Aluminum-plastic film defect intelligent detection system and method

Abstract

The invention discloses an intelligent detection system and method for aluminum-plastic film defects, belonging to the technical field of aluminum-plastic film detection. The system comprises a parameter recording module, an image acquisition module, a model management module, an intelligent adjustment module, a defect detection module and an interactive display module; the parameter recording module is used to record parameter data about the aluminum-plastic film; the image acquisition module is used to collect image data on the surface of the aluminum-plastic film; the model management module is used to establish a brightness analysis model and an accuracy analysis model to determine the optimal detection brightness of the image data; the intelligent adjustment module is used to determine the current image data acquisition distance and automatically adjust the brightness of the LED lamp; the defect detection module is used to analyze and process the collected image data and identify the defects of the aluminum-plastic film; the interactive display module is used to digitally display the aluminum-plastic film detection results through a human-computer interaction platform.

Description

Technical Field

[0001] The present invention relates to the technical field of aluminum-plastic film detection, and in particular to an intelligent detection system and method for aluminum-plastic film defects. Background Art

[0002] During the rapid development stage of new energy vehicles, the batteries used are directly related to the performance of new energy vehicles. There is a saying in the industry that whoever has the battery will have the world of new energy vehicles. Aluminum-plastic film is a key material widely used in lithium battery packaging, and its quality directly affects the performance and safety of the battery. Traditional manual inspection methods are inefficient, easily affected by subjective factors, and difficult to meet the needs of high precision and large-scale production. With the continuous advancement of machine vision, image processing technology and deep learning algorithms, intelligent inspection technology has emerged, providing an efficient and accurate solution for aluminum-plastic film defect detection.

[0003] In the process of aluminum-plastic film defect detection, due to the different sizes of aluminum-plastic films, the height of the camera needs to be adjusted without changing the camera parameters, so as to facilitate the acquisition of aluminum-plastic film images; but the change of camera height often changes the brightness of the acquired image data, affecting the image quality. At this time, a light source needs to be provided for backlighting. In the prior art, it is difficult to select appropriate light source intensity and parameters according to specific detection requirements and the characteristics of the aluminum-plastic film, which causes errors in the aluminum-plastic film defect detection results; when the number of aluminum-plastic film defects is too large, the operator cannot effectively evaluate the degree of each aluminum-plastic film defect, which affects the detection efficiency. Summary of the invention

[0004] The object of the present invention is to provide an intelligent detection system and method for aluminum-plastic film defects to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: an intelligent detection method for aluminum-plastic film defects, the method comprising the following steps:

[0006] S10, establishing a brightness analysis model, analyzing the brightness change of the aluminum-plastic film surface under different LED light brightness and aluminum-plastic film transmittance, and analyzing the brightness change of the collected image data under different aluminum-plastic film surface brightness and image data collection distance; establishing an accuracy analysis model, analyzing the accuracy change of aluminum-plastic film defect detection under different image data brightness, and determining the optimal detection brightness of the image data;

[0007] S20, the operator records parameter data about the aluminum-plastic film in the inspection instrument; the parameter data includes the size and transmittance of the aluminum-plastic film; the inspection instrument is used to detect defects of the aluminum-plastic film and collect image data of the surface of the aluminum-plastic film; according to the size of the aluminum-plastic film currently detected, the current image data collection distance is determined, and according to the current image data collection distance, the transmittance of the aluminum-plastic film currently detected, the determined optimal detection brightness of the image data, the established brightness analysis model and the accuracy analysis model, the brightness of the LED lamp is automatically adjusted;

[0008] S30, analyzing and processing the collected image data, identifying aluminum-plastic film defects, marking the aluminum-plastic film with defects, and sending the detection results to the human-computer interaction platform.

[0009] An aluminum-plastic film defect intelligent detection system, the system comprising a parameter recording module, an image acquisition module, a model management module, an intelligent adjustment module, a defect detection module and an interactive display module;

[0010] The parameter recording module is used to record parameter data about the aluminum-plastic film; the parameter data includes the size and light transmittance of the aluminum-plastic film; and the recorded parameter data is sent to the intelligent adjustment module;

[0011] The image acquisition module is used to collect image data of the surface of the aluminum-plastic film and send the collected image data to the defect detection module;

[0012] The model management module is used to establish a brightness analysis model to analyze the brightness changes of the aluminum-plastic film surface under different LED light brightness and aluminum-plastic film transmittance, and to analyze the brightness changes of the collected image data under different aluminum-plastic film surface brightness and image data collection distance; establish an accuracy analysis model to analyze the accuracy changes of aluminum-plastic film defect detection under different image data brightness, and determine the optimal detection brightness of the image data;

[0013] The intelligent adjustment module is used to determine the current image data acquisition distance according to the size of the aluminum-plastic film currently detected, and automatically adjust the brightness of the LED lamp according to the current image data acquisition distance, the determined optimal detection brightness of the image data, the established brightness analysis model and the accuracy analysis model;

[0014] The defect detection module is used to analyze and process the collected image data, identify the defects of the aluminum-plastic film, mark the aluminum-plastic film with defects, and send the detection results to the interactive display module;

[0015] The interactive display module is used to digitally display the aluminum-plastic film inspection result through a human-computer interaction platform; wherein the aluminum-plastic film inspection result includes the collected image data and the degree of aluminum-plastic film defects marked in the image data.

[0016] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: by establishing a brightness analysis model, the brightness change in the process of aluminum-plastic film image acquisition is analyzed; by establishing an accuracy analysis model, the influence of the brightness change on the accuracy of aluminum-plastic film defect detection is analyzed, and the optimal detection brightness parameter of the image data is determined; the brightness of the LED light is automatically adjusted according to the analysis result, so that the brightness of the image data collected by the camera is always maintained at the optimal detection brightness, thereby improving the quality of the collected image data and the accuracy of the intelligent detection of aluminum-plastic film defects; by detecting the defects of the aluminum-plastic film, marking the aluminum-plastic film at the defective position, and calculating the degree of the aluminum-plastic film defect at each marked position, it is convenient for the staff to evaluate the quality of the aluminum-plastic film. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic diagram of the steps of an intelligent detection method for aluminum-plastic film defects of the present invention;

[0018] Figure 2 is a graph showing the relationship between the brightness of image data and the detection accuracy of aluminum-plastic film defects in an embodiment of an aluminum-plastic film defect intelligent detection method of the present invention;

[0019] Figure 3 The invention discloses a structural schematic diagram of an intelligent detection system for aluminum-plastic film defects. DETAILED DESCRIPTION

[0020] 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.

[0021] See also Figure 1-3 , the present invention provides a technical solution:

[0022] See also Figure 1-2 In the first embodiment, an intelligent detection method for aluminum-plastic film defects is provided. The surface defects of the aluminum-plastic film are detected by an inspection instrument. An industrial CCD camera is set above the aluminum-plastic film. A high-brightness LED linear focusing cold light source is used to backlight the aluminum-plastic film. The LED brightness is automatically adjusted according to the analysis results. A linear array CCD camera and a high-speed image processing system are used to perform real-time online scanning to accurately detect whether there are scratches, pits, foreign matter, bubbles, and damaged areas of glue on the aluminum-plastic film. The manual visual method is replaced to improve the detection efficiency and accuracy, as well as extremely fine defects that cannot be observed by the naked eye. The method comprises the following steps:

[0023] It should be noted that the inspection instrument includes a CDD camera and an LED lamp; the CDD camera is mounted above the aluminum-plastic film to collect image data on the surface of the aluminum-plastic film; the LED lamp is installed below the aluminum-plastic film to backlight the aluminum-plastic film using a high-brightness LED linear focusing cold light source; wherein the height of the CDD camera and the brightness of the LED lamp can be adjusted; the inspection instrument is an aluminum-plastic film defect inspection instrument.

[0024] S10. Establish a brightness analysis model to analyze the brightness changes of the aluminum-plastic film surface under different LED lamp brightness and aluminum-plastic film transmittance, and analyze the brightness changes of the collected image data under different aluminum-plastic film surface brightness and image data collection distance; establish an accuracy analysis model to analyze the accuracy changes of aluminum-plastic film defect detection under different image data brightness, and determine the optimal detection brightness of the image data.

[0025] Specifically, the method steps are:

[0026] A brightness analysis model was established to determine the brightness R of the LED lamp and the transmittance E of the aluminum-plastic film. The brightness of the aluminum-plastic film surface was measured to determine the brightness B of the aluminum-plastic film surface. R and E were used as independent variables, and B was used as the dependent variable to analyze the changes in the brightness of the aluminum-plastic film surface under different LED lamp brightness and aluminum-plastic film transmittance, and the relationship curve of R, E and B was obtained:

[0027]

[0028] in, Indicates the transmission efficiency from LED light source to aluminum-plastic film;

[0029] Determine the straight-line distance h between the CDD camera and the aluminum-plastic film, and use h as the image data acquisition distance; determine the brightness Br of the image data collected by the CDD camera; use B and h as independent variables, and Br as the dependent variable, and analyze the changes in the brightness of the collected image data under different aluminum-plastic film surface brightness and image data acquisition distances based on the inverse square law of light, and obtain the relationship curve of B, h and Br:

[0030]

[0031] in, It indicates the conversion efficiency of the surface brightness of the aluminum-plastic film into the brightness of the image data.

[0032] It should be noted that the brightness analysis model was established by continuously conducting experiments to analyze the historical data of different LED lamp brightness, aluminum-plastic film transmittance and aluminum-plastic film surface brightness, and the determined R, E and B values ​​were substituted into the relationship curves. In the process of analyzing the historical data of different aluminum-plastic film surface brightness, image data acquisition distance and image data brightness, the determined B, h and Br values ​​are respectively substituted into the relationship curve In, confirm and The value of ; where and As the R, E and B values ​​change; in this embodiment, the LED light and the CDD camera are in the same vertical direction; since the position distance between the LED light and the aluminum-plastic film in the vertical direction is fixed, The value of includes the influence of the distance between the LED lamp and the aluminum-plastic film in the vertical direction, and The value of does not include the influence factor of the distance from the aluminum-plastic film surface to the CDD camera lens. At this time, the influence of the change in the distance from the aluminum-plastic film surface to the CDD camera lens on the brightness of the collected image data is considered through the inverse square law of light; the straight-line distance between the CDD camera and the aluminum-plastic film represents the distance from the aluminum-plastic film surface to the CDD camera lens.

[0033] It should be noted that, in this embodiment, a gloss meter is used to measure the brightness of the aluminum-plastic film surface; the brightness of the collected image data is determined according to the image analysis software; in the process of detecting the surface of the aluminum-plastic film, the distance between the aluminum-plastic film and the CDD camera will directly affect the quality of the image. According to the inverse square law of light, as the distance between the aluminum-plastic film and the CDD camera increases, the brightness of the collected image data will decrease. Therefore, by adjusting the LED light intensity, the brightness of the image data is guaranteed, and the clarity and accuracy of image acquisition are improved.

[0034] Establish an accuracy analysis model, select aluminum-plastic film samples, determine the number of defects and the corresponding degree of defects in the aluminum-plastic film samples, and obtain a1, a2, ..., a n ; The aluminum-plastic film samples are inspected by the inspection instrument, and the collected image data of the aluminum-plastic film samples are analyzed to determine the degree of defects of the aluminum-plastic film under different image data brightness. According to the calculation formula:

[0035]

[0036] Where n represents the number of defects of the aluminum-plastic film sample; a1, a2, ..., a n They represent the true values ​​of the defect levels of different aluminum-plastic film samples; They respectively represent the detection values ​​of the defect levels of different aluminum-plastic film samples detected by the inspection instrument when the image data brightness is Br; It indicates the detection value of the defect degree of the i-th aluminum-plastic film sample detected when the image data brightness is Br; represents the number of pixels occupied by the i-th aluminum-plastic film sample defect in the collected aluminum-plastic film sample image data when the image data brightness is Br; w represents the number of pixels occupied by the width of the aluminum-plastic film sample in the collected aluminum-plastic film sample image data;

[0037] According to a1, a2, ..., a n and Calculate the accuracy Z of aluminum-plastic film defect detection when the brightness of the collected image data is Br Br :

[0038]

[0039] Among them, a i Represents the true value of the defect degree of the i-th aluminum-plastic film sample; It represents the detection value of the defect degree of the i-th aluminum-plastic film sample detected by the inspection instrument when the brightness of the collected image data is Br; i=1, 2, ..., n;

[0040] According to Z Br , determine Z = max(Z Br ), the corresponding CDD camera collects the image data brightness Br0, and uses Br0 as the optimal detection brightness of the image data; where Br0∈Br; Z represents the accuracy of aluminum-plastic film defect detection.

[0041] It should be noted that an accuracy analysis model is established and aluminum-plastic film samples are selected for experimental analysis. To ensure the accuracy of the experimental analysis, multiple aluminum-plastic film samples can be selected to determine the optimal detection brightness of the image data corresponding to the multiple aluminum-plastic film samples, and the final optimal detection brightness can be determined by taking the average value.

[0042] It should be noted that the degree of defect is the proportion of the surface defect area of ​​the aluminum-plastic film at the corresponding size; is the number of pixels occupied, that is, the area of ​​the pixels occupied, (w)2 represents the square of the width, through Normalization processing is performed to put the judgment of the degree of defects of the aluminum-plastic film on the same scale; the aluminum-plastic film defects include but are not limited to scratches, pits, foreign matter, bubbles, and glue; aluminum-plastic film samples are selected to have defects and the degree of defects is determined.

[0043] It should be noted that in areas with high or low brightness, the accuracy of the image will show a downward trend; in areas with moderate brightness, the accuracy of the image is relatively stable and reaches a peak value, and the brightness of the image data and the accuracy of aluminum-plastic film defect detection conform to the normal distribution; and because the optimal detection brightness of the image data corresponding to the camera is determined without changing the camera parameters; therefore, in this implementation, by selecting aluminum-plastic film samples, the influence of different image data brightness on the accuracy of aluminum-plastic film defect detection is analyzed when other conditions are the same, and the variance is calculated. The cumulative summation reciprocal Z Br , Z Br The larger the value, the higher the accuracy; according to Z Br The curve, such as Figure 2 As shown, the corresponding image data brightness when the accuracy is maximum is determined, and the brightness is used as the optimal detection brightness of the image data; wherein the brightness Br of the collected image data is variable within a certain range.

[0044] S20, the operator records parameter data about the aluminum-plastic film in the inspection instrument; the parameter data includes the size and transmittance of the aluminum-plastic film; the inspection instrument is used to detect defects of the aluminum-plastic film and collect image data of the surface of the aluminum-plastic film; according to the detected size of the aluminum-plastic film, the current image data collection distance is determined, and according to the current image data collection distance, the currently detected transmittance of the aluminum-plastic film, the determined optimal detection brightness of the image data, the established brightness analysis model and the accuracy analysis model, the brightness of the LED lamp is automatically adjusted.

[0045] Specifically, the method and steps for automatically adjusting the brightness of the LED lamp are as follows:

[0046] According to the size of the aluminum-plastic film currently being detected, the height of the CDD camera is adjusted to determine the current straight-line distance h0 between the CDD camera and the aluminum-plastic film, and h0 is used as the current image data acquisition distance;

[0047] According to h0, Br0, the established brightness analysis model and accuracy analysis model, calculate the LED light brightness R0 that needs to be adjusted:

[0048]

[0049] Among them, E0 represents the light transmittance of the aluminum-plastic film currently being tested;

[0050] According to the calculated R0, the brightness of the LED light is automatically adjusted.

[0051] It should be noted that in this implementation, by analyzing real-time data, the area to be inspected is determined according to the size of the aluminum-plastic film currently being inspected, and the height of the CDD camera is adjusted according to the inspection area, so that all parts of the aluminum-plastic film currently being inspected are within the field of view of the CDD camera, and the optimal detection brightness of the calculated image data is combined with the current image data acquisition distance and the transmittance of the aluminum-plastic film. Through the brightness analysis model and the accuracy analysis model, the brightness value of the LED lamp that needs to be adjusted is inferred, and the brightness of the LED lamp is smoothly adjusted to the brightness value, so that the brightness of the collected image data is always maintained at the optimal detection brightness, thereby improving the quality of the collected image data.

[0052] S30, analyzing and processing the collected image data, identifying aluminum-plastic film defects, marking the aluminum-plastic film with defects, and sending the detection results to the human-computer interaction platform.

[0053] Furthermore, the defects of the aluminum-plastic film are identified, the defective aluminum-plastic film is marked in the collected image data, and the degree of defect of the aluminum-plastic film at each marked position is calculated; a human-computer interaction platform is provided to digitally display the aluminum-plastic film detection results; the aluminum-plastic film detection results include the collected image data and the degree of defect of the aluminum-plastic film marked in the image data.

[0054] In this embodiment, a linear array CCD camera and a high-speed image processing system are used to perform real-time online scanning, collect image data, splice the collected image data, establish an aluminum-plastic film defect recognition database, analyze the spliced ​​image data, and identify aluminum-plastic film defects; calculate the degree of aluminum-plastic film defects at each marked position, and the method steps are as follows: determine the number of pixels w0 occupied by the width of the aluminum-plastic film in the current aluminum-plastic film image data according to the current detected aluminum-plastic film size and the image data acquisition distance; respectively determine the number of pixels occupied by each aluminum-plastic film defect marked in the image data when the brightness of the current image data is Br0 Calculate the degree of aluminum-plastic film defects at each marked position when the current image data brightness is Br0 According to the calculation formula: in, Indicates the degree of aluminum-plastic film defects at the jth marked position detected when the brightness of the current image data is Br0; It indicates the number of pixels occupied by the j-th aluminum-plastic film defect marked when the brightness of the current image data is Br0; j=1, 2, ..., m; m indicates the number of aluminum-plastic film defects currently marked.

[0055] It should be noted that through The number of pixels occupied by aluminum-plastic film defects in the image data is divided by the number of square pixels occupied by the width of the aluminum-plastic film in the image data, that is, the proportion of the defect area of ​​the aluminum-plastic film is calculated, so as to normalize the degree of aluminum-plastic film defects at different image data collection distances, which is convenient for staff to evaluate the quality of the aluminum-plastic film.

[0056] See also Figure 3 , in this embodiment 2: a system for intelligent detection of aluminum-plastic film defects is provided, the system comprising a parameter recording module, an image acquisition module, a model management module, an intelligent adjustment module, a defect detection module and an interactive display module;

[0057] The parameter recording module is used to record parameter data about the aluminum-plastic film; the parameter data includes the size and light transmittance of the aluminum-plastic film; and the recorded parameter data is sent to the intelligent adjustment module;

[0058] The image acquisition module is used to collect image data of the surface of the aluminum-plastic film and send the collected image data to the defect detection module;

[0059] The model management module is used to establish a brightness analysis model to analyze the brightness changes of the aluminum-plastic film surface under different LED light brightness and aluminum-plastic film transmittance, and to analyze the brightness changes of the collected image data under different aluminum-plastic film surface brightness and image data collection distance; establish an accuracy analysis model to analyze the accuracy changes of aluminum-plastic film defect detection under different image data brightness, and determine the optimal detection brightness of the image data;

[0060] The intelligent adjustment module is used to determine the current image data acquisition distance according to the size of the aluminum-plastic film currently detected, and automatically adjust the brightness of the LED lamp according to the current image data acquisition distance, the determined optimal detection brightness of the image data, the established brightness analysis model and the accuracy analysis model;

[0061] The defect detection module is used to analyze and process the collected image data, identify the defects of the aluminum-plastic film, mark the aluminum-plastic film with defects, and send the detection results to the interactive display module;

[0062] The interactive display module is used to digitally display the aluminum-plastic film inspection result through a human-computer interaction platform; wherein the aluminum-plastic film inspection result includes the collected image data and the degree of aluminum-plastic film defects marked in the image data.

[0063] Furthermore, the model management module includes an aluminum-plastic film surface brightness analysis unit, an image brightness analysis unit, a defect degree calculation unit, and an optimal brightness calculation unit;

[0064] The aluminum-plastic film surface brightness analysis unit is used to determine the brightness of the LED lamp and the light transmittance of the aluminum-plastic film, measure the brightness of the aluminum-plastic film surface, and determine the brightness of the aluminum-plastic film surface; analyze the brightness change of the aluminum-plastic film surface under different LED lamp brightness and aluminum-plastic film transmittance;

[0065] The CDD camera brightness analysis unit is used to determine the straight-line distance between the CDD camera and the aluminum-plastic film, and use it as the image data acquisition distance to determine the brightness of the image data collected by the CDD camera; based on the inverse square ratio of light, analyze the brightness changes of the collected image data under different aluminum-plastic film surface brightness and image data acquisition distance;

[0066] The defect degree calculation unit is used to perform defect detection on the aluminum-plastic film sample through an inspection instrument, analyze the collected image data of the aluminum-plastic film sample, and determine the defect degree of the aluminum-plastic film detected under different image data brightness;

[0067] The optimal brightness calculation unit is used to calculate the accuracy of aluminum-plastic film defect detection when the brightness of the collected image data is different; and determine the optimal detection brightness of the image data.

[0068] Furthermore, the intelligent adjustment module includes a CDD camera height adjustment unit, an LED light brightness calculation unit and an automatic brightness adjustment unit;

[0069] The CDD camera height adjustment unit is used to adjust the height of the CDD camera according to the size of the aluminum-plastic film currently detected, determine the current straight-line distance between the CDD camera and the aluminum-plastic film, and use it as the current image data acquisition distance;

[0070] The LED lamp brightness calculation unit is used to calculate the LED lamp brightness that needs to be adjusted according to the current image data acquisition distance, the determined optimal detection brightness of the image data, the established brightness analysis model and the accuracy analysis model;

[0071] The automatic brightness adjustment unit is used to automatically adjust the brightness of the LED lamp according to the calculated brightness of the LED lamp.

[0072] In this embodiment:

[0073] The parameter recording module is interconnected with the intelligent adjustment module; the model management module is interconnected with the intelligent adjustment module; the intelligent adjustment module is interconnected with the defect detection module; the image acquisition module is interconnected with the defect detection module; the defect detection module is interconnected with the interactive display module.

[0074] The aluminum-plastic film surface brightness analysis unit is interconnected with the image brightness analysis unit; the defect degree calculation unit is interconnected with the optimal brightness calculation unit; the CDD camera height adjustment unit is interconnected with the LED light brightness calculation unit; the LED light brightness calculation unit is interconnected with the brightness automatic adjustment unit.

[0075] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An intelligent detection method for aluminum-plastic film defects, characterized in that: The method comprises the following steps: S10, establishing a brightness analysis model, analyzing the brightness change of the aluminum-plastic film surface under different LED light brightness and aluminum-plastic film transmittance, and analyzing the brightness change of the collected image data under different aluminum-plastic film surface brightness and image data collection distance; establishing an accuracy analysis model, analyzing the accuracy change of aluminum-plastic film defect detection under different image data brightness, and determining the optimal detection brightness of the image data; S20, the operator records parameter data about the aluminum-plastic film in the inspection instrument; the parameter data includes the size and transmittance of the aluminum-plastic film; the inspection instrument is used to detect defects of the aluminum-plastic film and collect image data of the surface of the aluminum-plastic film; according to the detected size of the aluminum-plastic film, the current image data collection distance is determined, and according to the current image data collection distance, the currently detected transmittance of the aluminum-plastic film, the determined optimal detection brightness of the image data, the established brightness analysis model and the accuracy analysis model, the brightness of the LED lamp is automatically adjusted; S30, analyzing and processing the collected image data, identifying aluminum-plastic film defects, marking the aluminum-plastic film with defects, and sending the detection results to the human-computer interaction platform; The inspection instrument comprises a CDD camera and an LED lamp; the CDD camera is mounted above the aluminum-plastic film to collect image data on the surface of the aluminum-plastic film; the LED lamp is installed below the aluminum-plastic film to backlight the aluminum-plastic film with a high-brightness LED linear focusing cold light source; the height of the CDD camera and the brightness of the LED lamp can be adjusted.

2. The method for intelligently detecting aluminum-plastic film defects according to claim 1, characterized in that: The method steps of step S10 are: S101, establish a brightness analysis model, determine the LED light brightness R and the aluminum-plastic film transmittance E respectively, measure the brightness of the aluminum-plastic film surface, and determine the aluminum-plastic film surface brightness B; take R and E as independent variables, and B as a dependent variable, analyze the brightness change of the aluminum-plastic film surface under different LED light brightness and aluminum-plastic film transmittance, and obtain the relationship curve of R, E and B: in, Indicates the transmission efficiency from LED light source to aluminum-plastic film; Determine the straight-line distance h between the CDD camera and the aluminum-plastic film, and use h as the image data acquisition distance; determine the brightness Br of the image data collected by the CDD camera; use B and h as independent variables, and Br as the dependent variable, and analyze the changes in the brightness of the collected image data under different aluminum-plastic film surface brightness and image data acquisition distances based on the inverse square law of light, and obtain the relationship curve of B, h and Br: in, Indicates the conversion efficiency of the aluminum-plastic film surface brightness into image data brightness; S102, establish an accuracy analysis model, select aluminum-plastic film samples, determine the number of defects and the corresponding degree of defects of the aluminum-plastic film samples, and obtain a1, a2, ..., a n ; The aluminum-plastic film samples are inspected by the inspection instrument, and the collected image data of the aluminum-plastic film samples are analyzed to determine the degree of defects of the aluminum-plastic film under different image data brightness. According to the calculation formula: Where n represents the number of defects of the aluminum-plastic film sample; a1, a2, ..., a n They represent the true values ​​of the defect levels of different aluminum-plastic film samples; They respectively represent the detection values ​​of the defect levels of different aluminum-plastic film samples detected by the inspection instrument when the image data brightness is Br; It indicates the detection value of the defect degree of the i-th aluminum-plastic film sample detected when the image data brightness is Br; represents the number of pixels occupied by the i-th aluminum-plastic film sample defect in the collected aluminum-plastic film sample image data when the image data brightness is Br; w represents the number of pixels occupied by the width of the aluminum-plastic film sample in the collected aluminum-plastic film sample image data; According to a1, a2, ..., a n and Calculate the accuracy Z of aluminum-plastic film defect detection when the brightness of the collected image data is Br Br : Among them, a i Represents the true value of the defect degree of the i-th aluminum-plastic film sample; It represents the detection value of the defect degree of the i-th aluminum-plastic film sample detected by the inspection instrument when the brightness of the collected image data is Br; i=1, 2, ..., n; According to Z Br , determine Z = max(Z Br ), the corresponding CDD camera collects the image data brightness Br0, and uses Br0 as the optimal detection brightness of the image data; where Br0∈Br; Z represents the accuracy of aluminum-plastic film defect detection.

3. The method for intelligently detecting aluminum-plastic film defects according to claim 2, characterized in that: In step S20, the method steps for automatically adjusting the brightness of the LED lamp are: S201, adjusting the height of the CDD camera according to the size of the aluminum-plastic film currently being detected, determining the current straight-line distance h0 between the CDD camera and the aluminum-plastic film, and using h0 as the current image data acquisition distance; S202, according to h0, Br0, the established brightness analysis model and accuracy analysis model, calculate the LED light brightness R0 that needs to be adjusted: Among them, E0 represents the light transmittance of the aluminum-plastic film currently being tested; S203: Automatically adjust the brightness of the LED lamp according to the calculated R0.

4. The method for intelligently detecting aluminum-plastic film defects according to claim 3, characterized in that: Identify aluminum-plastic film defects, mark the aluminum-plastic film with defects in the collected image data, and calculate the degree of aluminum-plastic film defects at each marked position; provide a human-computer interaction platform to digitally display the aluminum-plastic film detection results; the aluminum-plastic film detection results include the collected image data and the degree of aluminum-plastic film defects marked in the image data.

5. An intelligent detection system for aluminum-plastic film defects, characterized in that: The system includes a parameter recording module, an image acquisition module, a model management module, an intelligent adjustment module, a defect detection module and an interactive display module; The parameter recording module is used to record parameter data about the aluminum-plastic film in the inspection instrument; the parameter data includes the size and light transmittance of the aluminum-plastic film; and the recorded parameter data is sent to the intelligent adjustment module; The image acquisition module is used to collect image data of the surface of the aluminum-plastic film and send the collected image data to the defect detection module; The model management module is used to establish a brightness analysis model, analyze the brightness change of the aluminum-plastic film surface under different LED light brightness and aluminum-plastic film transmittance, and analyze the brightness change of the collected image data under different aluminum-plastic film surface brightness and image data collection distance; Establish an accuracy analysis model to analyze the accuracy changes of aluminum-plastic film defect detection under different image data brightness, and determine the optimal detection brightness of image data; The intelligent adjustment module is used to determine the current image data acquisition distance according to the size of the aluminum-plastic film currently detected, and automatically adjust the brightness of the LED lamp according to the current image data acquisition distance, the determined optimal detection brightness of the image data, the established brightness analysis model and the accuracy analysis model; The defect detection module is used to analyze and process the collected image data, identify the defects of the aluminum-plastic film, mark the aluminum-plastic film with defects, and send the detection results to the interactive display module; The interactive display module is used to digitally display the aluminum-plastic film inspection results through a human-computer interaction platform; wherein the aluminum-plastic film inspection results include the collected image data and the degree of aluminum-plastic film defects marked in the image data; The inspection instrument comprises a CDD camera and an LED lamp; the CDD camera is mounted above the aluminum-plastic film to collect image data on the surface of the aluminum-plastic film; the LED lamp is installed below the aluminum-plastic film to backlight the aluminum-plastic film with a high-brightness LED linear focusing cold light source; the height of the CDD camera and the brightness of the LED lamp can be adjusted.

6. The intelligent detection system for aluminum-plastic film defects according to claim 5 is characterized in that: The model management module includes an aluminum-plastic film surface brightness analysis unit, an image brightness analysis unit, a defect degree calculation unit, and an optimal brightness calculation unit; The aluminum-plastic film surface brightness analysis unit is used to determine the brightness of the LED lamp and the light transmittance of the aluminum-plastic film, measure the brightness of the aluminum-plastic film surface, and determine the brightness of the aluminum-plastic film surface; Analyze the brightness changes of the aluminum-plastic film surface under different LED light brightness and aluminum-plastic film transmittance; The image brightness analysis unit is used to determine the straight-line distance between the CDD camera and the aluminum-plastic film, and use it as the image data acquisition distance to determine the brightness of the image data collected by the CDD camera; based on the inverse square of light, analyze the brightness changes of the collected image data under different aluminum-plastic film surface brightness and image data acquisition distance; The defect degree calculation unit is used to perform defect detection on the aluminum-plastic film sample through an inspection instrument, analyze the collected image data of the aluminum-plastic film sample, and determine the defect degree of the aluminum-plastic film detected under different image data brightness; The optimal brightness calculation unit is used to calculate the accuracy of aluminum-plastic film defect detection when the brightness of the collected image data is different; and determine the optimal detection brightness of the image data.

7. The intelligent detection system for aluminum-plastic film defects according to claim 6 is characterized in that: The intelligent adjustment module includes a CDD camera height adjustment unit, an LED light brightness calculation unit and an automatic brightness adjustment unit; The CDD camera height adjustment unit is used to adjust the height of the CDD camera according to the size of the aluminum-plastic film currently detected, determine the current straight-line distance between the CDD camera and the aluminum-plastic film, and use it as the current image data acquisition distance; The LED lamp brightness calculation unit is used to calculate the LED lamp brightness that needs to be adjusted according to the current image data acquisition distance, the determined optimal detection brightness of the image data, the established brightness analysis model and the accuracy analysis model; The automatic brightness adjustment unit is used to automatically adjust the brightness of the LED lamp according to the calculated brightness of the LED lamp.

Images