An automated detection system for fruit cysts and foreign matter based on AI vision

Abstract

The present invention belongs to the technical field of automatic detection of fruit cyst colored foreign matter, and relates to an automatic detection system for fruit cyst colored foreign matter based on AI vision. The present invention proposes to use a high-definition camera in combination with a microcomputer to perform high-frequency high-definition photography of the surface of the fruit cysts when the fruit cyst raw material barrels are unpacked, collect information, and analyze the information, and then output the detection results of each fruit cyst raw material barrel, and accordingly realize the continued transportation demand, early warning, removal and scrapping of each fruit cyst raw material barrel. On the one hand, it replaces the human eye to discover more and smaller colored foreign matter, and is an effective detection method that reduces the risk of colored foreign matter in cyst unpacking. On the other hand, it avoids the health problems that may be caused by pests and diseases, chemical residues, etc. after ingestion into the human body, ensures the food safety of consumers, ensures the appearance quality of the product, and is conducive to improving consumers' acceptance and satisfaction with the product.

Description

Technical Field

[0001] The invention belongs to the technical field of automatic detection of fruit cyst variegated foreign matter, and relates to an automatic detection system for fruit cyst variegated foreign matter based on AI vision. Background Art

[0002] Fruit cyst foreign bodies refer to abnormal substances that appear inside or on the surface of the fruit cysts and are different from the normal flesh in color, shape, texture, etc. These foreign bodies may be caused by pests and diseases, mechanical damage, physiological changes during growth, or environmental factors. During food processing and consumer purchase, these foreign bodies may affect the quality, taste, and safety of the fruit, so accurate detection and identification are required. Therefore, the detection method for fruit cyst foreign bodies is of great significance.

[0003] Existing methods for detecting foreign matter in fruit cysts cannot identify foreign matter that may be contained in the cysts when the fruit cysts are unpacked after being thawed. They can only rely on simple visual inspection by the human eye, and are even more unable to distinguish the subtle foreign matter and the number of foreign matter in the cysts. Foreign matter in fruit cysts may be caused by pests and diseases, chemical residues, mechanical damage, etc., and may contain bacteria, viruses, pesticide residues or other harmful substances that are harmful to the human body, which may cause food poisoning, allergic reactions or other health problems after ingestion by the human body, causing harm to human health, and thus failing to guarantee the food safety of consumers. In addition, foreign matter in fruit cysts may also affect the appearance quality of the product, reduce consumer acceptance and satisfaction with the product, thereby affecting product sales, and may also damage the company's brand image and reputation. Summary of the invention

[0004] In view of this, in order to solve the problems raised in the above background technology, an automatic detection system for fruit cyst variegated foreign matter based on AI vision is proposed.

[0005] The objective of the present invention can be achieved through the following technical scheme: The present invention provides an AI vision-based automatic detection system for fruit cyst variegated foreign matter, comprising: a camera adjustment demand judgment module, a camera adjustment module, a raw material barrel position detection and analysis module, a variegated color detection and analysis module, a foreign matter detection and analysis module, a detection result output module, a control module and an information storage library.

[0006] The camera adjustment demand judgment module is respectively connected to the camera adjustment module and the raw material barrel position detection and analysis module, the raw material barrel position detection and analysis module is respectively connected to the mottled color detection and analysis module and the foreign matter detection and analysis module, the detection result output module is respectively connected to the mottled color detection and analysis module and the foreign matter detection and analysis module, the detection result output module is connected to the control module, and the information storage library is respectively connected to the raw material barrel position detection and analysis module, the mottled color detection and analysis module, the foreign matter detection and analysis module and the detection result output module.

[0007] The camera adjustment requirement judgment module is used to record the fruit cyst raw material barrel to be inspected at the detection position as the target fruit cyst raw material barrel, and then analyze the position matching between it and the target camera, and judge the adjustment requirement of the target camera.

[0008] The camera adjustment module is used to adjust the target camera when the adjustment requirement of the target camera is required.

[0009] The raw material barrel position detection and analysis module is used to detect the position of each fruit cyst raw material barrel to be detected, analyze its position matching with the target camera, and determine the correction requirements of the conveying position of the fruit cyst raw material barrel to be detected, and further process it.

[0010] The color variegation detection and analysis module is used to obtain the color variegation information parameters of each fruit cyst belonging to each fruit cyst raw material barrel, and analyze the parameters to obtain the color variegation analysis results of each fruit cyst belonging to each fruit cyst raw material barrel.

[0011] The foreign matter detection and analysis module is used to obtain the foreign matter information parameters of each fruit cyst belonging to each fruit cyst raw material barrel, and analyze the foreign matter information parameters to obtain the foreign matter analysis results of each fruit cyst belonging to each fruit cyst raw material barrel.

[0012] The detection result output module is used to output the detection results of each fruit cyst raw material barrel, and analyze the next control requirements of each fruit cyst raw material barrel based on this, where the next control requirements include continued transportation requirements, rejection warning requirements and scrap warning requirements.

[0013] The control module is used to control each fruit cyst raw material barrel according to the next control requirement.

[0014] The information repository is used to store the grayscale value of each pixel corresponding to the grayscale image of each permitted mottled spot, store the comprehensive risk coefficient range corresponding to each risk level, and store the standard delivery position of the fruit cyst raw material barrel to be tested.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention proposes to use a high-definition camera combined with a microcomputer to perform high-frequency high-definition photography of the surface of the fruit cysts when the fruit cyst raw material barrels are unpacked to collect information, analyze it, and then output the detection results of each fruit cyst raw material barrel, and accordingly realize the continued delivery demand, early warning, removal and scrapping of each fruit cyst raw material barrel, replacing the human eye to find more and smaller foreign objects, and reducing the risk of foreign objects in the unpacking of cysts. It is an effective detection method, thereby avoiding food poisoning, allergic reactions or other health problems caused by pests and diseases, chemical residues, mechanical damage, etc. after ingestion by the human body, ensuring the health of the human body and the food safety of consumers. It also guarantees the appearance quality of the product to a certain extent, which is conducive to improving consumers' acceptance and satisfaction with the product. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0017] Figure 1 It is a schematic diagram of system module connection of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] See also Figure 1As shown, the present invention provides an automatic detection system for fruit cyst variegated foreign matter based on AI vision, and the specific modules are distributed as follows: camera adjustment demand judgment module, camera adjustment module, raw material barrel position detection and analysis module, variegated color detection and analysis module, foreign matter detection and analysis module, detection result output module, control module and information storage library. Among them, the connection mode between the modules is: the camera adjustment demand judgment module is respectively connected with the camera adjustment module and the raw material barrel position detection and analysis module, the raw material barrel position detection and analysis module is respectively connected with the variegated color detection and analysis module and the foreign matter detection and analysis module, the detection result output module is respectively connected with the variegated color detection and analysis module and the foreign matter detection and analysis module, the detection result output module is connected with the control module, and the information storage library is respectively connected with the raw material barrel position detection and analysis module, the variegated color detection and analysis module, the foreign matter detection and analysis module and the detection result output module.

[0020] The camera adjustment requirement judgment module is used to record the fruit cyst raw material barrel to be inspected at the detection position as the target fruit cyst raw material barrel, and then analyze the position matching between it and the target camera, and judge the adjustment requirement of the target camera.

[0021] It needs to be further explained that the fruit cyst is the valve cyst of citrus fruit, specifically the orange cyst after the peeling of orange is removed.

[0022] As a specific feasibility example, the position matching situation of the target fruit cyst raw material barrel and the target camera is analyzed, specifically including: taking the center point position of the target fruit cyst raw material barrel as the coordinate origin, drawing a straight line through the coordinate origin on the surface of the target water surface cyst raw material barrel as the horizontal axis, drawing a straight line through the coordinate origin and perpendicular to the horizontal axis as the vertical axis, establishing a two-dimensional coordinate system, and then obtaining the center point coordinates of the target camera in the two-dimensional coordinate system established based on the target fruit cyst raw material barrel, recorded as (x, y), wherein x is the horizontal coordinate of the center point of the target camera, and y is the vertical coordinate of the center point of the target camera.

[0023] If y=0, it means that the position of the target fruit cyst raw material barrel matches the target camera, and thus the adjustment requirement of the target camera is no need for adjustment. If y≠0, it means that the position of the target fruit cyst raw material barrel does not match the target camera, and thus the adjustment requirement of the target camera is need for adjustment.

[0024] The camera adjustment module is used to adjust the target camera when the adjustment requirement of the target camera is required.

[0025] As a specific feasibility example, when the adjustment requirement of the target camera is that an adjustment requirement is required, it is adjusted, and the specific operation includes: adjusting the position of the target camera so that the vertical coordinate of its center point is 0.

[0026] The raw material barrel position detection and analysis module is used to detect the position of each fruit cyst raw material barrel to be detected, analyze its position matching with the target camera, and determine the correction requirements of the conveying position of the fruit cyst raw material barrel to be detected, and further process it.

[0027] As a specific feasibility example, the matching situation of the position of each to-be-detected fruit cyst raw material barrel and the position of the target camera is specifically analyzed in the following manner: the coordinates of the center point of the target camera in the two-dimensional coordinate system established based on the target fruit cyst raw material barrel can be obtained by obtaining the coordinates of the center point of the target camera in the two-dimensional coordinate system established based on each to-be-detected fruit cyst raw material barrel, which are respectively recorded as (x i ′,y i ′), where i = 1, 2, ..., a, i is the number of each barrel of fruit cyst raw material to be tested, a is the number of barrels of fruit cyst raw material to be tested, if y i ′=0, it means that the position of the i-th fruit cyst raw material barrel to be detected matches the position of the target camera. i ′≠0, it means that the position of the i-th fruit cyst raw material barrel to be detected does not match the position of the target camera, and then the number A of the fruit cyst raw material barrels to be detected that match the position of the target camera is obtained by counting. According to the calculation formula The matching rate Mr between the position of the fruit cyst raw material barrel to be detected and the target camera is obtained.

[0028] The matching rate between the position of the fruit cyst raw material barrel to be inspected and the target camera is compared with the set matching rate threshold. If it is greater than or equal to the set matching rate threshold, the correction requirement of the conveying position of the fruit cyst raw material barrel to be inspected is recorded as no correction requirement. Otherwise, the correction requirement of the conveying position of the fruit cyst raw material barrel to be inspected is recorded as a correction requirement, and it is corrected according to the predefined correction principle.

[0029] It should be further explained that the specific content of the predefined correction principle includes: extracting the standard conveying position of the fruit cyst raw material barrel to be tested from the information storage repository, and then correcting it by adjusting the speed or direction of the conveyor belt.

[0030] The color variegation detection and analysis module is used to obtain the color variegation information parameters of each fruit cyst belonging to each fruit cyst raw material barrel, and analyze the parameters to obtain the color variegation analysis results of each fruit cyst belonging to each fruit cyst raw material barrel.

[0031] As a specific feasibility example, the color information parameters of each fruit cyst belonging to each fruit cyst raw material barrel include each color spot belonging to the outer surface and each color spot belonging to the interior.

[0032] It needs to be further explained that the specific method of obtaining the various colorful spots belonging to the outer surface and the various colorful spots belonging to the inside of each fruit cyst belonging to each fruit cyst raw material barrel is: using a target camera to detect each fruit cyst belonging to each fruit cyst raw material barrel to obtain the various colorful spots belonging to the outer surface and the various colorful spots belonging to the inside of each fruit cyst belonging to each fruit cyst raw material barrel.

[0033] As a specific feasibility example, the variegated color analysis results of each fruit cyst belonging to each fruit cyst raw material barrel include: extracting each variegated color spot on the outer surface of each fruit cyst belonging to each fruit cyst raw material barrel and each variegated color spot inside, further determining each abnormal variegated color spot on the outer surface of each fruit cyst belonging to each fruit cyst raw material barrel and each abnormal variegated color spot inside, and performing statistics to obtain the number of abnormal variegated color spots on the outer surface of each fruit cyst belonging to each fruit cyst raw material barrel. and the number of abnormal mottled spots inside Wherein, j=1,2,...,J, j is the serial number of each fruit cyst raw material barrel, and b=1,2,...,B, b is the serial number of each fruit cyst.

[0034] Analyze the color risk coefficient of each fruit cyst belonging to each fruit cyst raw material barrel

[0035]

[0036] As a specific feasibility example, the specific operation method of determining each abnormal variegated spot on the outer surface and each abnormal variegated spot inside each fruit cyst raw material barrel includes: using grayscale processing technology to grayscale each fruit cyst belonging to each fruit cyst raw material barrel, to obtain the grayscale image of each variegated spot on the outer surface of each fruit cyst belonging to each fruit cyst raw material barrel and the grayscale value of each corresponding pixel point, and the grayscale image of each variegated spot inside and the grayscale value of each corresponding pixel point.

[0037] According to the analysis model Obtain the abnormality Yspot of each variegated spot on the outer surface of each fruit cyst belonging to each fruit cyst raw material barrel jbf , where Y1 is a set consisting of the grayscale values ​​of each pixel corresponding to the grayscale image of each mottled spot on the outer surface of each fruit cyst belonging to each fruit cyst raw material barrel, Y0 is a set consisting of the grayscale values ​​of each pixel corresponding to the grayscale image of each permitted mottled spot extracted from the information repository, ∪ is the union symbol, f=1,2,...,F, f is the number of each mottled spot on the outer surface, so that each abnormal mottled spot on the outer surface of each fruit cyst belonging to each fruit cyst raw material barrel can be determined.

[0038] As a specific example, the grayscale values ​​of each pixel point corresponding to the grayscale image of each mottled spot on the outer surface of each fruit cyst belonging to each fruit cyst raw material barrel are Gray jbf1 Gray jbf1 、......、Gray jbft 、......、Gray jbfT , where t = 1, 2, ..., T, t is the number of pixels corresponding to the grayscale image of the mottled spots, T is the number of pixels corresponding to the grayscale image of the mottled spots, and the elements of the set Y1 are Gray jbf1 Gray jbf1 、......、Gray jbft 、......、Gray jbfT The different values ​​that exist in .

[0039] It should be further explained that the set is a basic concept in mathematics, which is used to represent a group of objects with certain common characteristics. These objects are called elements of the set. The set has the characteristics of determinism, mutual difference and disorder.

[0040] Among them, the definition of determinism is: given a set, any element must either belong to the set or not belong to the set, and no ambiguity is allowed.

[0041] The definition of mutual heterogeneity is that the elements in a set are different from each other, that is, each element can only appear once.

[0042] The definition of disorder is that every element in a set has the same status and there is no order between the elements. In other words, the order of the elements in a set does not affect the set itself.

[0043] It should be further explained that Y1∪Y0 represents the union of set Y1 and set Y0, and the result is a set containing all elements in Y1 and Y0.

[0044] Similarly, the abnormal variegated spots inside each fruit cyst belonging to each fruit cyst raw material barrel can be determined.

[0045] The foreign matter detection and analysis module is used to obtain the foreign matter information parameters of each fruit cyst belonging to each fruit cyst raw material barrel, and analyze the foreign matter information parameters to obtain the foreign matter analysis results of each fruit cyst belonging to each fruit cyst raw material barrel.

[0046] As a specific feasibility example, the foreign matter information parameters of each fruit cyst belonging to each fruit cyst raw material barrel include the number of foreign matters and the volume of each foreign matter corresponding to each type of foreign matter on the outer surface and the number of foreign matters and the volume of each foreign matter corresponding to each type of foreign matter inside.

[0047] It needs to be further explained that the specific method for obtaining the number and volume of foreign objects corresponding to each type of foreign objects on the outer surface of each fruit cyst belonging to each fruit cyst raw material barrel, as well as the number and volume of foreign objects belonging to each type of foreign objects inside, includes: using a target camera to detect each fruit cyst belonging to each fruit cyst raw material barrel to obtain the number and volume of foreign objects corresponding to each type of foreign objects on the outer surface of each fruit cyst belonging to each fruit cyst raw material barrel, as well as the number and volume of foreign objects belonging to each type of foreign objects inside.

[0048] As a specific feasibility example, the foreign matter analysis results of each fruit cyst belonging to each fruit cyst raw material barrel include: extracting the number of foreign matters and the volume of each foreign matter corresponding to each type of foreign matter on the outer surface of each fruit cyst belonging to each fruit cyst raw material barrel, as well as the number of foreign matters and the volume of each foreign matter corresponding to each type of foreign matter inside, and recording them as Among them, h=1,2,...,H, h is the number of each type of foreign matter belonging to the outer surface, p=1,2,...,c, p is the number of each foreign matter corresponding to the type of foreign matter belonging to the outer surface, g=1,2,...,G, g is the number of each type of foreign matter belonging to the interior, q=1,2,...,Q, q is the number of each foreign matter corresponding to the type of foreign matter belonging to the interior.

[0049] Analyze the foreign matter risk factor of each fruit cyst belonging to each fruit cyst raw material barrel

[0050]

[0051] Where Vfm0 is the set foreign matter volume allowed to exist.

[0052] The detection result output module is used to output the detection results of each fruit cyst raw material barrel, and analyze the next control requirements of each fruit cyst raw material barrel based on this, where the next control requirements include continued transportation requirements, rejection warning requirements and scrap warning requirements.

[0053] As a specific feasibility example, the next step control requirements of each fruit cyst raw material barrel are analyzed in a specific manner including: extracting the color risk coefficient and foreign matter risk coefficient of each fruit cyst belonging to each fruit cyst raw material barrel, and calculating the color risk coefficient according to the calculation model. Get the comprehensive risk coefficient of each fruit cyst belonging to each fruit cyst raw material barrel

[0054] The comprehensive risk coefficients of each fruit cyst belonging to each fruit cyst raw material barrel are matched with the comprehensive risk coefficient ranges corresponding to each risk level extracted from the information storage repository to obtain the risk level of each fruit cyst belonging to each fruit cyst raw material barrel; the fruit cysts belonging to each fruit cyst raw material barrel are classified according to the risk level to obtain the fruit cysts corresponding to each risk level of each fruit cyst raw material barrel; statistics are performed to obtain the total number of fruit cysts corresponding to each risk level of each fruit cyst raw material barrel; the ratios are respectively made with the total number of fruit cysts belonging to each fruit cyst raw material barrel, and the quotient is recorded as the proportion of each risk level of each fruit cyst raw material barrel.

[0055] It should be further explained that the risk levels include no risk, low risk, medium risk and high risk.

[0056] Among them, no risk means that the fruit cysts have no abnormal color and foreign matter. Low risk means that the fruit cysts have slight diseases or damage, but do not affect the safety of the whole fruit. Medium risk means that the fruit cysts are affected by diseases. High risk means that the fruit cysts are seriously affected by diseases, such as rot, mold growth, etc.

[0057] And according to the proportion of each risk level of each fruit cyst raw material barrel, the next control requirement of each fruit cyst raw material barrel is obtained.

[0058] It should be further explained that the specific analysis method for obtaining the next control requirements of each fruit cyst raw material barrel by analyzing the proportion of each risk level of each fruit cyst raw material barrel is as follows: Get the proportion of each risk level of each fruit cyst raw material barrel and analyze it to get the next control demand of each fruit cyst raw material barrel Need j , where P1 represents the need to continue transporting, P2 represents the need to remove the warning, and P3 represents the need to scrap the warning. They are the proportions of no risk, low risk, medium risk and high risk for the j-th fruit cyst raw material barrel respectively.

[0059] The control module is used to control each fruit cyst raw material barrel according to the next control requirement.

[0060] As a specific feasibility example, the control is performed according to the next control demand of each fruit cyst raw material barrel, and its specific operation method includes: if the next control demand of a fruit cyst raw material barrel is a demand for continued transportation, then the fruit cyst raw material barrel will continue to be transported.

[0061] If the next control requirement of a fruit cyst raw material barrel is a rejection warning requirement, the fruit cyst raw material barrel will be given a rejection warning and transported to the rejection conveying track.

[0062] If the next control demand of a fruit cyst raw material barrel is a scrap warning demand, the fruit cyst raw material barrel will be scrapped and warned, and it will be transported to the scrap conveying track.

[0063] The information storage repository is used to store the grayscale value of each pixel point corresponding to the grayscale image of each permitted mottled spot, store the comprehensive risk coefficient range corresponding to each risk level, and store the standard conveying position of the fruit cyst raw material barrel to be tested.

[0064] The present invention proposes to use a high-definition camera combined with a microcomputer to perform high-frequency high-definition photography of the surface of the fruit cysts when the fruit cyst raw material barrels are unpacked to collect information, analyze the information, and then output the detection results of each fruit cyst raw material barrel, and accordingly realize the continued delivery demand, early warning, removal and scrapping of each fruit cyst raw material barrel, replacing the human eye to find more and smaller foreign objects, and reducing the risk of foreign objects in the unpacking of cysts. It is an effective detection method, thereby avoiding food poisoning, allergic reactions or other health problems caused by pests, chemical residues, mechanical damage, etc. after ingestion by the human body, ensuring the health of the human body and the food safety of consumers. It also guarantees the appearance quality of the product to a certain extent, which is conducive to improving consumers' acceptance and satisfaction with the product.

[0065] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they shall all fall within the protection scope of the present invention.

Claims

1. An AI vision-based automatic detection system for fruit cysts and foreign matter, characterized by: include: A camera adjustment requirement judgment module is used to record the fruit cyst raw material barrel to be detected at the detection position as the target fruit cyst raw material barrel, and then analyze the position matching between the target camera and the target camera, and judge the adjustment requirement of the target camera; A camera adjustment module is used to adjust the target camera when the adjustment requirement is that the adjustment requirement is required; The raw material barrel position detection and analysis module is used to detect the position of each fruit cyst raw material barrel to be detected, analyze its position matching with the target camera, and determine the correction requirements of the conveying position of the fruit cyst raw material barrel to be detected, and further process it; The color variegation detection and analysis module is used to obtain the color variegation information parameters of each fruit cyst belonging to each fruit cyst raw material barrel, and analyze the parameters to obtain the color variegation analysis results of each fruit cyst belonging to each fruit cyst raw material barrel; The foreign matter detection and analysis module is used to obtain the foreign matter information parameters of each fruit cyst belonging to each fruit cyst raw material barrel, and analyze the foreign matter information parameters to obtain the foreign matter analysis results of each fruit cyst belonging to each fruit cyst raw material barrel; The detection result output module is used to output the detection results of each fruit cyst raw material barrel, and analyze the next control requirements of each fruit cyst raw material barrel accordingly, wherein the next control requirements include the requirements for continued transportation, the requirements for rejection warning and the requirements for scrapping warning; A control module is used to control each fruit cyst raw material barrel according to the next control requirement; An information repository for storing the grayscale value of each pixel corresponding to the grayscale image of each permitted mottled spot, storing the comprehensive risk coefficient range corresponding to each risk level, and storing the standard conveying position of the fruit cyst raw material barrel to be tested; The variegated color information parameters of each fruit cyst belonging to each fruit cyst raw material barrel include variegated color spots belonging to the outer surface and variegated color spots belonging to the interior; The foreign matter information parameters of each fruit cyst belonging to each fruit cyst raw material barrel include the number of foreign matters and the volume of each foreign matter corresponding to each type of foreign matter on the outer surface and the number of foreign matters and the volume of each foreign matter corresponding to each type of foreign matter inside; The specific analysis method of the color analysis results of each fruit cyst belonging to each fruit cyst raw material barrel includes: Extract the mottled spots on the outer surface and the mottled spots inside each fruit cyst raw material barrel, further determine the abnormal mottled spots on the outer surface and the abnormal mottled spots inside each fruit cyst raw material barrel, and count them to obtain the number of abnormal mottled spots on the outer surface of each fruit cyst raw material barrel and the number of abnormal mottled spots inside ,in , is the number of each fruit cyst raw material barrel, , is the number of each fruit cyst; Analyze the color risk coefficient of each fruit cyst belonging to each fruit cyst raw material barrel ; The specific operation of determining each abnormal variegated spot on the outer surface and each abnormal variegated spot inside each fruit cyst belonging to each fruit cyst raw material barrel includes: The fruit cysts of each fruit cyst raw material barrel are subjected to grayscale processing by using a grayscale processing technology to obtain a grayscale image of each mottled spot on the outer surface of each fruit cyst raw material barrel and the grayscale value of each corresponding pixel point, and a grayscale image of each mottled spot inside the fruit cyst raw material barrel and the grayscale value of each corresponding pixel point; According to the analysis model Obtain the abnormality of each variegated spot on the outer surface of each fruit cyst belonging to each fruit cyst raw material barrel ,in is a set consisting of the grayscale values ​​of each pixel corresponding to the grayscale image of each mottled spot on the outer surface of each fruit cyst belonging to each fruit cyst raw material barrel, is a set consisting of the grayscale values ​​of each pixel corresponding to the grayscale image of each permitted mottled spot extracted from the information repository, is the union symbol, , The numbers of the various colored spots on the outer surface are used to identify the various abnormal colored spots on the outer surface of each fruit cyst belonging to each fruit cyst raw material barrel; Similarly, the abnormal variegated spots inside each fruit cyst belonging to each fruit cyst raw material barrel can be determined.

2. The AI ​​vision-based automatic detection system for fruit cyst variegated foreign matter according to claim 1, characterized in that: The analysis of the position matching between the target fruit cyst raw material barrel and the target camera specifically includes: Take the center point of the target fruit cyst raw material barrel as the coordinate origin, draw a straight line through the coordinate origin on the surface of the target water surface cyst raw material barrel as the horizontal axis, and draw a straight line through the coordinate origin and perpendicular to the horizontal axis as the vertical axis to establish a two-dimensional coordinate system, and then obtain the center point coordinates of the target camera in the two-dimensional coordinate system established based on the target fruit cyst raw material barrel, which is recorded as ,in is the horizontal coordinate of the center point of the target camera, is the ordinate of the center point of the target camera; like , it means that the position of the target fruit cyst raw material barrel matches the position of the target camera, and the adjustment requirement of the target camera is no need to adjust. , it means that the position of the target fruit cyst raw material barrel does not match the position of the target camera, and thus the adjustment demand of the target camera is required.

3. The AI ​​vision-based automatic detection system for fruit cyst variegated foreign matter according to claim 2, characterized in that: When the adjustment requirement of the target camera is that an adjustment requirement is required, the target camera is adjusted, and the specific operations include: Adjust the position of the target camera so that the vertical coordinate of its center point is 0.

4. The AI ​​vision-based automatic detection system for fruit cyst variegated foreign matter according to claim 2, characterized in that: The specific analysis method of the matching between the position of each of the fruit cyst raw material barrels to be detected and the position of the target camera includes: The center point coordinates of the target camera in the two-dimensional coordinate system established based on the target fruit cyst raw material barrel can be obtained by obtaining the center point coordinates of the target camera in the two-dimensional coordinate system established based on each fruit cyst raw material barrel to be detected, which are respectively recorded as ,in , is the number corresponding to each barrel of fruit cyst raw material to be tested, is the number of barrels of fruit cyst raw materials to be tested. , then it means If the position of the fruit cyst raw material barrel to be detected matches the position of the target camera, , then it means The positions of the fruit cyst raw material barrels to be detected do not match the positions of the target cameras, and then the number of fruit cyst raw material barrels to be detected that match the positions of the target cameras is counted. , according to the calculation formula Get the matching rate between the position of the fruit cyst raw material barrel to be detected and the target camera ; The matching rate between the position of the fruit cyst raw material barrel to be inspected and the target camera is compared with the set matching rate threshold. If it is greater than or equal to the set matching rate threshold, the correction requirement of the conveying position of the fruit cyst raw material barrel to be inspected is recorded as no correction requirement. Otherwise, the correction requirement of the conveying position of the fruit cyst raw material barrel to be inspected is recorded as a correction requirement, and it is corrected according to the predefined correction principle.

5. The AI ​​vision-based automatic detection system for fruit cyst variegated foreign matter according to claim 1, characterized in that: The foreign matter analysis results of each fruit cyst belonging to each fruit cyst raw material barrel include: The number and volume of each foreign body corresponding to each type of foreign body on the outer surface of each fruit cyst from each fruit cyst raw material barrel, as well as the number and volume of each type of foreign body inside the fruit cyst are extracted and recorded as ,in , is the number of each type of foreign matter on the outer surface, , is the number of each foreign body corresponding to the type of foreign body to which the outer surface belongs, , It is the number of each kind of foreign matter inside. , The numbers of the foreign objects corresponding to the categories of foreign objects in the interior; Analyze the foreign matter risk factor of each fruit cyst belonging to each fruit cyst raw material barrel ,in The volume of foreign matter allowed to exist is set.

6. The AI ​​vision-based automatic detection system for fruit cyst variegated foreign matter according to claim 5, characterized in that: The specific analysis method of the next step control requirements of each fruit cyst raw material barrel includes: Extract the risk coefficient of variegated color and foreign matter of each fruit cyst raw material barrel, and calculate the risk coefficient according to the calculation model. Get the comprehensive risk coefficient of each fruit cyst belonging to each fruit cyst raw material barrel ; Match the comprehensive risk coefficients of each fruit cyst belonging to each fruit cyst raw material barrel with the comprehensive risk coefficient ranges corresponding to each risk level extracted from the information repository to obtain the risk level of each fruit cyst belonging to each fruit cyst raw material barrel, classify the fruit cysts belonging to each fruit cyst raw material barrel according to the risk level to obtain the fruit cysts corresponding to each risk level of each fruit cyst raw material barrel, perform statistics on the total number of fruit cysts corresponding to each risk level of each fruit cyst raw material barrel, make ratios with the total number of fruit cysts belonging to each fruit cyst raw material barrel, and record the quotient as the proportion of each risk level of each fruit cyst raw material barrel; And according to the proportion of each risk level of each fruit cyst raw material barrel, the next control requirement of each fruit cyst raw material barrel is obtained through analysis.

7. The AI ​​vision-based automatic detection system for fruit cyst variegated foreign matter according to claim 6, characterized in that: The control is performed according to the next control requirements of each fruit cyst raw material barrel, and the specific operation method includes: If the next control requirement of a fruit cyst raw material barrel is to continue to transport, then the fruit cyst raw material barrel will continue to be transported; If the next control requirement of a fruit cyst raw material barrel is a rejection warning requirement, the fruit cyst raw material barrel is given a rejection warning and is transported to the rejection conveying track; If the next control demand of a fruit cyst raw material barrel is a scrap warning demand, the fruit cyst raw material barrel will be scrapped and warned, and it will be transported to the scrap conveying track.

Images