A method for detecting the morphology of tobacco shreds in cigarette sticks

Abstract

A method for detecting the morphology of tobacco shreds in cigarette sticks is characterized by: digitizing the three-dimensional structure of the cigarette stick through industrial CT scanning; obtaining a two-dimensional image of the distribution of tobacco shreds inside the cigarette stick through filtering back projection technology and noise filtering technology; and then obtaining a three-dimensional reconstruction model of the cigarette stick through three-dimensional reconstruction technology and interpolation technology. This model can quantitatively characterize the quality of the tobacco shreds inside the cigarette stick, and then classify and evaluate it according to the requirements of different specifications of cigarettes. The advantages of this invention are: (1) the length and curl of the tobacco shreds in the cigarette stick are detected by the CT scanning three-dimensional reconstruction method, and the results are real and reliable; (2) this method uses mature and reliable industrial technology, which is more efficient and accurate than manual identification methods; (3) the tobacco shred length obtained by this method is an exact value, not a range value, and the result is more accurate; (4) this method is a non-destructive testing method, which can effectively reduce the loss during the testing process and improve economic efficiency.

Description

Technical Field

[0001] This invention relates to the field of cigarette product testing technology, and in particular to a method for detecting the morphology of tobacco shreds inside a cigarette through three-dimensional reconstruction of the cigarette. Background Technology

[0002] The morphology of tobacco shreds in cigarettes is closely related to the combustion process (combustion rate, airflow velocity, combustion cone volume, combustion cone tip tendency, smoke composition, etc.) and the quality of cigarette rolling (physical quality indicators, smoking consistency, etc.). Especially with the rapid increase in the production and sales of slim cigarettes in recent years, the impact of tobacco shred processing morphology on cigarette quality has become more prominent due to the characteristics of slim cigarettes (smaller circumference, longer length, higher draw resistance, higher combustion temperature, etc.). The requirements and control of tobacco shred morphology during processing are more refined and stringent compared to conventional cigarettes. Regarding tobacco shred length, cigarette manufacturers currently mainly use the vibrating sieve method to test tobacco structure indicators. Tobacco structure is one of the important factors affecting cigarette quality. Its testing mainly utilizes sieving to separate tobacco shreds of different sizes, with the result expressed as the proportion of the cumulative mass on each layer or a certain layer of the sieve to the total mass. However, the currently used vibrating sieve method is still time-consuming, and due to sieving issues such as over-sieving and under-sieving, the sieving results do not accurately reflect the size of the tobacco shreds. The width of tobacco shreds is a key process quality indicator in the shredding process. Currently, the industry standard for shredding width on tobacco processing lines ranges from 0.6 to 1.2 mm, with a tolerance of ±0.1 mm. The conventional method for measuring tobacco shred width is the projection method, which involves manually measuring each shred segment using a projector. This method is destructive, labor-intensive, slow, susceptible to human error, and yields unstable results.

[0003] Chinese Patent (202110933900.1) proposes an image processing-based method and system for detecting tobacco quality parameters, specifically focusing on the density and size distribution of tobacco shreds. During image processing, the minimum bounding rectangle of different tobacco shreds is marked by segmenting the connected components of the dispersed shreds. The diagonal length is calculated from the length and width of the rectangle, and corresponding pixel thresholds are set to obtain the size ranges of whole, medium, and broken shreds. The quality index of the tobacco shreds is then evaluated based on comprehensive tobacco performance evaluation indicators. Compared to the method proposed in this application, the two differ primarily in the following ways: First, the technical means employed are different. Chinese Patent (202110933900.1) uses an image processing method, requiring the tobacco shreds to be arranged without overlap. This invention uses a CT three-dimensional reconstruction method, which does not require any further processing of the tobacco. Secondly, the analytical methods differ. Chinese patent (202110933900.1) uses the smallest bounding rectangle for the tobacco shreds and takes the diagonal length of the rectangle as the tobacco shred length, resulting in an approximate value. This invention obtains a two-dimensional image of the tobacco shred distribution inside the cigarette using filtered back-projection technology, and then uses three-dimensional reconstruction technology to obtain a three-dimensional reconstruction model of the cigarette. The reconstruction model yields precise values ​​for the tobacco shred dimensions inside the cigarette. Thirdly, the accuracy differs. Chinese patent (202110933900.1) obtains a two-dimensional image of the tobacco shreds, and the curliness is calculated based on the two-dimensional image. This invention calculates the curliness of the tobacco shreds using a three-dimensional reconstruction model of the cigarette, which can characterize the three-dimensional features of the tobacco shreds and is more consistent with reality.

[0004] In summary, the CT three-dimensional reconstruction method was used to obtain a two-dimensional image of the distribution of tobacco inside the cigarette through filtered back projection technology. Then, a three-dimensional reconstruction model of the cigarette was obtained through three-dimensional reconstruction technology and interpolation technology. Based on the reconstruction model, the morphological values ​​of the tobacco inside the cigarette, such as the length and curl of the tobacco, were obtained, which are still blank. Summary of the Invention

[0005] The purpose of this invention is to provide a method for detecting the quality of tobacco shreds inside cigarettes based on industrial CT three-dimensional reconstruction, which can non-destructively detect the morphology of tobacco shreds inside cigarettes, such as the length and curl of the shreds.

[0006] The objective of this invention is achieved through the following technical solution:

[0007] A method for detecting the morphology of tobacco shreds in cigarette sticks involves digitizing the three-dimensional structure of the cigarette stick through industrial CT scanning, obtaining a two-dimensional image of the tobacco shred distribution inside the cigarette stick using filtering back projection and noise reduction techniques, and then obtaining a three-dimensional reconstruction model of the cigarette stick using three-dimensional reconstruction and interpolation techniques. This model can quantitatively characterize the quality of the tobacco shreds inside the cigarette stick. The specific steps are as follows:

[0008] 1) Place the cigarette to be tested on the stage of the industrial CT equipment, keep it vertical with clamps and ensure that the tobacco part is within the CT scanning range;

[0009] 2) Start the industrial CT equipment to scan the cigarette, and the digital flat panel detector transmits and saves the received signal to the computer;

[0010] 3) The data processing system in the computer uses the filtered back projection algorithm (FDK algorithm) to process the acquired data, and obtains a two-dimensional image of the tobacco distribution state inside the cigarette;

[0011] 4) The image processing system in the computer performs operations such as image matching, image smoothing, and image enhancement on the tomographic two-dimensional CT image sequence for edge extraction and image segmentation; Interpolated images are performed between adjacent two-dimensional CT images to improve the accuracy of the three-dimensional reconstruction model; Finally, the three-dimensional structure model is reconstructed by the image processing system, and the tobacco skeleton contour inside the cigarette is extracted;

[0012] 5) Starting from one end of the tobacco to the other end in units of voxel points, connect the voxel points corresponding in order on the long sides and short sides on both sides of the contour image in turn and identify and locate the intermediate voxel points of this connection line. These intermediate voxel points are combined into a tobacco shape feature line: the width is 1 voxel point, and the length is n (≥5) voxel points. The length of this line is the tobacco length L; [[ID=十二]]

[0013] 6) Create a sufficiently large cube to contain the tobacco shape feature line inside the cube, and then gradually reduce the length, width, and height values of the cube until the tobacco shape feature line has a contact point with each of the six faces of the cube. Record the length, width, and height values of the cube at this time as A, B, and C respectively, and calculate the spatial hypotenuse length D of the cube. The calculation formula is

[0014] [[ID=十六]]

[0015] 7) The tobacco curl degree S is the ratio of the tobacco length L to the distance D between the two farthest points. The calculation formula is as follows:

[0016]

[0017] Combined with Figure 2 , it can be obtained that 0 < S < 1. When S is close to 0, the tobacco shape is more curly; when S is close to 1, the tobacco shape is more straight. The tobacco curl degree is not only related to the gas transfer during cigarette burning, but also related to the filling performance of the tobacco itself. Different types of cigarettes have different requirements for the curl degree of the tobacco. Therefore, to evaluate whether the curl degree of the tobacco in the cigarette meets the expectation, it needs to be analyzed specifically according to the type of cigarette.

[0018] The measured object cigarette can be a slender cigarette, a medium cigarette, or a regular cigarette.

[0019] The cigarette imaging technology adopts CT scanning imaging technology and three-dimensional reconstruction technology.

[0020] The advantages of this invention are: (1) The length and curl of tobacco shreds in cigarettes are detected by CT scanning three-dimensional reconstruction, and the results are real and reliable; (2) This method uses mature and reliable industrial technology, which is more efficient and accurate than manual identification methods; (3) The length and width values ​​of tobacco shreds obtained by this method are precise values, not range values, and the results are more accurate; (4) This method is a non-destructive testing method, which can effectively reduce losses during the testing process and improve economic efficiency. Attached Figure Description

[0021] Figure 1 This is a flowchart of the method for detecting the state of tobacco shreds in a cigarette provided by the present invention.

[0022] Figure 2 This diagram illustrates the calculation of tobacco curl, where the black dots represent the contact points between the tobacco shape feature lines and the six faces of the cube; this diagram serves as an appendix to the abstract.

[0023] Figure 3 A diagram of a cone-beam scanning structure with a planar detector;

[0024] Figure 3 In the diagram: γ is the angle between the ray and the central ray, β is the angle formed by the central ray and the y-axis, called the projection angle, and κ is the cone angle in the Z-axis direction of the cone beam.

[0025] In the figure, the ab coordinate system is the coordinate system on the virtual detector. In this patent, a, b and a(x,y,β) and b(x,y,z,β) are the same, all representing the coordinate information on the virtual detector. Among them, x, y, z and β represent the expression factors of the ab coordinate system in the xyz coordinate system, that is, (a,b) can be converted to (x,y,z) and express the same spatial position information in different coordinate systems. Specific implementation methods

[0026] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0027] 1. Place the cigarette to be tested on the stage of the industrial CT equipment, keep it vertical with clamps and ensure that the tobacco part is within the CT scanning range; the cigarette sample to be tested needs to be placed in the equilibration chamber for temperature and humidity equilibration before the experiment. The specific values ​​are: temperature 22℃, relative humidity 65%, equilibration time of more than 24 hours.

[0028] 2. Start the industrial CT equipment to scan the cigarette. The digital flat panel detector transmits the received signals and saves them to the computer. Before scanning the cigarette, the industrial CT equipment needs to be pre-set with the following parameters: image size of 2048x2048, X-ray source tube voltage of 100kV, X-ray source tube current of 70uA, scanning thickness of 0.004mm, scanning interval of 0.004mm, CT scanning method of cone-beam scanning, and CT scanning mode of Normal scanning.

[0029] 3. The data processing system in the computer uses the Filtered Back Projection (FDK) algorithm to process the acquired data, obtaining a two-dimensional image of the distribution of tobacco shreds inside the cigarette. The FDK algorithm mainly includes several steps: pre-weighting of the projection data, one-dimensional filtering, and back projection.

[0030] (1) First, the projection data is weighted using a function similar to cosine, and the distance and angle difference between the voxel and the source point are appropriately corrected.

[0031] (2) Then, perform one-dimensional filtering in the horizontal direction on the projection data of different projection angles;

[0032] (3) Perform a weighted back projection of the filtered data using a cone-beam scanner. The weight function in the back projection depends on the distance from the reconstructed point to the focal point. See the attached diagram for a cone-beam scanning structure with a planar detector. Figure 3 .

[0033] The FDK reconstruction algorithm for flat panel detectors can be expressed by the following formula:

[0034]

[0035]

[0036] Where R represents the radius of rotation. Let g(a) represent the filtered projection data, g(a) be the filtering function, and U(x, y, β) represent the distance of the reconstructed pixel in the xy plane to the X-ray source. Here, a and b represent the coordinates on the virtual detector.

[0037]

[0038] a(x, y, β) represents the horizontal position of the corresponding ray on the virtual detector, and the corresponding vertical position can be represented as...

[0039]

[0040] The weighted function can be decomposed into the following expression:

[0041]

[0042] Z is the Z-axis coordinate of the point to be reconstructed. Similarly, we can obtain the length of U based on geometric relationships.

[0043] U(x,y,β)=R+xcosβ+ysinβ

[0044] The weighting factor U is similar to the weighting factor in the two-dimensional filtering back projection algorithm. Geometrically, it is related to the projection of the line connecting the ray source point and the reconstructed image onto the intermediate ray.

[0045] 4. The image processing system in the computer performs image matching, smoothing, and enhancement operations on the 2D CT image sequence to facilitate edge extraction and image segmentation; it also interpolates between adjacent 2D CT images to improve the accuracy of the 3D reconstruction model; finally, the image processing system reconstructs the 3D structural model. In CT scan images, the spacing in the X and Y directions is equal during sampling, with a larger difference only in the Z direction. Therefore, linear interpolation is performed in the Z direction. The calculation formula is:

[0046]

[0047] In the formula, P1 and P2 are the CT values ​​of corresponding points in adjacent CT images, a1 and a2 are the Z-axis distances of the two corresponding points from the interpolation point, and P is the CT value of the interpolation point.

[0048] Multifractal spectrum technology is used to extract the internal and contour features of each cigarette CT image. The images are then analyzed using their intuitiveness. The range of spectral values ​​in the multifractal spectrum theory is set, and the edges of the cigarette CT images are detected and extracted. The contour of the three-dimensional image is drawn using the edge information extracted from each cigarette CT image. The original cigarette CT image and the contour image extracted by using linear interpolation are then integrated to form a three-dimensional reconstruction model, and the contour of the tobacco skeleton inside the cigarette is extracted.

[0049] 5. Starting from one end of the tobacco shred and moving towards the other end in units of voxels, connect the corresponding voxels on the long sides of the tobacco shred skeleton image in sequence and identify and locate the intermediate voxel of the connecting line. These intermediate voxels are combined to form a tobacco shred shape feature line: the width is 1 voxel and the length is n (≥5) voxels. The length of this line is the length L of the tobacco shred.

[0050] 6. Industrial CT scanners can scan cigarettes to obtain overall point cloud data, including 3D coordinates X, Y, and Z. This point cloud data can be used to obtain the specific coordinates of voxels. Create a sufficiently large cube to contain the tobacco shape feature lines within it. Then, gradually decrease the cube's length, width, and height until the tobacco shape feature lines have a contact point with all six faces of the cube. Record the cube's length, width, and height values ​​at this point as A, B, and C, respectively, and calculate the length of the cube's hypotenuse, D. The calculation formula is as follows:

[0051]

[0052] 7. The curliness S of tobacco shreds is the ratio of the length L of the tobacco shreds to the distance D between the two farthest points. The calculation formula is as follows:

[0053]

[0054] Combination Figure 2 , can get 0

Claims

1. A method for detecting the morphology of tobacco shreds in a cigarette stick, characterized in that: The three-dimensional structure of cigarettes is digitized using industrial CT scanning. Two-dimensional images of the tobacco distribution inside the cigarette are obtained through filtered back-projection and noise reduction techniques. Then, a three-dimensional reconstruction model of the cigarette is obtained through three-dimensional reconstruction and interpolation techniques. This model can quantitatively characterize the quality of the tobacco inside the cigarette. The specific steps are as follows: 1) Place the cigarette to be tested on the stage of the industrial CT equipment, keep it vertical with clamps and ensure that the tobacco part is within the CT scanning range; 2) Start the industrial CT equipment to scan the cigarette, and the digital flat panel detector will transmit and save the received signal to the computer; 3) The data processing system in the computer uses a filtering back-projection algorithm to process the acquired data. The method involves using the FDK algorithm for filtering and back-projection to obtain a two-dimensional image of the distribution of tobacco shreds inside the cigarette. 4) The computer's image processing system performs image matching, smoothing, and enhancement operations on the two-dimensional CT image sequence to facilitate edge extraction and image segmentation; in adjacent two-dimensional CT images... Image interpolation is performed between images to improve the accuracy of the 3D reconstruction model; finally, the 3D structural model is reconstructed using an image processing system, and the outline of the tobacco skeleton inside the cigarette is extracted. 5) Starting from one end of the tobacco shred and moving towards the other, using voxels as units, connect the corresponding voxels on the long and short sides of the contour image in sequence, and identify and locate the intermediate voxels of this connection line. These intermediate voxels combine to form a tobacco shred shape feature line with a width of 1 voxel. The length of the line is n voxels, where n≥5, and the length of the line is the length L of the tobacco shreds. 6) Create a sufficiently large cube to contain the tobacco-shaped feature line. Then, gradually decrease the cube's length, width, and height until the tobacco-shaped feature line has a contact point with all six faces of the cube. Record the cube's length, width, and height values ​​as A, B, and C, respectively, and calculate the length D of the cube's spatial hypotenuse using the following formula: 7) The curvature S of the tobacco shreds is the ratio of the distance D between the two farthest points to the length L of the tobacco shreds. The calculation formula is as follows:

2. The method for detecting the morphology of tobacco shreds in a cigarette as described in claim 1, characterized in that: The cigarettes to be tested are slim cigarettes, medium-length cigarettes, or regular cigarettes.

3. The method for detecting the morphology of tobacco shreds in a cigarette as described in claim 1, characterized in that: The CT equipment is configured with the following parameters: image size of 2048x2048 for scanning the structure of cigarettes, X-ray source tube voltage of 100kV, X-ray source tube current of 70uA, scanning thickness of 0.004mm, scanning interval of 0.004mm, CT scanning mode of cone-beam scanning, and CT scanning mode of Normal scanning.

4. The method for detecting the morphology of tobacco shreds in a cigarette according to claim 1, characterized in that: In step 1), the cigarette sample to be tested needs to be placed in an equilibration chamber for temperature and humidity equilibration before the experiment. The specific values ​​are: temperature 22℃, relative humidity 65%, and equilibration time of more than 24 hours.

5. The method for detecting the morphology of tobacco shreds in a cigarette according to claim 1, characterized in that: Step 3) of the FDK algorithm mainly includes several steps: pre-weighting of the projected data, one-dimensional filtering, and back projection. (1) First, the projection data is weighted using a function similar to cosine, and the distance and angle difference between the voxel and the source point are appropriately corrected. (2) Then, perform one-dimensional filtering in the horizontal direction on the projection data of different projection angles; (3) Perform a cone-beam weighted back projection on the filtered data. The weight function in the back projection depends on the distance from the reconstructed point to the focal point. The FDK reconstruction algorithm for flat panel detectors can be expressed by the following formula: Where R represents the radius of rotation. This represents the filtered projection data, where g(a) is the filtering function and U(x, y, β) represents the distance of the reconstructed pixel from the X-ray source in the xy plane. a(x, y, β) represents the horizontal position of the corresponding ray on the virtual detector, and the corresponding vertical position is represented as... The weighted function is decomposed and expressed as follows: z is the z-axis coordinate of the point to be reconstructed. Similarly, we obtain the length of U based on geometric relationships. U(x,y,β)=R+xcosβ+ysinβ γ is the angle between the ray and the central ray, β is the angle formed by the central ray and the y-axis, κ is the cone angle in the Z-axis direction of the cone beam, a and b represent the coordinate information on the virtual detector, and x and y are the x-axis and y-axis coordinates of the three-dimensional coordinates of the point to be reconstructed. The weighting factor U is similar to the weighting factor in the two-dimensional filtering back projection algorithm. The weighting factor U is associated with the projection of the line connecting the ray source point and the reconstructed image onto the intermediate ray.

6. The method for detecting the morphology of tobacco shreds in a cigarette according to claim 1, characterized in that: Step 4) is as follows: In the CT scan image, the spacing in the X and Y directions is equal during sampling, while the spacing in the Z direction has a larger difference. Therefore, a linear interpolation is performed in the Z direction, calculated using the following formula: In the formula, P1 and P2 are the CT values ​​of corresponding points in adjacent CT images, a1 and a2 are the Z-axis distances of the two corresponding points from the interpolation point, and P is the CT value of the interpolation point. Multifractal spectrum technology is used to extract the internal and contour features of each cigarette CT image. The images are then analyzed using their intuitiveness. The spectrum value range in the multifractal spectrum theory is set, and the edges of the cigarette CT images are detected and extracted. The contour of the three-dimensional image is drawn using the edge information extracted from each cigarette CT image. The original cigarette CT image and the contour image extracted by using linear interpolation are integrated to form a three-dimensional reconstruction model, and the contour of the tobacco skeleton inside the cigarette is extracted.

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