CN112098415B - Nondestructive testing method for quality of waxberries - Google Patents
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Abstract
The invention discloses a nondestructive testing method for the quality of waxberries. The existing non-destructive testing methods for the quality of waxberries, such as near infrared spectrum detection, X-ray detection, laser-induced fluorescence detection and the like, have higher general cost. The invention uses the industrial camera with the filter with the specific central wave band to shoot the waxberry image, and deduces the reflectivity of the waxberry according to the image and the illumination intensity during shooting, thereby replacing a hyperspectral meter with the industrial camera in the nondestructive testing of the waxberry, and greatly reducing the cost of the nondestructive testing of the waxberry. The invention establishes the relation between the illumination intensity and the white board image gray value, thereby providing a foundation for acquiring the red bayberry reflectivity by using an industrial camera. The invention provides a method for detecting sugar content and acidity of waxberries by utilizing reflectivity in a nondestructive mode, and the size of waxberry fruits is obtained by utilizing a binocular system, so that comprehensive judgment on the quality of the waxberries is realized in multiple dimensions.
Description
Technical Field
The invention belongs to the technical field of agricultural nondestructive testing and detection, and particularly relates to a nondestructive testing method for the quality of waxberries.
Background
The waxberry is a popular fruit, and can be directly eaten or processed into preserved fruit jam and the like. The Zhejiang Xian is named as the first county of Chinese waxberries, the Xian Ju begins to plant the waxberries in the Tang and Song dynasties, and the waxberries are particularly prosperous in the current Xian Ju, so that almost every household can plant the waxberries. At present, the Xianju is the first to China whether the planting scale, the yield, the output value, the commercial processing capacity, the brand effect and the market share are adopted. In 2020, the planting area of the Xianju waxberries reaches 13.8 ten thousand mu, and the yield is nearly 10 ten thousand tons.
The price of the Xianju waxberries is mainly influenced by the quality of the waxberries, the price of the waxberries with better quality can reach dozens of yuan, and the price of the waxberries with poorer quality is only a few dollars, so the sorting of the waxberries is particularly important in the production of the waxberries. The quality of the waxberries is related to the sugar degree, acidity and size of the waxberries besides the variety of the waxberries. The traditional waxberry sorting mainly depends on manual sorting, and the quality of the waxberry is usually identified accurately by extremely experienced people. In the current society, picking workers are gradually reduced, the manpower is insufficient, and a worker with rich experience needs to consume a large amount of materials and time to successfully cultivate the product. In addition, the price of purchasing the non-classified red bayberries from the dealers to the farmers is only a few money per jin, and the classified red bayberries with high quality can be sold to dozens of yuan per jin in the market. If the waxberries can be directly sorted in the hands of the peasant households, the income of the peasant households can be greatly increased. Therefore, a nondestructive device suitable for rapid waxberry product detection of farmers is needed.
The quality of bayberries is mainly sorted from sugar degree, acidity and size. The traditional sugar degree detection method comprises a ketone-sulfuric acid colorimetric method, a sugar meter measurement, a pH meter measurement and a graduated scale measurement. However, the methods are complicated in process and long in time consumption, and the waxberries need to be damaged, so that the requirements of the current production cannot be met. In recent years, some methods for nondestructive detection of fruit quality, such as near infrared spectrum detection, X-ray detection, laser-induced fluorescence detection and the like, have appeared, but the methods generally have higher cost.
Disclosure of Invention
The invention aims to provide a nondestructive testing device and a nondestructive testing method for waxberry quality.
The method comprises the following specific steps:
and 2-1, determining the exposure gain and the exposure time of the image acquisition device.
2-2, establishing an expression of the image gray value relative to the illumination intensity under the central wavelength of 570nm as shown in a formula (2), and establishing an expression of the image gray value relative to the illumination intensity under the central wavelength of 610nm as shown in a formula (3).
Gray 570 ＝0.0013X+16.693 (2)
Gray 610 ＝0.001X+11.159 (3)
In formulae (2) and (3), gray 570 、Gray 610 Respectively representing the gray values of images obtained by image acquisition devices provided with 570nm and 610nm optical filters; x represents the incident light illumination intensity.
Shooting the image of the detected waxberry by using two image acquisition devices which are subjected to binocular calibration, stereo correction and binocular stereo matching, and calculating the depth information of the detected waxberry. And calculating the height and width of the detected waxberry according to the depth information of the waxberry and the calibration plate in the two images, the pixel size of the checkerboard in the waxberry and binocular calibration and the real size of the checkerboard.
And 6, judging the quality of the waxberries according to the sugar content, the pH value, the width and the height of the waxberries by a worker or a computer.
Preferably, in step 2-1, the specific process of determining the exposure gain and the exposure time is as follows: the white board is shot under the condition of maximum ambient light intensity, and the brightness value of the G channel is controlled to be 240-250 by adjusting the exposure gain and the exposure time. Thereby respectively determining the exposure gain and the exposure time of the band-pass filters with the central wave band of 570nm and the central wave band of 610 nm;
preferably, the process established by the formulae (2) and (3) in step 2-2 is as follows:
and acquiring the change condition of the white board gray value under the illumination intensity of 10000-100000 LUX incident light. And (3) taking points within the illumination intensity range of 10000-100000 LUX incident light, and shooting the white board by using two image acquisition devices to obtain the brightness values of the R, G and B channels. The gray scale value converted by the brightness value is shown as formula (1).
Gray＝0.299R+0.587G+0.114B (1)
In the formula (1), gray represents an image Gray value, and R represents a brightness value of an R channel of an image; g represents the brightness value of the G channel of the image; b represents the luminance value of the B channel of the image.
And respectively carrying out linear fitting on the illumination intensity and the corresponding gray value of the images obtained by shooting the two image acquisition devices to respectively obtain linear equations between the illumination intensity and the gray value under the central wave bands of 570nm and 610nm as shown in the formulas (2) and (3).
Preferably, in step 4, the specific process of calculating the sugar content and the pH value of the waxberry fruit is as follows: calculating the relative content of anthocyanin in the detected waxberry
Calculating sugar content C in fructus Myricae Rubrae sugar ＝0.01087C anth +6.284. Calculating the pH value of the waxberry fruit
Preferably, in step 5, the specific procedure of binocular calibration is as follows:
the method comprises the steps of shooting a plurality of standard checkerboard images from different angles by two image acquisition devices, detecting characteristic points in the standard checkerboard images, solving internal parameters and external parameters of a camera under an ideal distortion-free condition, and improving the precision of the internal parameters and the external parameters by using maximum likelihood estimation. And solving the actual radial distortion coefficient by using a least square method, finally integrating the internal parameter, the external parameter and the distortion coefficient, and improving the estimation precision by using a maximum likelihood estimation method to finally obtain the internal parameter, the external parameter and the distortion parameter of the camera. Table 1 shows camera parameters of the left image capturing device, table two shows camera parameters of the right image capturing device, and table 3 shows calibration results of the binocular camera.
Table 1 left image capture device camera parameters
Camera parameters for image capture device on right side of table 2
TABLE 3 binocular Camera calibration results
Preferably, in step 5, the specific process of stereo correction is as follows:
and (5) monocular distortion correction. Specifically, an internal parameter matrix and a distortion parameter in camera calibration are used for carrying out inverse distortion processing on an acquired image. Firstly, the image coordinate system is converted into a camera coordinate system through an internal reference matrix, and the distortion removing operation is carried out under the camera coordinate system. And after the distortion removal operation is finished, converting the camera coordinate system into the pixel coordinate system again, and performing interpolation operation on the pixel points of the new image by using the pixel values of the source image to obtain the image after distortion removal. Binocular parallel correction was performed using the Bouguet epipolar line correction method.
Preferably, in step 5, the binocular stereo matching may be divided into four steps: matching cost calculation, cost aggregation, parallax calculation and parallax optimization.
Preferably, the spectral response range of the industrial camera in the image acquisition device is 350nm-1000nm. The lens of the industrial camera adopts a 6-12mm zoom lens.
Preferably, the optical filters are all band-pass filters with OD3, transmittance of more than 80% and half-height width of 30-50 nm.
Preferably, the type of the illuminometer is ST-85.
The invention has the beneficial effects that:
1. the invention uses the industrial camera with the specific central wave band filter to shoot the waxberry image, and deduces the reflectivity of the waxberry according to the image and the illumination intensity during shooting, thereby replacing a hyperspectral meter with the industrial camera in the nondestructive testing of the waxberry, and greatly reducing the cost of the nondestructive testing of the waxberry.
2. The invention establishes the relation between the illumination intensity and the white board image gray value, thereby providing a foundation for acquiring the red bayberry reflectivity by using an industrial camera.
3. The invention provides a method for detecting sugar content and acidity of waxberries by utilizing reflectivity in a nondestructive mode, and the size of waxberry fruits is obtained by utilizing a binocular system, so that comprehensive judgment on the quality of the waxberries is realized in multiple dimensions.
Drawings
FIG. 1 is a schematic view of a nondestructive testing apparatus for quality of red bayberry according to the present invention;
FIG. 2 is a scatter diagram of the measured values and predicted values of a 570nm filter according to the present invention;
FIG. 3 is a scatter diagram of the measured and predicted values of the 610nm filter according to the present invention;
FIG. 4 is a two-dimensional scattergram of the measured value and the inverted value of the sugar content of waxberry according to the present invention;
FIG. 5 is a two-dimensional scattergram of measured and inverted values of the pH of waxberry according to the present invention;
FIG. 6 is a two-dimensional scattergram of measured and estimated values of waxberry height according to the present invention;
fig. 7 is a two-dimensional scatter diagram of measured and estimated values of the width of red bayberries according to the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
The nondestructive testing device and the testing method for the quality of the waxberries comprise the following specific steps:
And constructing a detection device for the quality of the waxberries without damage. As shown in fig. 1, the detection device includes two image acquisition devices, a light meter 1 and a control computing platform 2. The image acquisition device comprises an industrial camera 3, an optical filter 4 and an industrial camera lens 5. An industrial camera lens is mounted on an industrial camera. And an optical filter is arranged between the industrial camera lens and the industrial camera. The relative positions of the two image acquisition devices are determined and known, forming a binocular recognition system.
The method comprises the steps of controlling two industrial cameras provided with an industrial camera lens and an optical filter to shoot red bayberries from different angles respectively through a control calculation platform to obtain the brightness value of a red bayberry image, controlling a light intensity meter to collect the light intensity at the same moment, calculating the gray value of the red bayberry image (obtaining a gray image) through the control calculation platform by using the brightness value of the red bayberry image, calculating the gray value of incident light by using the light intensity, obtaining the reflectivity of the red bayberries through the gray value of the red bayberries and the gray value of the incident light, substituting the reflectivity of the red bayberries into a sugar degree and acidity calculation formula to obtain the sugar degree and acidity of the red bayberries, and displaying the sugar degree and the acidity. And obtaining a waxberry disparity map by using the two waxberry images obtained by the two cameras, calculating to obtain the size of the waxberries and displaying the size of the waxberries.
The industrial camera is an MV-UBS500-T camera produced by Middyviewtechnology Limited, shenzhen; the MV-UBS500-T camera is a cheap CMOS sensor camera, the spectral response range of which is 350nm-1000nm and comprises a red bayberry response waveband and a mature sensitive waveband. The pixel bit depth is 12bit, and the frame buffer is arranged, so that the simultaneous work of a plurality of cameras is supported, and an SDK packet can be provided for secondary development. The image shot by the industrial camera contains the corresponding characteristic wave band of the waxberry fruit, so that an effective image can be obtained.
The optical filter is positioned between the camera and the lens. The spectral data of the waxberries are processed to know that the central wavelengths corresponding to the sugar degree and the acidity are 610nm and 570nm respectively, and the customized band-pass filter with the central wavelengths of 610nm and 570nm, the Optical Density (OD) of 3, the transmittance of more than 80 percent and the full width at half maximum of 30-50nm is selected as the filter. In order to match the selected CMOS sensor camera, the size of the selected circular threaded optical filter is 20mm in diameter.
The industrial camera lens is Ms-0612 of Midweism, and the working wavelength, the working distance, the focal length, the aperture, the interface and the cost are mainly considered. The sensitive wave band of the sugar acidity is 500 nm-650 nm, which belongs to the visible light range, so the working wavelength of the lens is within the visible light wave band. When shooting, the working distance is relatively short, and a zoom lens of 6-12mm is selected, so that the proper focal length can be adjusted according to the actual condition.
The model of the illuminometer is ST-85. The range of the illuminometer is 0-200000 LUX, and the maximum illumination intensity which can appear under natural light conditions can be perfectly covered. The flat probe can well detect the illumination intensity irradiated on the waxberries and reduce errors due to the fact that the waxberries need to be placed on the flat plate, and can be placed in an ideal place to avoid the influence of shadows on light intensity.
The control calculation platform is responsible for controlling the industrial camera to shoot and controlling the illuminometer to measure the illumination intensity of incident light, and is responsible for calculating the gray value reflectivity and the subsequent waxberry sugar degree acidity.
And 2-1, determining exposure gain and exposure time. In order to make the present invention suitable for different light intensity scenes, the exposure gain and the exposure time of the camera need to be determined. Since the target band is in the green band, the luminance values of the R and B channels are substantially 0, we only need to consider the G channel. The white board is shot under the condition of maximum ambient light intensity, and the brightness value of the G channel is controlled to be 240-250 by adjusting the exposure gain and the exposure time. Respectively determining the exposure gain and the exposure time of the band-pass filters with the central wave band of 570nm and the central wave band of 610 nm; the exposure gain of the band-pass filter with the central wave band of 570nm is 1.625, and the exposure time is 30.5220ms; the exposure gain of the bandpass filter with a 610nm central band is 1.625, and the exposure time is 25.5220ms.
2-2, establishing a relation between the illumination intensity and the gray value.
And acquiring the change condition of the white board gray value under the illumination intensity of 10000-100000 LUX incident light. And (3) taking points within the illumination intensity range of 10000-100000 LUX incident light, and shooting the white board by using two image acquisition devices to obtain the brightness values of the R, G and B channels. The Gray value Gray of the whiteboard under each illumination intensity is calculated by the following formula (1) of converting the brightness value into the Gray value.
Gray＝0.299R+0.587G+0.114B (1)
In the formula (1), gray represents an image Gray value, and R represents a brightness value of an R channel of an image; g represents the brightness value of the G channel of the image; b represents the luminance value of the B channel of the image.
And respectively carrying out linear fitting on the illumination intensity and the corresponding gray value of the images obtained by shooting of the two image acquisition devices to respectively obtain linear equations between the illumination intensity and the gray value under the conditions that the central wave bands are 570nm and 610 nm. The linear equation obtained when the central band is 570nm is shown in the following formula (2), and the coefficient of determination R is 2 Is 0.9792; the linear equation obtained when the central band is 610nm is shown in the following formula (3), and the coefficient of determination R is 2 Is 0.9973:
Gray 570 ＝0.0013X+16.693 (2)
Gray 610 ＝0.001X+11.159 (3)
in formulae (2) and (3), gray 570 Gray, representing the Gray value of the image obtained by an industrial camera fitted with a 570nm filter 610 Gray values of images obtained by an industrial camera equipped with a 610nm filter are indicated, and X represents the incident light illumination intensity.
And 3, obtaining the reflectivity of the detected waxberries under two wave bands by using a waxberry quality nondestructive detection device.
Since the calibration experiment object is a laboratory white board, the reflectivity of the white board can be considered to be 99%, and the reflection gray value of the white board obtained by calibration can also be considered to be almost equal to the incident gray value. Shooting the waxberry to be detected by two image acquisition devices according to the exposure gain and the exposure time determined in the step 2 respectively to obtain RGB images of the waxberry to be detected under 570nm wave band and 610nm wave band respectively; respectively calculating the gray value of each pixel of the RGB image under the two wave bands according to the formula (1), namely the reflection gray value gray out,570 、gray out，610 . The illuminometer collects the illumination intensity X while shooting, and respectively substitutes the formula (2) and the formula (3), and the gray value of the ambient light intensity gray at the 570nm waveband during shooting is calculated in,570 And the gray value of the light intensity of the environment under the 610nm wave band in,610 。
Respectively calculating the red bayberry reflectivity REF under the 570nm and 610nm wave bands 570 、REF 610 As shown in formulas (4) and (5).
5-1. Binocular calibration
A plurality of standard checkerboard images (the actual size of the checkerboard is known) are shot from different angles by two image acquisition devices with filters, characteristic points in the standard checkerboard images are detected, camera internal parameters and external parameters under the ideal distortion-free condition are solved, and the precision of the internal and external parameters is improved by using maximum likelihood estimation. And solving the actual radial distortion coefficient by using a least square method, finally integrating the internal parameter, the external parameter and the distortion coefficient, and improving the estimation precision by using a maximum likelihood estimation method to finally obtain the internal parameter, the external parameter and the distortion parameter of the camera. Table 1 shows camera parameters of the left image capturing device, table two shows camera parameters of the right image capturing device, and table 3 shows calibration results of the binocular camera.
TABLE 1 left Camera parameters
TABLE 2 Right Camera parameters
TABLE 3 binocular Camera calibration results
5-2, stereo correction
And correcting monocular distortion. Specifically, an internal parameter matrix and a distortion parameter in camera calibration are used for carrying out inverse distortion processing on an acquired image. Firstly, converting the image coordinate system into a camera coordinate system through an internal reference matrix, and performing distortion removal operation under the camera coordinate system. And after the distortion removal operation is finished, converting the camera coordinate system into the pixel coordinate system again, and performing interpolation operation on the pixel points of the new image by using the pixel values of the source image to obtain the image after distortion removal. Binocular parallel correction was performed using the Bouguet epipolar rectification method.
5-3. Binocular stereo matching
The binocular stereo matching can be divided into four steps: matching cost calculation, cost aggregation, parallax calculation and parallax optimization. The matching process belongs to the prior art and is not described herein.
5-4, obtaining the size of the waxberry
And acquiring the image of the detected waxberry after being filtered by the optical filter by using two image acquisition devices provided with corresponding optical filters, and calculating to obtain the depth information of the detected waxberry. And calculating the height and width of the detected waxberry according to the depth information of the waxberry and the calibration plate, the pixel size of the waxberry and the checkerboard and the real size of the checkerboard of the calibration plate in the two images.
And 6, judging the quality of the waxberries according to the sugar content, the pH value, the width and the height of the waxberries by a worker or a computer.
The effects of the present invention are demonstrated below with reference to specific examples:
s1, verifying the relation between the gray value of the image and the illumination intensity
The white board is photographed by using the camera provided with the 570nm and 610nm optical filters of the invention, and the brightness values of the three channels of R, G and B are obtained. And calculating the gray value of the white board under each illumination intensity through a psychology formula for converting the brightness value into the gray value to obtain a gray measured value. And calculating to obtain a gray scale predicted value according to the relationship between the image gray scale value and the illumination intensity. The scattering diagram of the measured value and the predicted value of the 570nm filter is shown in fig. 2, and the RMSE of the measured value and the predicted value is 5.255; the scatter plot of the measured and predicted values of the 610nm filter is shown in fig. 3, where the RMSE of the measured and predicted values is 0.9563. The two-dimensional scatter diagram formed by the two diagrams is approximately on the 1. The relationship between the image gray value and the illumination intensity is reliable.
S2, verifying reflectivity inversion sugar acidity after calibration
The sugar content and the pH value of the waxberries are measured, and the reflectivity of 30 waxberries in 570nm and 610nm wave bands is obtained by using the method. Calculating sugar content of the waxberries by using the waxberry sugar degree inversion model and the reflectivity at a 610nm waveband, and calculating the pH value of the waxberries by using the waxberry acidity inversion model and the reflectivity at a 570nm waveband. A two-dimensional scatter diagram of the measured value and the inverted value (i.e., the estimated value) of the content of the myrica rubra is shown in fig. 4, and the RMSE of the two-dimensional scatter diagram is 1.156; the two-dimensional scatter plot of the observed and inverted values of the pH of waxberries is shown in fig. 5, with an RMSE of 0.1812. The two-dimensional scatter diagram formed by the two diagrams is approximately near a 1. The obtained reflectivity value of the invention is reliable.
S3, verifying the size of the waxberry by using a binocular system
The width and height of 30 waxberries are measured by the device of the invention and compared with the actually measured width and height. FIG. 6 shows a comparison graph of measured and estimated values of waxberry height, wherein RMSE is 0.37; the comparison between the measured value and the estimated value of the waxberry width is shown in fig. 7, and the RMSE is 0.27. Both graphs have certain systematic errors, but the errors are small. The size of the waxberry obtained by the invention is reliable.
Claims (9)
1. A nondestructive testing method for the quality of waxberries is characterized by comprising the following steps: step 1, constructing a detection device; the detection device comprises a luminometer and two image acquisition devices; optical filters are arranged at the lens positions of the industrial cameras in the two image acquisition devices; the central wavelengths of the two optical filters are 610nm and 570nm respectively;
step 2, determining parameters during shooting, and establishing a relation between an image gray value and incident light illumination intensity
2-1, determining the exposure gain and the exposure time of the image acquisition device;
2-2, establishing an expression of the gray value of the image under the 570nm central wavelength relative to the illumination intensity as shown in a formula (2), and an expression of the gray value of the image under the 610nm central wavelength relative to the illumination intensity as shown in a formula (3);
Gray 570 ＝0.0013X+16.693 (2)
Gray 610 ＝0.001X+11.159 (3)
in formulae (2) and (3), gray 570 、Gray 610 Respectively representing the gray values of images obtained by image acquisition devices provided with 570nm and 610nm optical filters; x represents the incident light illumination intensity;
step 3, shooting the measured images by two image acquisition devices respectivelyRed bayberry, calculating gray value gray of the image obtained under two wave bands out,570 、gray out，610 (ii) a The illuminometer collects illumination intensity X while shooting, and calculates the gray value of the ambient light intensity at 570nm and 610nm wave bands during shooting in,570 gray in,610 (ii) a Respectively calculating the reflectivity of the red bayberries under the wave bands of 570nm and 610nm
Step 4, according to the red bayberry reflectivity REF under the 570nm wave band 570 Calculating the pH value of the waxberry fruits; according to the reflectivity REF of the waxberry at the 610nm wave band 610 Calculating the sugar content in the waxberry fruits;
the specific process for calculating the sugar content and the pH value of the waxberry fruits comprises the following steps: calculating the relative content of anthocyanin in the waxberry
Calculating sugar content C in fructus Myricae Rubrae sugar ＝0.01087C anth +6.284; calculating the pH value of the waxberry fruit
Step 5, detecting the size of the waxberries
Shooting the image of the detected waxberry by using two image acquisition devices which are subjected to binocular calibration, stereo correction and binocular stereo matching, and calculating the depth information of the detected waxberry; calculating the height and width of the detected waxberry according to the depth information of the waxberry and the calibration plate in the two images, the pixel size of the checkerboard in the waxberry and binocular calibration and the real size of the checkerboard;
and 6, judging the quality of the waxberries according to the sugar content, the pH value, the width and the height of the waxberries by a worker or a computer.
2. The nondestructive testing method for the quality of waxberries, according to claim 1, is characterized in that: the process established by the formula (2) and the formula (3) in the step 2-2 is as follows:
acquiring the change condition of the white board gray value under the illumination intensity of 10000-100000 LUX incident light; taking points within the range of 10000-100000 LUX incident light illumination intensity, and shooting a white board by using two image acquisition devices to obtain the brightness values of three channels of R, G and B; converting the gray value through the brightness value as shown in the formula (1);
Gray＝0.299R+0.587G+0.114B (1)
in the formula (1), gray represents an image Gray value, and R represents a brightness value of an R channel of an image; g represents the brightness value of the G channel of the image; b represents the brightness value of the B channel of the image;
and respectively carrying out linear fitting on the illumination intensity and the corresponding gray value of the images obtained by shooting the two image acquisition devices to respectively obtain linear equations between the illumination intensity and the gray value under the central wave bands of 570nm and 610nm as shown in the formulas (2) and (3).
3. The nondestructive testing method for the quality of waxberries, according to claim 1, is characterized in that: in step 2-1, the specific process of determining the exposure gain and the exposure time is as follows: shooting the white board under the condition of maximum ambient light intensity, and enabling the brightness value of the G channel to be 240-250 by adjusting exposure gain and exposure time; thereby respectively determining the exposure gain and the exposure time of the band-pass filter with the central wave band of 570nm and 610 nm.
4. The nondestructive testing method for the quality of waxberries, according to claim 1, is characterized in that: in step 5, the specific process of binocular calibration is as follows: shooting a plurality of standard checkerboard images from different angles by using two image acquisition devices, detecting characteristic points in the standard checkerboard images, solving internal parameters and external parameters of the camera under an ideal distortion-free condition, and improving the precision of the internal parameters and the external parameters by using maximum likelihood estimation; and solving the actual radial distortion coefficient by using a least square method, finally integrating the internal parameter, the external parameter and the distortion coefficient, and improving the estimation precision by using a maximum likelihood estimation method to finally obtain the internal parameter, the external parameter and the distortion parameter of the camera.
5. The nondestructive testing method for the quality of waxberries according to claim 1, wherein: in step 5, the stereo correction process is as follows: performing inverse distortion processing on the acquired image by using an internal parameter matrix and distortion parameters in camera calibration; firstly, converting an image coordinate system into a camera coordinate system through an internal reference matrix, and performing distortion removal operation under the camera coordinate system; after the distortion removing operation is finished, the camera coordinate system is converted into a pixel coordinate system again, and interpolation operation is carried out on pixel points of a new image by using pixel values of a source image to obtain an image after distortion removing; binocular parallel correction was performed using the Bouguet epipolar rectification method.
6. The nondestructive testing method for the quality of waxberries according to claim 1, wherein: in step 5, binocular stereo matching is divided into four steps: matching cost calculation, cost aggregation, parallax calculation and parallax optimization.
7. The nondestructive testing method for the quality of waxberries, according to claim 1, is characterized in that: the spectral response range of an industrial camera in the image acquisition device is 350nm-1000nm; the lens of the industrial camera adopts a 6-12mm zoom lens.
8. The nondestructive testing method for the quality of waxberries, according to claim 1, is characterized in that: the optical filters are all band-pass filters with OD3, transmittance greater than 80% and half-height width of 30-50 nm.
9. The nondestructive testing method for the quality of waxberries, according to claim 1, is characterized in that: the type of the illuminometer is ST-85.
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