Pellet granularity detection system and detection method based on image processing

Abstract

The invention designs a pellet granularity detection system and detection method based on image processing. The pellet granularity detection system comprises a feeding device, a test container device and a detection device, when in use, the total weight m0 of a pellet sample to be detected is firstly weighed, the pellet sample is fed into the pellet granularity detection system from the material guide port and subjected to granularity preliminary screening through the screening transmission mechanism, the pellet sample remaining on the screen falls into the test platform, and the pellet sample remaining on the screen falls into the test platform after the total weight m1 of the pellet sample contained in the test platform is collected by the gas purging device and the weighing sensor. The industrial camera collects an image of the containing area of the test platform and transmits the image to the detection computer for image analysis to obtain a pellet granularity detection result. A new pellet particle size analysis method based on image detection analysis is introduced, pellet particle size detection is carried out by adopting a method of sampling discretization and segmentation circle interpolation closing in image recognition, the operation method is simple, manpower is saved, and pellet particle size detection can be carried out on pellets more rapidly, effectively and accurately.

Description

Technical Field

[0001] The present invention relates to image processing technology and the technical field of ironmaking pellet intelligent detection, and in particular to a pellet particle size detection system and a detection method based on image processing. Background Art

[0002] Particle size is one of the important parameters that affect the quality of pellets and is related to the roasting effect of pellets. If the particle size is too large, the oxidation roasting effect of the pellets is not good and it is easy to form "green nuclei"; if the particle size is too small, the pellets have a large packing density and poor air permeability, which also affects the roasting effect of the pellets. The reasonable particle size recognized by the industry is 8mm-16mm. Therefore, accurate measurement of pellet particle size and its distribution is crucial to pellet production.

[0003] Commonly used particle size detection methods in modern times include sedimentation method, screening method, electric induction method, microscopy method, etc. According to different detection principles, the particle size parameters obtained include average diameter, equivalent volume diameter, equivalent area diameter, equivalent diameter, etc. The representation of particle size distribution is also different, such as the number of particles, percentage, mass percentage, etc. At present, the more popular pellet size detection method in steel mills is mechanical screening method. Usually, the operators of the steel mills use tools to obtain a small amount of pellet particles at the outlet of the disc pelletizing machine, and use a special pellet sieve to screen the sample. Finally, the particle size distribution of the pellets is obtained by manual screening and weighing. There are some problems with this method, including that the particle size distribution data is not accurate enough, the particle size distribution needs to be calculated manually, and the results reflect lags.

[0004] In order to make up for the shortcomings of manual screening methods, more and more studies have begun to use image processing technology. Bai Zhicheng et al. used the collected pellet particle images and used pre-processing schemes such as filtering, thresholding, enhancement, and optimization to solve the problems of high image noise and dark colors. They then proposed an optimized watershed segmentation algorithm based on adaptive labeling, which realized the image segmentation of pellets and obtained the statistical results of the particle size distribution of pellets, providing convenience for pellet production.

[0005] Although the above method can realize contactless pellet size detection, it lacks an effective solution for images with low signal-to-noise ratio, low contrast, uneven particle size and severe accumulation, and cannot effectively segment them correctly. In addition, the "center point + long axis" method is also used to calibrate the particle size during the image recognition process, and the recognition and distinction of overlapping images are insufficient.

[0006] Therefore, how to detect the particle size of pellets more quickly, effectively and accurately has become a research topic. Summary of the invention

[0007] In view of the above-mentioned deficiencies in the prior art, the present invention provides a pellet size detection system and method based on image processing, which has a simple operation method, saves manpower, and can detect the pellet size of pellet ore more quickly, effectively and accurately.

[0008] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0009] In a first aspect, the present invention provides a pellet size detection system based on image processing, such as Figure 1 As shown, it includes a feeding device, a test container device and a detection device;

[0010] The feed device comprises a feed inlet 2 and a screening transmission mechanism 3 for preliminary screening of the pellet sample, and the feed inlet 2 is connected to the test container device through the screening transmission mechanism 3;

[0011] The test container device comprises a test platform 4, which is a platform-shaped container with a horizontal bottom plate, and is used to hold the pellet sample introduced through the material guide port 2 and the screening and transmission mechanism 3; a gas purge device 6 capable of blowing gas into the test platform 4 is provided on the outer side of the four sides of the test platform 4, and a weighing sensor 5 is installed at the bottom of the test platform 4, which is used to weigh the total weight of the pellet sample held in the test platform 4;

[0012] The detection device includes a detection computer 8 and an industrial camera 7 mounted directly above the test platform 4; the industrial camera 7 is used to collect images of the storage area of ​​the test platform 4; the data receiving end of the detection computer 8 is connected to the image data output end of the industrial camera 7 and the weight data output end of the weighing sensor 5 for data transmission, and the detection computer 8 is used to perform image analysis on the storage area image collected by the industrial camera 7, and in combination with the total weight of the pellet sample collected by the weighing sensor 5, to obtain the pellet particle size detection result of the pellet sample placed in the test platform 4.

[0013] In the above-mentioned pellet particle size detection system based on image processing, as a preferred solution, the screening transmission mechanism 3 includes a screening conveyor belt formed by a plurality of transversely arranged screening rollers arranged in an inclined manner, and a conveyor cover with enclosures arranged on both sides and above the screening conveyor belt, and the interval distance between two adjacent screening rollers in the screening conveyor belt is 3mm-5.5mm.

[0014] In the above-mentioned pellet particle size detection system based on image processing, as a preferred solution, the color of the horizontal bottom plate of the test platform 4 has a color difference with the color of the pellet sample.

[0015] In the above-mentioned pellet particle size detection system based on image processing, as a preferred solution, the horizontal bottom plate of the test platform 4 is surrounded by side walls for preventing the pellet samples from falling, and a plurality of ventilation gaps are arranged in a grid shape on the side walls, and the gap spacing distance is 3mm-5.5mm, and the gas purge device 6 can spray gas into the test platform 4 through the ventilation gaps on the side walls.

[0016] In the pellet size detection system based on image processing, as a preferred solution, the detection computer 8 processes the pellet size detection result of the pellet sample placed in the test platform 4 in the following process:

[0017] S1. Obtain the total weight m of the pellet sample to be tested. 0 , and the corresponding relationship between the actual space size and the pixel size corresponding to the image of the holding area, and save the data;

[0018] S2. Binarization is performed on the collected image of the holding area using a pixel threshold method, where the pixel threshold is set between the color pixel value of the pellet sample and the color pixel value of the horizontal bottom plate to obtain a binary image;

[0019] S3, using a watershed segmentation algorithm to perform discretization segmentation processing on the obtained binary image of the pellet sample area, and performing edge closing processing on the pellet sample area edge of the segmented area to obtain a pellet sample segmentation image containing the segmentation results of each pellet sample area;

[0020] S4. Count the total number n of pellet sample regions contained in the pellet sample segmentation image 0 , and calculate the geometric center pixel position and pixel radius of each pellet sample area respectively, combine the corresponding relationship between the actual space size and pixel size corresponding to the holding area image, calculate and determine the particle size of each pellet sample area, and count the number of pellet sample areas n that meet the particle size requirements 1 ;

[0021] S5, the total weight m of the pellet sample contained in the test platform 4 collected by the weighing sensor 5 1 , calculate the qualified pellet ratio β according to the following formula 1 and the proportion of unqualified pellets β 2 , and output as the pellet size test result:

[0022]

[0023] In the above-mentioned pellet particle size detection system based on image processing, as a preferred solution, in step S3, the edge closure processing of the edge of the segmented area is specifically carried out in the following manner: using Lagrange interpolation method, Spline interpolation method, Cubic interpolation method or polynomial interpolation method, the edge of each pellet sample area in the segmented area is subjected to difference processing to form a closed edge segmentation result of each pellet sample area.

[0024] In the above-mentioned pellet size detection system based on image processing, as a preferred solution, the step S4 is specifically as follows:

[0025] S401, counting the total number n of pellet sample regions contained in the pellet sample segmentation image 0 ;

[0026] S402, for any i-th pellet sample region in the pellet sample segmentation image, calculate the pixel area A of the pellet sample region i and the contour perimeter P i , and determine the shape coefficient C of the i-th pellet sample area i =4πA i / P i 2 ; If the shape coefficient C i <0.5, then ignore the pellet sample area; if the shape coefficient C i ≥0.5, then calculate and determine the pixel position coordinates of the geometric center of the i-th pellet sample area Go to step S403;

[0027] S403: Count the coordinates of the outline pixel points of the i-th pellet sample area relative to the geometric center pixel. The pixel distance of the i-th pellet sample area is determined by Mean i , combined with the correspondence between the actual space size and pixel size corresponding to the image of the holding area, the particle size G of the i-th pellet sample area is calculated i :

[0028] G i =2×Mean i ×d max / n max ;

[0029] Among them, d max / n max Indicates the correspondence ratio between the actual space size and the pixel size corresponding to the image of the holding area, d max is the actual horizontal spatial size of the image of the storage area captured by the industrial camera, n max The total number of horizontal pixels of the storage area image collected by the industrial camera;

[0030] S404, using the processing of steps S402 to S403 to respectively calculate and determine the particle size of each pellet sample area, and then count the number of pellet sample areas n that meet the particle size requirements 1 .

[0031] In the pellet size detection system based on image processing, as a preferred solution, in step S402, the geometric center pixel position coordinates of the i-th pellet sample area are calculated and determined. The specific method is:

[0032] Count the pixel coordinates of each contour of the i-th pellet sample area K represents the number of pixels contained in the contour of the i-th pellet sample area, and then the geometric center pixel position coordinates of the i-th pellet sample area are calculated as follows:

[0033]

[0034] In the pellet size detection system based on image processing, as a preferred solution, in step S403, the pixel radius Mean of the i-th pellet sample area is determined. i The specific method is:

[0035] First, count the contour pixel points of the i-th pellet sample area relative to the geometric center pixel coordinates The pixel distance L 1 ,L 2 ,…,L k ,…,L K , K represents the number of pixels contained in the contour of the i-th pellet sample area, where:

[0036]

[0037] Then, determine the shape coefficient C of the i-th pellet sample area i The value size of ;

[0038] If the shape factor 0.8 ≤ C i ≤1, the pixel radius of the i-th pellet sample area is calculated as follows:

[0039]

[0040] Among them, L K is the mean pixel distance of each contour pixel point of the i-th pellet sample area relative to the pixel coordinate of the geometric center;

[0041] If the shape factor 0.5 ≤ C i<0.8, then the coordinates of the outline pixels of the i-th pellet sample area relative to the geometric center pixel are counted. The pixel distance is greater than or equal to The pixel distance value is marked as L 1 ′,L 2 ′,…,L k ′,…,L′ N , N means the statistical value is greater than or equal to The number of pixel distance values ​​is then calculated as follows: i :

[0042]

[0043] In a second aspect, the present invention further provides a pellet size detection method based on image processing, which uses the above-mentioned pellet size detection system to perform detection, and specifically includes the following steps:

[0044] Step A, first weigh the total weight of the pellet sample to be tested m 0 , input to the detection computer 8 to save the data; send all the pellet samples to be tested from the material guide port 2 into the pellet size detection system, and after preliminary particle size screening by the screening transmission mechanism 3, the remaining pellet samples fall into the test platform 4, and start the gas purge device 6 to spray gas into the test platform 4, so that the pellet samples contained in the test platform 4 are evenly distributed, and then stop the operation of the gas purge device 6;

[0045] Step B: The total weight m of the pellet sample contained in the test platform 4 collected by the weighing sensor 5 1 , input to the detection computer 8 to save the data; then, the industrial camera 7 collects the image of the holding area of ​​the test platform 4 and transmits it to the detection computer 8;

[0046] Step C: the detection computer 8 performs image analysis on the image of the containing area captured by the industrial camera 7, and combines the total weight m of the pellet sample collected by the weighing sensor 5. 1 , and obtain the pellet size test result of the pellet sample placed in the test platform 4.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] The pellet particle size detection system and detection method of the present invention overcome the disadvantage of traditional detection methods that the raw material performance is destroyed, and avoid the problem that the existing image analysis method cannot effectively solve the particle size measurement of the overlapping part of the pellets. A new pellet size analysis method based on image detection and analysis is introduced, and the pellet size detection is performed by using the method of sample discretization and segmentation circle interpolation closure in image recognition. The operation method is simple, manpower is saved, and the pellet size detection of the pellet ore can be performed more quickly, effectively and accurately. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to make the purpose, technical solution and advantages of the invention more clear, the present invention will be further described in detail below with reference to the accompanying drawings, in which:

[0050] Figure 1 It is a schematic diagram of the framework of the pellet size detection system based on image processing of the present invention.

[0051] Figure 2 It is a schematic diagram of a top view of a test platform in the pellet size detection system of the present invention.

[0052] Figure 3 It is a process diagram of Example 1 of the present invention.

[0053] Figure 4 This is a result diagram of Example 1 of the present invention.

[0054] Figure 5 This is a process diagram of Example 2 of the present invention.

[0055] Figure 6 This is a result diagram of Example 2 of the present invention.

[0056] Figure 7 This is a process diagram of Example 3 of the present invention.

[0057] Figure 8 It is a schematic diagram of the process of performing edge closing treatment on the edge of the No. 587 pellet sample area in Example 3 of the present invention.

[0058] In the figure: 1- pellet sample; 2- material guide port; 3- screening and transmission mechanism; 4- testing platform; 5- weighing sensor; 6- gas purge device; 7- industrial camera; 8- detection computer. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but only represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the scope of protection of the present invention.

[0060] In the description of the present invention, it is to be understood that the terms “center”, “longitudinal”, “lateral”, “length”, “width”, “thickness”, “up”, “down”, “left”, “right”, “vertical”, “horizontal”, “top”, “bottom”, “inside”, “outside”, “axial”, “radial”, “circumferential”, etc., indicating orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present invention.

[0061] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected" and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a direct connection, or an indirect connection through an intermediate medium, or it can be the internal communication of two components.

[0062] A person skilled in the art can understand the specific meanings of the above terms in the present invention according to specific circumstances.

[0063] In the description of the present invention, it should be noted that the terms used herein are only for describing specific embodiments, and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0064] Unless otherwise specifically stated, the relative arrangement of the parts and steps described in these embodiments, the numerical expressions and numerical values ​​do not limit the scope of the present invention. At the same time, it should be clear that, for ease of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship. The technology, methods and equipment known to ordinary technicians in the relevant field may not be discussed in detail, but in appropriate cases, the technology, methods and equipment should be regarded as part of the authorization specification. In all examples shown and discussed here, any specific value should be interpreted as merely exemplary, rather than as a limitation. Therefore, other examples of exemplary embodiments may have different values. It should be noted that similar numbers and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further discussed in subsequent drawings.

[0065] In addition, it should be noted that the use of terms such as "first" and "second" to limit components is only for the convenience of distinguishing the corresponding components. If not otherwise stated, the above terms have no special meaning and therefore cannot be understood as limiting the scope of protection of the present invention.

[0066] like Figure 1 As shown, the present invention provides a pellet size detection system based on image processing, which is characterized by comprising a feeding device, a test container device and a detection device.

[0067] The feeding device includes a material guide port 2 and a screening and transmission mechanism 3 for preliminary screening of the pellet sample. The material guide port 2 is connected to the test container device through the screening and transmission mechanism 3.

[0068] The test container device includes a test platform 4, which is a platform-shaped container with a horizontal bottom plate, used to hold the pellet sample introduced through the material guide port 2 and the screening and transmission mechanism 3; a gas purge device 6 capable of blowing gas into the test platform 4 is provided on the outer side of the four sides of the test platform 4, and a weighing sensor 5 is installed at the bottom of the test platform 4, which is used to weigh the total weight of the pellet sample held in the test platform 4.

[0069] The detection device includes a detection computer 8 and an industrial camera 7 mounted directly above the test platform 4; the industrial camera 7 is used to collect images of the storage area of ​​the test platform 4; the data receiving end of the detection computer 8 is connected to the image data output end of the industrial camera 7 and the weight data output end of the weighing sensor 5 for data transmission, and the detection computer 8 is used to perform image analysis on the storage area image collected by the industrial camera 7, and in combination with the total weight of the pellet sample collected by the weighing sensor 5, to obtain the pellet size detection result of the pellet sample placed in the test platform 4.

[0070] The operation flow of using the pellet size detection system of the present invention to perform pellet size detection is as follows:

[0071] Step A, first weigh the total weight of the pellet sample to be tested m 0 , input to the detection computer 8 to save the data; send all the pellet samples to be tested from the material guide port 2 into the pellet size detection system, and after preliminary particle size screening by the screening transmission mechanism 3, the remaining pellet samples fall into the test platform 4, and start the gas purge device 6 to spray gas into the test platform 4, so that the pellet samples contained in the test platform 4 are evenly distributed, and then stop the operation of the gas purge device 6;

[0072] Step B: The total weight m of the pellet sample contained in the test platform 4 collected by the weighing sensor 5 1 , input to the detection computer 8 to save the data; then, the industrial camera 7 collects the image of the holding area of ​​the test platform 4 and transmits it to the detection computer 8;

[0073] Step C: the detection computer 8 performs image analysis on the image of the containing area captured by the industrial camera 7, and combines the total weight m of the pellet sample collected by the weighing sensor 5. 1 , and obtain the pellet size test result of the pellet sample placed in the test platform 4.

[0074] The pellet size detection system of the present invention overcomes the disadvantage of traditional detection methods that the raw material performance is destroyed, and avoids the problem that the existing image analysis method cannot effectively solve the particle size measurement of the overlapping part of the pellets. It introduces a new pellet size analysis method based on image detection and analysis, and adopts the method of sample discretization and segmentation circle interpolation closure in image recognition to perform pellet size detection. The operation method is simple, manpower is saved, and the pellet size detection of pellet ore can be performed more quickly, effectively and accurately.

[0075] In a specific implementation, the screening transmission mechanism 3 includes a screening conveyor belt formed by a plurality of transversely arranged screening rollers arranged in an inclined manner, and a conveyor cover arranged on both sides and above the screening conveyor belt. The screening rollers in the screening conveyor belt are arranged in an inclined manner and are arranged at intervals. The interval between two adjacent screening rollers is the particle size screening size of the pellet sample. For example, the interval between the screening rollers is designed to be 3mm-5.5mm. Its function is to preliminarily screen out some unqualified pellet samples with too small particle size, and reduce the unnecessary interference caused by them entering the test platform 4 to the image detection of the pellet particle size.

[0076] In a specific implementation, the color of the horizontal bottom plate of the test platform 4 has a color difference with the color of the pellet sample. For example, the color of the pellet sample 1 is usually dark gray, black, etc., then the color of the horizontal bottom plate of the test platform 4 can be a color such as white, yellow, green, blue and red that has a significant color difference relative to the pellet sample, which is more conducive to the image detection and recognition effect of the pellet sample and is conducive to improving the recognition accuracy of the pellet sample area. The horizontal bottom plate of the test platform 4 has side walls around it for blocking the pellet sample from falling, ensuring that the pellet sample entering the test platform 4 does not fall out of the holding area of ​​the test platform 4. A plurality of ventilation slits are arranged in a grid-like manner on the side walls around the horizontal bottom plate, and the gap spacing is 3mm-5.5mm. The gas purge device 6 can spray gas into the test platform 4 through the ventilation slits on the side walls. In this way, the gas purge device 6 can achieve a better purge effect by spraying gas into the test platform 4.

[0077] The gas purge device 6 is used to purge the test platform 4 with airflow, so as to make the pellet samples 1 falling into the test platform 4 as evenly distributed as possible and reduce overlap, which is more conducive to accurately identifying the size and particle size of each pellet sample through image processing and reducing the recognition error caused by the overlap of pellet samples. Figure 2 As shown, the gas purging device 6 can be implemented by a plurality of hair dryers distributed around the test platform 4. The gas purging device 6 facing the feeding side of the test platform 4 can be started and opened when the unloading starts, and the gas flow rate is not less than 3 m / s. After the pellet sample is unloaded, the gas nozzle of the gas purging device in the feeding side is closed, and then the gas purging devices 6 in other directions are started, and their nozzles are purged sequentially along the circumferential direction of the test platform 4, or the airflow of each gas purging device 6 can be manually controlled when there is a special need, so as to make the pellet sample 1 in the test platform 4 as evenly distributed as possible.

[0078] In a specific implementation, the image acquisition pixel number of the industrial camera 7 is not less than 2 million to ensure the accuracy of image acquisition.

[0079] In a specific implementation, the detection computer 8 can be implemented by a desktop computer, a mobile terminal computer, or other equipment with data computing and processing capabilities. The detection computer 8 processes the pellet size detection result of the pellet sample placed in the test platform 4 in the following process:

[0080] S1. Obtain the total weight m of the pellet sample to be tested. 0 , and the corresponding relationship between the actual space size and the pixel size corresponding to the image of the holding area, and save the data;

[0081] S2. Binarization is performed on the collected image of the holding area using a pixel threshold method, where the pixel threshold is set between the color pixel value of the pellet sample and the color pixel value of the horizontal bottom plate to obtain a binary image;

[0082] S3, using a watershed segmentation algorithm to perform discretization segmentation processing on the obtained binary image of the pellet sample area, and performing edge closing processing on the pellet sample area edge of the segmented area to obtain a pellet sample segmentation image containing the segmentation results of each pellet sample area;

[0083] S4. Count the total number n of pellet sample regions contained in the pellet sample segmentation image 0 , and calculate the geometric center pixel position and pixel radius of each pellet sample area respectively, combine the corresponding relationship between the actual space size and pixel size corresponding to the holding area image, calculate and determine the particle size of each pellet sample area, and count the number of pellet sample areas n that meet the particle size requirements 1 ;

[0084] S5, the total weight m of the pellet sample contained in the test platform 4 collected by the weighing sensor 5 1 , calculate the qualified pellet ratio β according to the following formula 1 and the proportion of unqualified pellets β 2 , and output as the pellet size test result:

[0085]

[0086] It can be seen that in the pellet size detection process of the present invention, starting from step S2, the problem of difficult particle size detection is gradually solved by image binarization and discretization segmentation processing; at the same time, in view of the problem of rough particle size detection of the segmented image, the present invention adopts edge closure processing to improve the adverse effects caused by the incomplete closure of the edges of the pellet sample area in the discretization segmentation processing; finally, according to the particle size of each pellet sample area determined by calculation, the number of pellet sample areas that meet the particle size requirements is counted, and finally the qualified pellet ratio and the unqualified pellet ratio are obtained. In the specific implementation, the pellet size interval that meets the particle size requirements can be set to 8-16mm.

[0087] In the specific implementation, if the horizontal bottom plate color of the test platform 4 is white or light yellow, in step S2, when the collected holding area image is binarized using the pixel threshold method, the pixel threshold can be set to 80-120, which can achieve a better binarization effect.

[0088] In the specific implementation, in step S3, in the edge closing process of the segmented area, Lagrange interpolation method, Spline interpolation method, Cubic interpolation method or polynomial interpolation method can be used to perform difference processing on the edges of each pellet sample area in the segmented area to form the closed edge segmentation results of each pellet sample area. The edge difference processing using these interpolation methods has a good edge closing enhancement effect.

[0089] In a specific implementation, step S4 is specifically as follows:

[0090] S401, counting the total number n of pellet sample regions contained in the pellet sample segmentation image 0 ;

[0091] S402, for any i-th pellet sample region in the pellet sample segmentation image, calculate the pixel area A of the pellet sample region i and the contour perimeter P i , and determine the shape coefficient C of the i-th pellet sample area i =4πA i / P i 2 ; If the shape coefficient C i <0.5, then ignore the pellet sample area; if the shape coefficient C i ≥0.5, then calculate and determine the pixel position coordinates of the geometric center of the i-th pellet sample area Go to step S403;

[0092] S403: Count the coordinates of the outline pixel points of the i-th pellet sample area relative to the geometric center pixel. The pixel distance of the i-th pellet sample area is determined by Mean i , combined with the correspondence between the actual space size and pixel size corresponding to the image of the holding area, the particle size G of the i-th pellet sample area is calculated i :

[0093] G i =2×Mean i ×d max / n max ;

[0094] Among them, d max / n max Indicates the correspondence ratio between the actual space size and the pixel size corresponding to the image of the holding area, d max is the actual horizontal spatial size of the image of the storage area captured by the industrial camera, n max The total number of horizontal pixels of the storage area image collected by the industrial camera;

[0095] S404, using the processing of steps S402 to S403 to respectively calculate and determine the particle size of each pellet sample area, and then count the number of pellet sample areas n that meet the particle size requirements 1 .

[0096] In the above process, the statistical shape coefficient C is used. i The shape factor C i The pellet sample area with a value <0.5 is considered to be a closed area with an irregular shape or a large part of the area being blocked. These areas are excluded and only the shape coefficient C is considered to be i The pellet sample area with a particle size of ≥0.5 is used for particle size detection and calculation. This processing method also reduces the interference of misidentification and helps improve the accuracy of the final pellet particle size detection.

[0097] In the specific implementation, in step S402, the geometric center pixel position coordinates of the i-th pellet sample area are calculated and determined. The specific method is:

[0098] Count the pixel coordinates of each contour of the i-th pellet sample area K represents the number of pixels contained in the contour of the i-th pellet sample area, and then the geometric center pixel position coordinates of the i-th pellet sample area are calculated as follows:

[0099]

[0100] In step S403, the pixel radius Mean of the i-th pellet sample area is determined. i The specific method is:

[0101] First, count the contour pixel points of the i-th pellet sample area relative to the geometric center pixel coordinates The pixel distance L 1 ,L 2 ,…,L k ,…,L K , K represents the number of pixels contained in the contour of the i-th pellet sample area, where:

[0102]

[0103] Then, determine the shape coefficient C of the i-th pellet sample area i The value size of ;

[0104] If the shape factor 0.8 ≤ C i ≤1, the pixel radius of the i-th pellet sample area is calculated as follows:

[0105]

[0106] Among them, L K is the mean pixel distance of each contour pixel point of the i-th pellet sample area relative to the pixel coordinate of the geometric center;

[0107] If the shape factor 0.5 ≤ C i <0.8, then the coordinates of the outline pixels of the i-th pellet sample area relative to the geometric center pixel are counted. The pixel distance is greater than or equal to The pixel distance value is marked as L 1 ′,L 2 ′,…,L k ′,…,L′ N , N means the statistical value is greater than or equal to The number of pixel distance values ​​is then calculated as follows: i :

[0108]

[0109] In the above process, according to the shape coefficient C i The value of C is divided into different radius calculation methods, taking into account: if the shape coefficient is 0.8≤C i ≤1, the pellet sample area is considered to be a relatively complete, overall circular area, and the radius is directly calculated by the mean of all pixel distances to determine the pellet size; if the shape coefficient is 0.5≤C i <0.8, it means that the pellet sample area may be partially blocked and not very complete, or the area shape is elliptical, so the radius is calculated based on the average distance of the pixels with larger distance values ​​to determine the pellet size; this method can more realistically reflect the particle size of each pellet area.

[0110] Below, the present invention is further described by examples.

[0111] Example:

[0112] First, the total weight m 0 The pellet sample is put into the pellet size detection system through the material guide port and enters the test platform through the screening transmission mechanism. Then, after being weighed by the weighing sensor at the bottom of the test platform and purged by the gas purging device around it (gas flow rate 5-8m / s), the industrial camera (2 million pixels) above it is used to collect the image of the holding area of ​​the test platform. Finally, the collected image is processed and analyzed by the detection computer to obtain the pellet size detection result, and the proportion of qualified pellets and the proportion of unqualified pellets are given.

[0113] [Example 1]

[0114] In this embodiment, the total weight of the pellet sample of dispersed sample 1 is m 0 =2000g, the total weight of the pellet sample placed in the test platform collected by the weighing sensor m 1 =1985g, the pixel threshold in image analysis is set to 100, and the total number of pellet sample areas identified is n 0 = 42, after testing, the number of pellet sample areas that meet the particle size requirements is n 1 =32.

[0115] The image detection process of this embodiment is as follows Figure 3 As shown in Figure 2, the pellet size test results are as follows: Figure 4 As shown, the output is:

[0116]

[0117] [Example 2]

[0118] In this embodiment, the total mass of the pellets of the dispersed sample 2 is m o =2000g, test sample mass m 1 =1993g, the threshold value was set to 120 in image analysis;

[0119] In this embodiment, the total weight of the pellet sample of dispersed sample 2 is m 0 =2000g, the total weight of the pellet sample placed in the test platform collected by the weighing sensor m 1 =1993g, the pixel threshold in image analysis is set to 100, and the total number of pellet sample areas identified is n 0 = 24, after testing, the number of pellet sample areas that meet the particle size requirements is n 1 =22.

[0120] The image detection process of this embodiment is as follows Figure 5 As shown in Figure 2, the pellet size test results are as follows: Figure 6 As shown, the output is:

[0121]

[0122] [Example 3]

[0123] (Overlapping sample) Total mass of pellets m o =10000g, test sample mass m 1=9888g, the threshold in image analysis is set to 120; the total number of edge lines is 2470, of which, taking the 587th edge line as an example, the number of recognition points is 1992, the number of valid points is 1246, the number after interpolation is 3601, and the interpolation method is "cubic". The original data is interpolated after radius / angle conversion. The property coefficient of the closed segmentation figure is 0.632. After calculation, the radius of the segmented image is 234 without interpolation processing, and the radius of the segmented image after interpolation processing is 186.

[0124] In this embodiment, the total weight of the pellet sample of the overlapping sample is m 0 =10000g, the total weight of the pellet sample placed in the test platform collected by the weighing sensor m 1 = 8534g, the pixel threshold in image analysis is set to 120, and the total number of pellet sample areas identified is n 0 = 127; among them, taking the edge recognition of the pellet sample area No. 587 as an example, Figure 7 As shown in the figure, the original number of valid edge pixels is 1246, and the number of edge pixels after interpolation is 3601. The interpolation method uses the Cubic interpolation method for difference processing. The process of edge closure processing by difference is as follows: Figure 8 As shown, the shape coefficient C of the pellet sample area after closed edge segmentation is calculated. i is 0.632; finally, the number of pellet sample areas n that meet the particle size requirements is detected 1 =111.

[0125] The output result of the pellet size detection in this embodiment is:

[0126]

[0127] It can be seen that the pellet size detection system and detection method of the present invention overcome the shortcomings of traditional detection methods that damage the performance of raw materials, and avoid the problem that existing image analysis methods cannot effectively solve the problem of measuring the particle size of overlapping parts. The operation method is simple, saves manpower, and can perform pellet size detection on pellet ore more quickly, effectively and accurately.

[0128] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit the technical solution. Those skilled in the art should understand that those modifications or equivalent substitutions of the technical solution of the present invention that do not depart from the purpose and scope of the technical solution should be included in the scope of the claims of the present invention.

Claims

1. A pellet size detection system based on image processing, characterized in that: It includes a feeding device, a test container device and a detection device; The feed device comprises a feed inlet (2) and a screening transmission mechanism (3) for preliminary screening of the pellet sample according to particle size, and the feed inlet (2) is connected to the test container device through the screening transmission mechanism (3); The test container device comprises a test platform (4), which is a platform-shaped container with a horizontal bottom plate and is used to hold a pellet sample introduced through a material guide port (2) and a screening and transmission mechanism (3); a gas purge device (6) capable of blowing gas into the test platform (4) is provided on the outer sides of the test platform (4); a weighing sensor (5) is installed at the bottom of the test platform (4) and is used to weigh the total weight of the pellet sample held in the test platform (4); The detection device comprises a detection computer (8) and an industrial camera (7) mounted directly above the test platform (4); the industrial camera (7) is used to collect images of a storage area of ​​the test platform (4); a data receiving end of the detection computer (8) is respectively connected to an image data output end of the industrial camera (7) and a weight data output end of a weighing sensor (5) for data transmission, and the detection computer (8) is used to perform image analysis on the storage area image collected by the industrial camera (7), and in combination with the total weight of the pellet sample collected by the weighing sensor (5), obtain a pellet size detection result of the pellet sample contained in the test platform (4).

2. The pellet size detection system based on image processing according to claim 1, characterized in that: The screening transmission mechanism (3) comprises a screening conveyor belt formed by a plurality of transversely arranged screening rollers arranged in an inclined manner, and a conveyor cover arranged on both sides and above the screening conveyor belt, wherein the spacing between two adjacent screening rollers in the screening conveyor belt is 3 mm-5.5 mm.

3. The pellet size detection system based on image processing according to claim 1, characterized in that: The color of the horizontal bottom plate of the test platform (4) is different from the color of the pellet sample.

4. The pellet size detection system based on image processing according to claim 1, characterized in that: The horizontal bottom plate of the test platform (4) is surrounded by side walls for preventing pellet samples from falling, and the side walls are provided with a plurality of ventilation slits distributed in a grid pattern, with the slit spacing being 3 mm to 5.5 mm. The gas purge device (6) is capable of spraying gas into the test platform (4) through the ventilation slits on the side walls.

5. The pellet size detection system based on image processing according to claim 1, characterized in that: The process of the detection computer (8) obtaining the pellet size detection result of the pellet sample placed in the test platform (4) is as follows: S1, obtaining the total weight m0 of the pellet sample to be tested, and the corresponding relationship between the actual space size and the pixel size corresponding to the image of the containing area, and saving the data; S2. Binarization is performed on the collected image of the holding area using a pixel threshold method, where the pixel threshold is set between the color pixel value of the pellet sample and the color pixel value of the horizontal bottom plate to obtain a binary image; S3, using a watershed segmentation algorithm to perform discretization segmentation processing on the obtained binary image of the pellet sample area, and performing edge closing processing on the pellet sample area edge of the segmented area to obtain a pellet sample segmentation image containing the segmentation results of each pellet sample area; S4, counting the total number n0 of pellet sample areas contained in the pellet sample segmentation image, and respectively calculating the geometric center pixel position and pixel radius of each pellet sample area, combining the corresponding relationship between the actual space size corresponding to the holding area image and the pixel size, calculating and determining the particle size of each pellet sample area, and counting the number n1 of pellet sample areas that meet the particle size requirements; S5. Combined with the total weight m1 of the pellet sample placed in the test platform 4 collected by the weighing sensor 5, the qualified pellet ratio β1 and the unqualified pellet ratio β2 are calculated according to the following formulas, and output as the pellet size detection result:

6. The pellet size detection system based on image processing according to claim 5, characterized in that: In step S3, the edge closure processing of the segmented area is specifically performed by using Lagrange interpolation method, Spline interpolation method, Cubic interpolation method or polynomial interpolation method to perform difference processing on the edge of each pellet sample area in the segmented area to form a closed edge segmentation result of each pellet sample area.

7. The pellet size detection system based on image processing according to claim 5, characterized in that: The step S4 is specifically as follows: S401, counting the total number n0 of pellet sample regions contained in the pellet sample segmentation image; S402, for any i-th pellet sample region in the pellet sample segmentation image, calculate the pixel area A of the pellet sample region i and the contour perimeter P i , and determine the shape coefficient C of the i-th pellet sample area i =4πA i / P i 2 ; If the shape coefficient C i <0.5, then ignore the pellet sample area; if the shape coefficient C i ≥0.5, then calculate and determine the pixel position coordinates of the geometric center of the i-th pellet sample area Go to step S403; S403: Count the pixel coordinates of the contour of the i-th pellet sample area relative to the geometric center pixel The pixel distance of the i-th pellet sample area is then determined by i , combined with the correspondence between the actual space size and pixel size corresponding to the image of the holding area, the particle size G of the i-th pellet sample area is calculated i : G i =2×Mean i ×d max / n max ; Among them, d max / n max Indicates the correspondence ratio between the actual space size and the pixel size corresponding to the image of the holding area, d max is the actual horizontal spatial size of the image of the storage area captured by the industrial camera, n max The total number of horizontal pixels of the storage area image collected by the industrial camera; S404, using the processing of steps S402 to S403 to respectively calculate and determine the particle size of each pellet sample area, and then count the number n1 of pellet sample areas that meet the particle size requirements.

8. The pellet size detection system based on image processing according to claim 7, characterized in that: In step S402, the geometric center pixel position coordinates of the i-th pellet sample area are calculated and determined. The specific method is: Count the pixel coordinates of each contour of the i-th pellet sample area K represents the number of pixels contained in the contour of the i-th pellet sample area, and then the geometric center pixel position coordinates of the i-th pellet sample area are calculated as follows:

9. The pellet size detection system based on image processing according to claim 8, characterized in that: In step S403, the pixel radius Mean of the i-th pellet sample area is determined. i The specific method is: First, count the contour pixel points of the i-th pellet sample area relative to the geometric center pixel coordinates The pixel distances L1, L2, ..., L k ,…,L K , K represents the number of pixels contained in the contour of the i-th pellet sample area, where: Then, determine the shape coefficient C of the i-th pellet sample area i The value size of ; If the shape factor 0.8 ≤ C i ≤1, the pixel radius of the i-th pellet sample area is calculated as follows: in, is the mean pixel distance of each contour pixel point of the i-th pellet sample area relative to the pixel coordinate of the geometric center; If the shape factor 0.5 ≤ C i <0.8, then the coordinates of the outline pixels of the i-th pellet sample area relative to the geometric center pixel are counted. The pixel distance is greater than or equal to The pixel distance values ​​are marked as L1′, L2′, …, L k ′,…,L′ N , N means the statistical value is greater than or equal to The number of pixel distance values ​​is then calculated as follows: i :

10. A pellet size detection method based on image processing, characterized in that: The pellet size detection system according to any one of claims 1 to 9 is used to perform the detection, which specifically comprises the following steps: Step A, first weigh the total weight m0 of the pellet sample to be tested, and input it into the detection computer (8) to save the data; send all the pellet samples to be tested from the material guide port (2) into the pellet size detection system, and after preliminary particle size screening by the screening transmission mechanism (3), the remaining pellet samples fall into the test platform (4), and start the gas purge device (6) to spray gas into the test platform (4), so that the pellet samples contained in the test platform (4) are evenly distributed, and then stop the operation of the gas purge device (6); Step B, the total weight m1 of the pellet sample contained in the test platform (4) is collected by the weighing sensor (5), and is input into the detection computer (8) to save the data; then, an image of the containing area of ​​the test platform (4) is collected by the industrial camera (7) and transmitted to the detection computer (8); Step C, the detection computer (8) performs image analysis on the image of the storage area captured by the industrial camera (7), and combines the total weight m1 of the pellet sample collected by the weighing sensor (5) to obtain the pellet size detection result of the pellet sample contained in the test platform (4).

Images