A method for preventing heavy work in pipeline visual processing

Abstract

The present invention discloses a pipeline visual processing anti-duplicate method, the method comprising: obtaining a visual recognition image p (n) ; According to the visual recognition image p (n) Determine the recognition data and the visual recognition coordinates of the recognition data; (n) and the previous visual recognition image p (n‑1) Compare and determine the repeated recognition area as the overlapping area; filter and remove the recognition data corresponding to the visual recognition coordinates in the overlapping area; set the moving distance tolerance; based on the moving distance tolerance, respectively use the previous visual recognition image p (n‑1) and the n‑2th visual recognition image p (n‑2) For visual recognition image p (n) The remaining identification data is screened, and the identification data that meets the two screening conditions is eliminated. The present invention avoids the problem of repeated processing without visual inspection, improves product inspection efficiency, and effectively prevents duplication when the on-site environment is harsh and visual inspection is not possible.

Description

Technical Field

[0001] The present application relates to the field of laser marking technology, and more specifically, to an anti-weighting method for assembly line visual processing. Background Art

[0002] The visual processing anti-duplicate technology of assembly line is a technology to identify and verify products during the manufacturing process, which is mainly detected by visual inspection methods. However, the visual inspection method has high requirements for the technical environment, and the technical solution is complex and has high technical requirements for personnel.

[0003] This technology is commonly used in automated production and processing lines to ensure product consistency and traceability throughout the entire production process. The importance of assembly line visual processing anti-duplicate is becoming increasingly prominent. It can quickly detect abnormal situations and take measures to correct them by real-time monitoring of product and production process data, thereby improving production efficiency and quality. In addition, assembly line visual processing anti-duplicate can also help companies achieve visualization and intelligent management, and improve the flexibility and adaptability of production lines. However, due to the complexity and diversity of production processes, assembly line visual processing anti-duplicate faces many challenges, such as the accuracy and real-time nature of data collection and processing, rapid identification and response to abnormal situations, and protection of personnel and equipment safety. Therefore, the development of efficient assembly line visual processing anti-duplicate technology and solutions is of great significance to enhancing the competitiveness of the manufacturing industry and responding to future challenges.

[0004] Therefore, the prior art has defects and is in urgent need of improvement. Summary of the invention

[0005] In view of the above problems, the purpose of the present invention is to provide a method for preventing weight loss through visual processing of pipelines, which can improve processing efficiency, lower the threshold of visual weight loss prevention, and solve the problem that the visual detection method cannot effectively prevent weight loss due to the harsh on-site environment. At the same time, the method is simple to use and does not require complex debugging such as visual detection.

[0006] The first aspect of the present invention provides a pipeline visual processing anti-weighting method, comprising:

[0007] Get the visual recognition image p (n) ;

[0008] According to the visual recognition image p (n) determining identification data and visual identification coordinates of the identification data;

[0009] The visual recognition image p (n) and the previous visual recognition image p (n-1) Comparison is performed and the repeated identification areas are determined as overlapping areas;

[0010] Screening and removing the recognition data corresponding to the visual recognition coordinates in the overlapping area;

[0011] Set the moving distance tolerance;

[0012] Based on the moving distance tolerance, the previous visual recognition image p is respectively (n-1) and the n-2th visual recognition image p (n-2) For visual recognition image p (n) The remaining identification data are screened and the identification data that meet the two screening conditions are eliminated.

[0013] In this solution, the acquisition of visual recognition image p (n) ,include:

[0014] Set the preset movement distance based on the length of the camera's field of view;

[0015] Get the moving distance of the motor shaft;

[0016] When the motor shaft moves to a preset distance, the visual recognition image p is obtained. (n) .

[0017] This plan also includes:

[0018] The preset moving distance L satisfies:

[0019] ;

[0020] Wherein, W is the length of the camera field of view and D is the length of a single recognition data.

[0021] In this solution, the setting of the moving distance tolerance includes:

[0022] The moving distance tolerance is calculated by the angle difference a between the camera and the motor shaft; the moving distance tolerance includes a lateral tolerance Rx and a longitudinal tolerance Ry;

[0023] The calculation method of the lateral tolerance Rx and the longitudinal tolerance Ry is expressed by the formula:

[0024] ;

[0025] .

[0026] In this solution, based on the moving distance tolerance, the previous visual recognition image p (n-1) and the n-2th visual recognition image p (n-2) For visual recognition image p (n) The remaining identification data is screened and the identification data that meets the two screening conditions is eliminated, including:

[0027] Determine the first theoretical coordinates of the recognition data based on the visual recognition coordinates and the preset moving distance of the recognition data in the previous visual recognition image p (n-1) Analyze the first theoretical coordinates and the visual recognition coordinates of the same recognition data, and label the recognition data that meets the screening conditions;

[0028] Determine the second theoretical coordinates of the recognition data according to the visual recognition coordinates and the preset moving distance of the recognition data in the (n - 2)th visual recognition image p (n-2) Analyze the second theoretical coordinates and the visual recognition coordinates of the same recognition data, and label the recognition data that meets the screening conditions;

[0029] Count the number of times each recognition data is labeled, and eliminate the recognition data with two labeling times.

[0030] In this solution, the step of determining the first theoretical coordinates of the recognition data according to the visual recognition coordinates and the preset moving distance of the recognition data in the previous visual recognition image p (n-1) and analyzing the first theoretical coordinates and the visual recognition coordinates of the same recognition data, and labeling the recognition data that meets the screening conditions includes:

[0031] Determine the center coordinates of the recognition data in the previous visual recognition image p (n-1) as the first reference coordinates (x0, y0);

[0032] Determine the first theoretical coordinates (x1, y1) after the recognition data moves once according to the first reference coordinates (x0, y0) and the preset moving distance;

[0033] Based on the visual recognition coordinates (x2, y2) of the recognition data in the visual recognition image p (n) analyze the first theoretical coordinates (x1, y1), the visual recognition coordinates (x2, y2) and the first distance tolerance of the same recognition data to determine whether the same recognition data meets the screening conditions; the first distance tolerance is the moving distance tolerance (Rx, Ry);

[0034] When |x1 - x2| < Rx and |y1 - y2| < Ry are satisfied simultaneously, it is determined that the same recognition data meets the screening conditions.

[0035] In this solution, the step of determining the second theoretical coordinates of the recognition data according to the visual recognition coordinates and the preset moving distance of the recognition data in the (n - 2)th visual recognition image p (n-2) and analyzing the second theoretical coordinates and the visual recognition coordinates of the same recognition data, and labeling the recognition data that meets the screening conditions includes:

[0036] Determine the central coordinates of the recognition data in the visual recognition image p of the (n - 2)th time as the second reference coordinates (x0', y0'); (n-2)

[0037] Determine the second theoretical coordinates (x3, y3) after the recognition data is moved twice according to the second reference coordinates (x0', y0') and the preset moving distance;

[0038] Calculate the second distance tolerance (Rx', Ry') of the recognition data according to the moving distance tolerance;

[0039] Based on the visual recognition coordinates (x2, y2) of the recognition data in the visual recognition image p (n) Analyze the second theoretical coordinates (x3, y3), visual recognition coordinates (x2, y2) and second distance tolerance (Rx', Ry') of the same recognition data to determine whether the same recognition data meets the screening conditions;

[0040] When |x3 - x2| < Rx and |y3 - y2| < Ry are satisfied simultaneously, determine that the same recognition data meets the screening conditions.

[0041] In this solution, it further includes:

[0042] The calculation method of the second distance tolerance (Rx', Ry') is expressed by the formula:

[0043] ;

[0044] .

[0045] The present invention discloses a method for preventing duplication in pipeline visual processing. The method includes: obtaining the visual recognition image p (n) ; determining the recognition data and the visual recognition coordinates of the recognition data according to the visual recognition image p (n) ; comparing the visual recognition image p (n) with the previous visual recognition image p (n-1) to determine the overlapping area as the overlapping region; screening and eliminating the recognition data corresponding to the visual recognition coordinates in the overlapping region; setting the moving distance tolerance; based on the moving distance tolerance, respectively screening the remaining recognition data in the visual recognition image p (n-1) and the visual recognition image p of the (n - 2)th time (n-2) to screen the remaining recognition data in the visual recognition image p (n) and eliminating the recognition data that meets the two screening conditions. The present invention avoids the occurrence of repeated processing problems without visual detection, improves the product detection efficiency, and effectively prevents duplication in the case where the on-site environment is harsh and visual detection cannot be passed. BRIEF DESCRIPTION OF THE DRAWINGS ​

[0046] Figure 1 A flow chart of an assembly line visual processing anti-weighting method provided by the present invention is shown;

[0047] Figure 2 A flow chart showing the visual recognition image acquisition method provided by the present invention is shown;

[0048] Figure 3 A flow chart of a method for screening and eliminating the remaining recognition data in a visual recognition image provided by the present invention is shown;

[0049] Figure 4 A schematic diagram of the n-1th visual recognition image provided by the present invention is shown;

[0050] Figure 5 A schematic diagram of the nth visual recognition image provided by the present invention is shown. DETAILED DESCRIPTION

[0051] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0052] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0053] Figure 1 A flow chart of an anti-duplicate method for pipeline visual processing provided by the present invention is shown.

[0054] like Figure 1 As shown, the present invention discloses a pipeline visual processing anti-weighting method, comprising:

[0055] S102, obtaining a visual recognition image p (n) ;

[0056] S104, based on the visual recognition image p (n) determining identification data and visual identification coordinates of the identification data;

[0057] S106, visual recognition image p (n) and the previous visual recognition image p (n-1) Comparison is performed and the repeated identification areas are determined as overlapping areas;

[0058] S108, filtering and removing the recognition data corresponding to the visual recognition coordinates in the overlapping area;

[0059] S110, setting the moving distance tolerance;

[0060] S112, based on the moving distance tolerance, respectively through the previous visual recognition image p (n-1) and the n-2th visual recognition image p (n-2) For visual recognition image p (n) The remaining identification data are screened and the identification data that meet the two screening conditions are eliminated.

[0061] According to an embodiment of the present invention, in a scenario where unidirectional motor movement is combined with visual recognition positioning, the conveyor belt is controlled by a motor so that the product on the conveyor belt moves with the conveyor belt, and a visual recognition image is acquired by the moving distance of the motor shaft. When the moving distance of the motor shaft meets the preset moving distance, a visual recognition image is acquired by a detection device such as a camera. The recognition data in the visual recognition image is the recognition image of a single product, and the center coordinates of the recognition image of each product are determined as the visual recognition coordinates of the recognition data corresponding to the product. The current visual recognition image p is calibrated according to the preset moving distance. (n) and the previous visual recognition image p (n-1) To analyze, the current (nth) visual recognition image p (n) The schematic diagram is as follows Figure 5 As shown, the last (n-1th) visual recognition image p (n-1) The schematic diagram is as follows Figure 4 As shown, Figure 4 The middle arrow indicates that the conveyor belt moves from left to right. The repeated recognition areas that appear in the two recognition processes are determined as overlapping areas. (n) The recognition data whose visual recognition coordinates are in the overlapping area are screened out to avoid repeated recognition.

[0062] Figure 2 A flow chart of the visual recognition image acquisition method provided by the present invention is shown.

[0063] like Figure 2 As shown, according to an embodiment of the present invention, a visual recognition image p is obtained. (n) ,include:

[0064] S202, setting a preset moving distance based on the length of the camera's field of view;

[0065] S204, obtaining the moving distance of the motor shaft;

[0066] S206, when the motor shaft movement distance meets the preset movement distance, obtain the visual recognition image p (n) .

[0067] It should be noted that the preset moving distance is smaller than the length of the camera's field of view (i.e., the length of the visual recognition image). By setting a reasonable moving distance, it is ensured that after the motor shaft moves, part of the previous visual recognition image appears within the recognition range of the current visual recognition image. Those skilled in the art can adjust the value of the preset moving distance according to actual needs. The smaller the value of the preset moving distance, the higher the accuracy of preventing missed recognition.

[0068] According to an embodiment of the present invention, it also includes:

[0069] The preset moving distance L satisfies:

[0070] ;

[0071] Wherein, W is the length of the camera field of view and D is the length of a single recognition data.

[0072] It should be noted that the system's default preset moving distance is at least greater than half the length of the camera's field of view. To ensure that adjacent visual recognition images acquired have overlapping areas and that there is at least one complete product's identification data in the overlapping area, the preset moving distance also needs to be less than the length of the camera's field of view minus the length of the identification data of a single (product) on the assembly line.

[0073] According to an embodiment of the present invention, setting the moving distance tolerance includes:

[0074] The moving distance tolerance is calculated by the angle difference a between the camera and the motor shaft; the moving distance tolerance includes the lateral tolerance Rx and the longitudinal tolerance Ry;

[0075] The calculation method of the lateral tolerance Rx and the longitudinal tolerance Ry is expressed by the formula:

[0076] ;

[0077] .

[0078] It should be noted that the moving distance tolerance includes the lateral tolerance Rx in the lateral direction and the longitudinal tolerance Ry in the longitudinal direction. The moving distance tolerance is represented by (Rx, Ry). The moving distance tolerance is calculated by the angle difference a between the camera and the motor shaft in the assembly line in the horizontal direction or the vertical direction.

[0079] Figure 3 A flow chart of a method for screening and eliminating remaining recognition data in a visual recognition image provided by the present invention is shown.

[0080] like Figure 3 As shown, according to an embodiment of the present invention, based on the moving distance tolerance, the previous visual recognition image p is respectively (n-1) and the n-2th visual recognition image p(n-2) For visual recognition image p (n) The remaining identification data is screened and the identification data that meets the two screening conditions is eliminated, including:

[0081] S302, based on the previous visual recognition image p (n-1) The first theoretical coordinates of the recognition data are determined by the visual recognition coordinates of the recognition data and the preset moving distance, and the recognition data that meets the screening conditions are marked by analyzing the first theoretical coordinates and the visual recognition coordinates of the same recognition data;

[0082] S304, based on the n-2th visual recognition image p (n-2) The visual recognition coordinates of the recognition data and the preset moving distance are used to determine the second theoretical coordinates of the recognition data, and the recognition data that meets the screening conditions are marked by analyzing the second theoretical coordinates and the visual recognition coordinates of the same recognition data;

[0083] S306, counting the number of times each piece of recognition data is marked, and eliminating recognition data that has been marked twice.

[0084] It should be noted that, based on the determination of the overlapping area, the previous visual recognition image p (n-1) and the n-2th visual recognition image p (n-2) For the current visual recognition image p (n) The remaining identification data are screened twice, and the identification data that meet the screening conditions in both screenings are eliminated.

[0085] First, the previous visual recognition image p (n-1) and the n-2th visual recognition image p (n-2) The center coordinates of each recognition data in the image are used as reference coordinates, and the number of moves and the preset moving distance are combined to determine the position of each recognition data in the current visual recognition image p. (n) The theoretical coordinate data in the image is used to calculate the coordinate difference between the theoretical coordinate data and the visual recognition coordinate of the same recognition data, and the recognition data whose horizontal coordinate difference meets the horizontal tolerance and whose vertical coordinate difference meets the vertical tolerance are marked to determine that the recognition data meets the screening condition. Statistics of the current visual recognition image p (n) The number of annotations for each recognition data in the , and the recognition data with two annotations will be eliminated.

[0086] According to an embodiment of the present invention, based on the previous visual recognition image p (n-1) The first theoretical coordinates of the recognition data are determined by the visual recognition coordinates of the recognition data and the preset moving distance, and the recognition data that meets the screening conditions are marked by analyzing the first theoretical coordinates and the visual recognition coordinates of the same recognition data, including:

[0087] Determine the center coordinates of the recognition data in the previous visual recognition image p (n-1) as the first reference coordinates (x0, y0);

[0088] Determine the first theoretical coordinates (x1, y1) after the recognition data is moved once according to the first reference coordinates (x0, y0) and the preset moving distance;

[0089] Based on the visual recognition coordinates (x2, y2) of the recognition data in the visual recognition image p (n) Analyze the first theoretical coordinates (x1, y1), the visual recognition coordinates (x2, y2) and the first distance tolerance of the same recognition data to determine whether the same recognition data meets the screening conditions; the first distance tolerance is the moving distance tolerance (Rx, Ry);

[0090] When both |x1 - x2| < Rx and |y1 - y2| < Ry are satisfied, it is determined that the same recognition data meets the screening conditions.

[0091] It should be noted that when the recognition data in the visual recognition image p (n) is screened for the first time, the previous visual recognition image p (n-1) is used as the comparison standard, and the center coordinates of the recognition data in the previous visual recognition image p (n-1) are determined as the first reference coordinates (x0, y0). According to the preset moving distance L, the first theoretical coordinates (x1, y1) after the recognition data is moved once are determined, where x1 = x0 + L and y1 = y0. Calculate the distance differences between the first theoretical coordinates (x1, y1) and the visual recognition coordinates (x2, y2) in the horizontal and vertical directions, represented by (|x1 - x2|, |y1 - y2|). Compare the distance difference (|x1 - x2|, |y1 - y2|) with the first distance tolerance (Rx, Ry). When both |x1 - x2| < Rx and |y1 - y2| < Ry are satisfied, it is determined that the corresponding recognition data is duplicate recognition, and it is determined that the recognition data meets the screening conditions for the first screening.

[0092] Among them, since the current visual recognition image p (n) is obtained by moving the previous visual recognition image p (n-1) once, the first distance tolerance can be represented by the moving distance tolerance, that is, the first distance tolerance is (Rx, Ry).

[0093] ;

[0094] .

[0095] According to the embodiments of the present invention, according to the visual recognition image p of the (n - 2)th time (n-2)Determine the second theoretical coordinates of the recognition data based on the visual recognition coordinates and the preset moving distance of the recognition data in []. By analyzing the second theoretical coordinates and the visual recognition coordinates of the same recognition data, label the recognition data that meets the screening conditions, including:

[0096] Determine the center coordinates of the recognition data in the visual recognition image p of the (n - 2)th time as the second reference coordinates (x0', y0'); (n-2) (n-2)

[0097] Determine the second theoretical coordinates (x3, y3) of the recognition data after moving twice according to the second reference coordinates (x0', y0') and the preset moving distance;

[0098] Calculate the second distance tolerance (Rx', Ry') of the recognition data according to the moving distance tolerance;

[0099] Based on the visual recognition coordinates (x2, y2) of the recognition data in the visual recognition image p (n) Analyze the second theoretical coordinates (x3, y3), the visual recognition coordinates (x2, y2) and the second distance tolerance (Rx', Ry') of the same recognition data to determine whether the same recognition data meets the screening conditions;

[0100] When |x3 - x2| < Rx and |y3 - y2| < Ry are satisfied simultaneously, determine that the same recognition data meets the screening conditions.

[0101] It should be noted that when performing the second screening on the recognition data in the visual recognition image p (n) Take the visual recognition image p of the (n - 2)th time as the comparison standard, and determine the center coordinates of the recognition data in the visual recognition image p of the (n - 2)th time as the second reference coordinates (x0', y0'). Determine the second theoretical coordinates (x3, y3) of the recognition data after moving once according to the preset moving distance L, where x3 = x0' + 2L and y3 = y0'. Calculate the distance differences between the second theoretical coordinates (x3, y3) and the visual recognition coordinates (x2, y2) in the horizontal and vertical directions, represented by (|x3 - x2|, |y3 - y2|). Compare the distance difference (|x3 - x2|, |y3 - y2|) with the second distance tolerance (Rx', Ry'). When |x1 - x2| < Rx' and |y1 - y2| < Ry' are satisfied simultaneously, determine the corresponding recognition data as duplicate recognition and determine that the recognition data meets the screening conditions for the second screening. (n-2) (n-2) (n-2) (n-2)

[0102] According to the embodiments of the present invention, it further includes:

[0103] The calculation method of the second distance tolerance (Rx', Ry') is expressed by the formula:

[0104] ;

[0105] .

[0106] It should be noted that the distance tolerance is determined by the product of the number of moves and the moving distance tolerance, and the second distance tolerance is determined by the n-2th visual recognition image p (n-2) and the current visual recognition image p (n) Determine, from the visual recognition image p (n-2) To the visual recognition image p (n) There are two moves, therefore, Ry'=2×Ry, Rx'=2×Rx.

[0107] The information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.) and signals (including but not limited to signals transmitted between user terminals and other devices, etc.) involved in this application are all authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards of relevant countries and regions. For example, the "visual recognition image" and "motor shaft movement distance" involved in this disclosure are all obtained with full authorization.

[0108] The present invention discloses a pipeline visual processing anti-duplicate method, the method comprising: obtaining a visual recognition image p (n) ; According to the visual recognition image p (n) Determine the recognition data and the visual recognition coordinates of the recognition data; (n) and the previous visual recognition image p (n-1) Compare and determine the repeated recognition area as the overlapping area; filter and remove the recognition data corresponding to the visual recognition coordinates in the overlapping area; set the moving distance tolerance; based on the moving distance tolerance, respectively use the previous visual recognition image p (n-1) and the n-2th visual recognition image p (n-2) For visual recognition image p (n) The remaining identification data is screened, and the identification data that meets the two screening conditions is eliminated. The present invention avoids the problem of repeated processing without visual inspection, improves product inspection efficiency, and effectively prevents duplication when the on-site environment is harsh and visual inspection is not possible.

[0109] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0110] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0111] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0112] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the aforementioned storage medium includes: a mobile storage device, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0113] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

Claims

1. A method for preventing weight loss in pipeline visual processing, characterized in that: Including: Get the visual recognition image p (n) ; According to the visual recognition image p (n) determining identification data and visual identification coordinates of the identification data; The visual recognition image p (n) and the previous visual recognition image p (n-1) Comparison is performed and the repeated identification areas are determined as overlapping areas; Screening and eliminating the recognition data corresponding to the visual recognition coordinates within the overlapping area; Setting a movement distance tolerance; Based on the moving distance tolerance, the previous visual recognition image p is respectively (n-1) and the n-2th visual recognition image p (n-2) For visual recognition image p (n) The remaining identification data are screened, and the identification data that meets the two screening conditions are eliminated; The obtaining of the visual recognition image p (n) ,include: Setting a preset movement distance based on the length of the camera's field of view; Obtaining the movement distance of the motor shaft; When the motor shaft moves to a preset distance, the visual recognition image p is obtained. (n) ; The preset moving distance L satisfies: ; Where W is the length of the camera's field of view and D is the length of a single recognition data; The setting of the movement distance tolerance includes: Calculating the movement distance tolerance through the angle difference a between the camera and the motor shaft; the movement distance tolerance includes a horizontal tolerance Rx and a vertical tolerance Ry; The calculation method of the lateral tolerance Rx and the longitudinal tolerance Ry is expressed by the formula: ; .

2. The method for preventing weight loss in pipeline visual processing according to claim 1, characterized in that: Based on the moving distance tolerance, the last visual recognition image p is respectively (n-1) and the n-2th visual recognition image p (n-2) For visual recognition image p (n) The remaining identification data is screened and the identification data that meets the two screening conditions is eliminated, including: According to the previous visual recognition image p (n-1) The first theoretical coordinates of the recognition data are determined by the visual recognition coordinates of the recognition data and the preset moving distance, and the recognition data that meets the screening conditions are marked by analyzing the first theoretical coordinates and the visual recognition coordinates of the same recognition data; According to the n-2th visual recognition image p (n-2) The visual recognition coordinates of the recognition data and the preset moving distance are used to determine the second theoretical coordinates of the recognition data, and the recognition data that meets the screening conditions are marked by analyzing the second theoretical coordinates and the visual recognition coordinates of the same recognition data; Counting the annotation times of each recognition data and eliminating the recognition data with two annotation times.

3. The method for preventing weight loss in pipeline visual processing according to claim 2, characterized in that: The method according to the previous visual recognition image p (n-1) The first theoretical coordinates of the recognition data are determined by the visual recognition coordinates of the recognition data and the preset moving distance, and the recognition data that meets the screening conditions are marked by analyzing the first theoretical coordinates and the visual recognition coordinates of the same recognition data, including: The previous visual recognition image p (n-1) The center coordinates of the identified data are determined as the first reference coordinates (x0, y0); Determining the first theoretical coordinates (x1, y1) after the recognition data moves once according to the first reference coordinates (x0, y0) and the preset movement distance; Based on visual recognition image p (n) The visual recognition coordinates (x2, y2) of the recognition data are analyzed to determine whether the same recognition data meets the screening condition; the first distance tolerance is the moving distance tolerance (Rx, Ry); When both |x1 - x2| < Rx and |y1 - y2| < Ry are satisfied, it is determined that the same recognition data meets the screening conditions.

4. The method for preventing weight loss in pipeline visual processing according to claim 2, characterized in that: The n-2th visual recognition image p (n-2) The visual recognition coordinates of the recognition data and the preset moving distance are used to determine the second theoretical coordinates of the recognition data. By analyzing the second theoretical coordinates and the visual recognition coordinates of the same recognition data, the recognition data that meets the screening conditions are marked, including: The n-2th visual recognition image p (n-2) The center coordinates of the identified data are determined as the second reference coordinates (x0', y0'); Determining the second theoretical coordinates (x3, y3) after the recognition data moves twice according to the second reference coordinates (x0', y0') and the preset movement distance; Calculating the second distance tolerance (Rx', Ry') of the recognition data according to the movement distance tolerance; Based on visual recognition image p (n) The visual recognition coordinates (x2, y2) of the recognition data are analyzed, and the second theoretical coordinates (x3, y3), the visual recognition coordinates (x2, y2) and the second distance tolerance (Rx', Ry') of the same recognition data are analyzed to determine whether the same recognition data meets the screening condition; When both |x3 - x2| < Rx and |y3 - y2| < Ry are satisfied, it is determined that the same recognition data meets the screening conditions.

5. The method for preventing weight loss in pipeline visual processing according to claim 4, characterized in that: Also including: The calculation method of the second distance tolerance (Rx', Ry') is expressed by the formula: ; .

Images