets in a similar actual environment, process the images, and establish the conversion relationship between gray values and visual brightness;

[0030] S4. Align the digital imaging device with the target area, manually adjust the parameters according to the illumination and environment, and collect clear and normally exposed image data;

[0031] S5. Convert the image data to the gray space, correct it according to the response function, calculate the average gray value of the target, and substitute it into the relationship to obtain the visual brightness.

[0032] Among them, in S1, a digital imaging device with adjustable parameter function is selected. The digital imaging device has the capabilities of high-resolution imaging and wide dynamic range response, and can manually set the sensitivity, shutter speed, and aperture value. An appropriate optical lens is equipped for the digital imaging device, 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, and the filter is used to screen light of a specific wavelength band into the imaging system of the digital imaging device;

[0033] In S2, a standard brightness light source is used, and the light source brightness is set to a series of different level values L. At each brightness level, keep the parameters such as the sensitivity, shutter speed, and aperture value of the digital imaging device constant, collect the images of the standard brightness light source, extract the average pixel gray value G of the standard brightness light source area from each image, and through the analysis and fitting of these brightness values and the corresponding gray values, construct the response function G = f(L) of the digital imaging device, where G is the pixel gray value and L is the actual brightness of the light source;

[0034] In S3, under conditions similar to the actual measurement environment, take pictures of multiple standard targets with known visual brightness V, process the taken standard target images, obtain the average gray value G of each standard target image, and through data processing and modeling, establish the conversion relationship V = g(G) between the gray value and the visual brightness, where V is the visual brightness and G is the pixel gray value;

[0035] In S4, align the prepared digital imaging device system with the target measurement area, manually adjust the sensitivity, shutter speed, and aperture value of the digital imaging device according to the illumination intensity and environmental characteristics of the target area, so that the taken image is clear and normally exposed, and collect the image data of the target area;

[0036] In S5, convert the collected image data from the RGB color space to the gray space, based on the pre-calibrated response function G = f(L) of the digital imaging device, correct each pixel value in the gray image to obtain the corrected gray image, and calculate the average gray value G of the target area in the corrected gray image; Substitute G into the established conversion relationship V = g(G) between the gray and the visual brightness, and calculate the visual brightness value V of the target area;

[0037] In S1, set the white balance mode of the digital imaging device to ensure accurate image color reproduction;

[0038] In S2, preprocess the captured standard brightness light source image, including denoising processing and image enhancement processing, to improve the accuracy of the response function of the digital imaging device;

[0039] In S4, take multiple shots of the same target area and perform image fusion processing to reduce the impact of image noise on the measurement results.

[0040] Specifically, it includes the following operations:

[0041] Select a digital imaging device with the function of manually adjusting parameters. Its resolution should meet the requirements of the measurement scene for details, and the dynamic range should be wide enough to adapt to different lighting conditions. Select a digital imaging device with a resolution of [X] million pixels and a dynamic range of [X] dB. This digital imaging device can manually set the sensitivity between [ISO range], the shutter speed between [shutter speed range], and the aperture value between [f value range];

[0042] According to the distance and viewing angle requirements of the measurement scene, select a suitable optical lens. If the measurement scene is a small area at a short distance, a fixed-focus lens with a focal length of [specific focal length value 1] can be selected. If the measurement scene is a larger area, a zoom lens with a [focal length range] can be selected;

[0043] Install a suitable filter in front of the optical lens. If the measurement target emits light of a specific wavelength, a narrow-band filter with a central wavelength of [specific wavelength value] and a bandwidth of [bandwidth value] can be selected to filter out light in a specific wavelength band and enter the imaging system of the digital imaging device;

[0044] 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.

[0045] Place the standard brightness light source in a suitable position and set its brightness to a series of different level values, and these brightness values should cover the brightness range that may be encountered in actual measurements;

[0046] Fix the parameters such as the sensitivity, shutter speed, and aperture value of the digital imaging device at a set of suitable values. Set the sensitivity to ISO [specific ISO value], the shutter speed to [specific shutter speed value], and the aperture value to f[specific aperture value]. At each brightness level, use the digital imaging device to capture the image of the standard brightness light source;

[0047] For each acquired image, use an image processing algorithm to extract the average pixel gray value G of the standard brightness light source area. The standard brightness light source area can be determined by methods such as threshold segmentation and morphological processing, and then calculate the average gray value of all pixels in this area;

[0048] By analyzing and fitting these brightness values L and the corresponding gray 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, to improve the fitting accuracy, the acquired standard brightness light source images can be preprocessed, Gaussian filtering can be used for denoising to remove noise interference in the images, and methods such as histogram equalization can be used for image enhancement processing to improve the contrast and clarity of the images.

[0049] Under conditions similar to the actual measurement environment, such as the same lighting conditions, background environment, etc., capture multiple standard targets with known apparent brightness V. The materials, surface characteristics, etc. of these standard targets should be representative to cover different types of objects that may be encountered in actual measurements;

[0050] Process each captured standard target image. Through image processing methods similar to those in S2, obtain the average gray value G of each standard target image;

[0051] Through data processing and modeling, methods such as multiple linear regression and neural networks can be used to establish the conversion relationship V = g(G) between the gray value and the apparent brightness. If the multiple linear regression method is used, the conversion relationship can be assumed to be V = aG + b, and by performing regression analysis on the collected data, determine the values of the coefficients a and b.

[0052] Align the prepared digital imaging device system with the target measurement area, observe the light intensity and environmental characteristics of the target area. If the light in the target area is strong, appropriately reduce the sensitivity, decrease the aperture value, or shorten the shutter speed. If the light in the target area is weak, perform the opposite operations;

[0053] Manually adjust the sensitivity, shutter speed, and aperture value of the digital imaging device. By observing the image effect on the display screen of the digital imaging device in real time, make the captured image clear and properly exposed. To reduce the influence of image noise on the measurement result, the digital imaging device can be set to capture the same target area multiple times, [X] times;

[0054] Perform image fusion processing on the images obtained from multiple captures. The weighted average method can be used to perform weighted averaging on the gray values of the corresponding pixels in each captured image to obtain the fused image data.

[0055] Convert the collected and fused image data from the RGB color space to the grayscale space. A 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;

[0056] Based on the digital imaging device response function G = f(L) obtained through pre-calibration, correct each pixel value in the grayscale image. For each pixel grayscale value G in the grayscale image, calculate the corrected grayscale value G’ = h(G) through the response function, thereby obtaining the corrected grayscale image, and calculate the average grayscale value G’ of the target area in the corrected grayscale image; Substitute G’ into the established conversion relationship between grayscale and visual brightness g(G’) = V’ to calculate the visual brightness value V’ of the target area.

[0057] Through the above detailed implementation steps, the visual brightness measurement method based on a digital imaging device of the present invention can accurately and simply measure the visual 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). For each pixel grayscale value G in the grayscale image, the corrected grayscale value G'=h (G) is calculated by the response function, thereby obtaining 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 g(G')= V', and the brightness value V' of the target area is calculated.

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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