A glass fiber cloth production quality detection method and system

Abstract

The present invention relates to the field of image processing technology, and discloses a method and system for detecting the quality of glass fiber cloth production, wherein the method comprises: utilizing a multi-scale texture decomposition algorithm and a dynamic light and shadow consistency analysis technique to separate the texture signals of the fiber interwoven region and extract the candidate regions for holes; combining a time series analysis model to calculate the dynamic change characteristics of the candidate regions, and accurately determining the holes through a reliability scoring model. Compared with the prior art, which has insufficient detection accuracy due to the complex texture and light and shadow interference in the production of glass fiber cloth, and is particularly problematic in the high-speed production environment of glass fiber cloth, and cannot effectively distinguish hole defects from normal textures on the glass fiber cloth, the present application significantly improves the detection accuracy of hole defects through the use of a multi-scale texture decomposition algorithm and a reliability scoring model, thereby avoiding the problems of false detection and missed detection, and improving the quality control efficiency and detection reliability of glass fiber cloth production.

Description

Technical Field

[0001] The present invention belongs to the technical field of image processing, and in particular relates to a method and system for detecting the production quality of glass fiber cloth. Background Art

[0002] Currently, the detection of hole defects in glass fiber cloth production quality inspection primarily relies on traditional static image analysis methods or single-spectrum imaging technology. These methods suffer from several shortcomings. For example, due to the complex surface texture and significant light and shadow variations of glass fiber cloth, traditional methods cannot effectively distinguish hole defects from normal fiber interweaving structures. This is especially true when the holes are small and the edge features are blurred, which can easily lead to misjudgments or missed detections. Furthermore, in high-speed production environments, dynamic blur and reflected light interference caused by cloth movement further reduce detection accuracy. Existing detection technologies also lack dynamic analysis methods for multi-frame image sequences, making it difficult to reliably determine hole defects using information in the temporal dimension. Therefore, a method that combines multispectral imaging technology, multi-scale texture decomposition, light and shadow consistency analysis, and dynamic time series modeling is urgently needed to accurately identify hole defects and generate reliable quality assessment results under complex texture interference and high-speed production conditions, thereby improving the accuracy and real-time performance of glass fiber cloth production quality inspection. Summary of the Invention

[0003] In response to the above-mentioned technical deficiencies, the purpose of the present invention is to propose a method for inspecting the production quality of glass fiber cloth, aiming to solve the technical problem in the prior art of insufficient inspection accuracy due to the complex texture and light and shadow interference in the production of glass fiber cloth, especially in the high-speed production environment of glass fiber cloth, which cannot effectively distinguish between hole defects and normal texture on the glass fiber cloth.

[0004] In order to solve the above technical problems, the present invention adopts the following technical solutions: The present invention provides a method for detecting the production quality of glass fiber cloth.

[0005] The glass fiber cloth production quality detection method comprises:

[0006] Step S10: collecting multi-channel image data of the glass fiber cloth surface by multispectral imaging;

[0007] The multi-channel image data includes:

[0008] Visible light image L v , used to obtain fiber surface texture characteristics;

[0009] Infrared image L ir , used to obtain thermal radiation characteristics;

[0010] Ultraviolet image L uv , used to obtain the microscopic abnormal characteristics of materials;

[0011] Perform geometric calibration and registration on the multi-channel image data to generate a fused image L f (x,y), where x is the horizontal coordinate of the image pixel and y is the vertical coordinate of the image pixel;

[0012] Step S20: fusion image L f (x,y) performs optical property correction, including:

[0013] From the fused image L f (x, y) to obtain the reflected light intensity R(x, y), define the area where the reflected light intensity R(x, y) is greater than the preset reflected light intensity threshold as a high-reflection area, and perform brightness compensation on the high-reflection area to obtain the first optimized fusion image L c (x,y);

[0014] For the first optimized fusion image L c (x, y) is processed with grayscale equalization to obtain the second optimized fusion image L n (x,y);

[0015] Step S30: Use a multi-scale texture decomposition algorithm to decompose the second optimized fusion image L n (x, y) is processed to decompose the texture signal of the fiber interweaving area into:

[0016] Low-frequency texture feature T1(x,y), used to represent local uniformity;

[0017] High-frequency abnormal characteristics T h (x,y), used to represent the characteristics of the hole;

[0018] For high frequency abnormal features T h (x, y) applies the hole template matching algorithm to calculate the hole template matching value R(x, y), retains the pixel coordinates of the hole template matching value R(x, y) greater than the preset candidate threshold, and then generates the candidate area; calculates the light and shadow consistency score S for the extracted candidate area g (x,y), and remove the light and shadow consistency score S g Pixels whose (x, y) is lower than the preset light and shadow consistency score threshold are then used to generate optimized hole candidate areas;

[0019] Step S40: Obtaining image sequence data L of cloth movement t (x, y), to optimize the hole candidate area, use the time series analysis model combined with the image sequence data L of the cloth movement t (x,y), calculate the time variation variance of the optimized hole candidate area Combined time-varying variance The reliability score P of the hole is calculated using the reliability score model. defect (x,y), the formula is:

[0020]

[0021] Among them, μ is the time feature mean of the optimized hole candidate area, and exp is the exponential function;

[0022] Step S50: According to the reliability score P defect (x, y), determine whether the optimized hole candidate area is a hole defect and output the glass fiber cloth production quality inspection report, including the hole location, hole size and hole reliability score.

[0023] Preferably, in step S20, the first optimized fusion image L c The formula for (x,y) is:

[0024] L c (x,y)=L f (x,y)-αR(x,y)

[0025] Wherein, α is the light compensation coefficient of the high-reflective area, which is determined by the optical properties of the glass fiber cloth.

[0026] 3. A glass fiber cloth production quality detection method according to claim 1, characterized in that in step S30, the low-frequency texture feature T l (x,y) and high-frequency abnormal features T h (x,y) satisfies the formula L n (x,y)=T l (x,y)+T h (x,y).

[0027] Preferably, in step S30, the low-frequency texture feature T l (x,y) and high-frequency abnormal features T h (x,y) satisfies the formula L n (x,y)=T l (x,y)+T h (x,y).

[0028] Preferably, in step S30, the calculation formula of the hole template matching value R(x,y) is:

[0029]

[0030] Where W(i,j) is the hole template weight coefficient matrix, m and n are the preset horizontal and vertical pixel coordinate sizes of the hole template, i is the horizontal offset of the pixel relative to the center point (x, y) in the filter window, and the unit is pixel, j is the vertical offset of the pixel relative to the center point (x, y) in the filter window, and the unit is pixel.

[0031] Preferably, in step S30, the light and shadow consistency score S g The formula for (x,y) is:

[0032]

[0033] Where i is the horizontal offset of the pixel relative to the center point (x, y) in the filter window, in pixels, j is the vertical offset of the pixel relative to the center point (x, y) in the filter window, in pixels, L n (x+i,y+j) is the grayscale value of the pixel coordinate (x+i,y+j), is the window mean grayscale value, N w is the number of pixels in the window.

[0034] Preferably, in step S20, the second optimized fusion image L n The formula for (x,y) is:

[0035]

[0036] Among them, L min and L max are the preset minimum grayscale value and the preset maximum grayscale value, L scale is the preset target grayscale range.

[0037] Preferably, in step S40, the time variation variance of the hole candidate region is optimized The calculation formula is:

[0038]

[0039] Where t is the time frame, T is the total number of frames in the time series of the image sequence data of the cloth movement, is the time mean of the image sequence data of cloth motion.

[0040] The present invention also provides a glass fiber cloth production quality detection system comprising:

[0041] A multispectral imaging module, used for collecting multi-channel image data of the glass fiber cloth surface through a multispectral imaging method;

[0042] The multi-channel image data includes:

[0043] Visible light image Lv , used to obtain fiber surface texture characteristics;

[0044] Infrared image L ir , used to obtain thermal radiation characteristics;

[0045] Ultraviolet image L uv , used to obtain the microscopic abnormal characteristics of materials;

[0046] Perform geometric calibration and registration on the multi-channel image data to generate a fused image L f (x,y), where x is the horizontal coordinate of the image pixel and y is the vertical coordinate of the image pixel;

[0047] Optical property correction module, used to correct the fusion image L f (x,y) performs optical property correction, including:

[0048] From the fused image L f (x, y) to obtain the reflected light intensity R(x, y), define the area where the reflected light intensity R(x, y) is greater than the preset reflected light intensity threshold as a high-reflection area, and perform brightness compensation on the high-reflection area to obtain the first optimized fusion image L c (x,y);

[0049] For the first optimized fusion image L c (x, y) is processed with grayscale equalization to obtain the second optimized fusion image L n (x,y);

[0050] The multi-scale texture analysis module is used to use the multi-scale texture decomposition algorithm to analyze the second optimized fusion image L n (x, y) is processed to decompose the texture signal of the fiber interweaving area into:

[0051] Low-frequency texture feature T l (x,y), used to represent local uniformity;

[0052] High-frequency abnormal characteristics T h (x,y), used to represent the characteristics of the hole;

[0053] For high frequency abnormal features T h (x, y) applies the hole template matching algorithm to calculate the hole template matching value R(x, y), retains the pixel coordinates of the hole template matching value R(x, y) greater than the preset candidate threshold, and then generates the candidate area; calculates the light and shadow consistency score S for the extracted candidate area g (x,y), and remove the light and shadow consistency score S g Pixels whose (x, y) is lower than the preset light and shadow consistency score threshold are then used to generate optimized hole candidate areas;

[0054] Time series analysis module, used to obtain image sequence data L of cloth movement t (x, y), to optimize the hole candidate area, use the time series analysis model combined with the image sequence data L of the cloth movement t (x,y), calculate the time variation variance of the optimized hole candidate area Combined time-varying variance The reliability score P of the hole is calculated using the reliability score model. defect (x,y0, the formula is:

[0055]

[0056] Among them, μ is the time feature mean of the optimized hole candidate area, and exp is the exponential function;

[0057] Report generation module, used to score P according to reliability defect (x, y), determine whether the optimized hole candidate area is a hole defect and output the glass fiber cloth production quality inspection report, including the hole location, hole size and hole reliability score.

[0058] The present invention also provides a glass fiber cloth production quality inspection device, comprising a memory, a processor, and a glass fiber cloth production quality inspection program stored in the memory and runnable on the processor. When the glass fiber cloth production quality inspection program is executed by the processor, the glass fiber cloth production quality inspection method is implemented.

[0059] The present invention also provides a computer program product, including a glass fiber cloth production quality detection program, which implements the glass fiber cloth production quality detection method when executed by a processor.

[0060] The beneficial effects of the present invention are: compared with the prior art, which has insufficient detection accuracy due to the complex texture and light and shadow interference in the production of glass fiber cloth, especially in the high-speed production environment of glass fiber cloth, and cannot effectively distinguish between hole defects and normal textures on the glass fiber cloth, the present application significantly improves the detection accuracy of hole defects through the use of multi-scale texture decomposition algorithm and reliability scoring model, thereby avoiding the problems of false detection and missed detection, and improving the quality control efficiency and detection reliability of glass fiber cloth production. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0062] Figure 1 The figure is a flow chart of a first embodiment of a method for inspecting the production quality of glass fiber cloth according to the present invention.

[0063] Figure 2 The figure is a schematic diagram of equipment for a method for detecting the production quality of glass fiber cloth according to the present invention. DETAILED DESCRIPTION

[0064] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0065] Example 1: Figure 1 FIG. 1 is a flow chart of a first embodiment of a method for inspecting the quality of glass fiber cloth production according to the present invention, and provides a first embodiment of a method for inspecting the quality of glass fiber cloth production according to the present invention.

[0066] In a first embodiment, the glass fiber cloth production quality inspection method includes:

[0067] Step S10: collecting multi-channel image data of the glass fiber cloth surface by multispectral imaging;

[0068] The multi-channel image data includes:

[0069] Visible light image L v , used to obtain fiber surface texture characteristics;

[0070] Infrared image L ir , used to obtain thermal radiation characteristics;

[0071] Ultraviolet image L uv , used to obtain the microscopic abnormal characteristics of materials;

[0072] Perform geometric calibration and registration on the multi-channel image data to generate a fused image L f (x,y), where x is the horizontal coordinate of the image pixel and y is the vertical coordinate of the image pixel;

[0073] It should be noted that the multispectral imaging method obtains the visible light image L v , infrared light image L ir and UV image L uv , which can capture the texture characteristics, thermal radiation characteristics and microscopic abnormal characteristics of the glass fiber cloth surface in different spectral ranges. Compared with single-spectral imaging, the technical advantage of multi-spectral imaging is that it can integrate the data of each spectral channel to provide more comprehensive and diverse feature information.

[0074] It can be understood that the fused image L f (x,y) is generated by geometrically calibrating and registering multi-channel image data. During the calibration process, the spatial distortion of the image is effectively corrected, while registration ensures that the data of each spectral channel are aligned in the same coordinate system. This fused image can provide a unified feature basis for subsequent texture decomposition and defect detection.

[0075] It should be understood that in practical applications, data from different spectral channels have specific functions: visible light image L v Provides a direct representation of surface texture, infrared image L ir It can reveal the material anomaly caused by the thermal conductivity of glass fiber cloth, and the ultraviolet image L uv It can amplify subtle abnormal reactions on the surface of the material. The diversity of this information effectively improves the sensitivity and accuracy of defect detection.

[0076] For example, when inspecting a batch of glass fiber cloth, the visible light image L v Shows the local unevenness of fiber interweaving texture; infrared image L ir This area shows abnormal thermal radiation characteristics, indicating possible material damage; while the ultraviolet image L uv It further confirmed that there was a weak abnormal reflection characteristic at the edge of the hole in this area. By fusing the three spectral data into L f (x, y), the system can clearly identify the boundaries and positions of the holes, avoiding missed detections due to insufficient single spectrum.

[0077] Step S20: fusion image L f (x,y) performs optical property correction, including:

[0078] From the fused image L f (x, y) to obtain the reflected light intensity R(x, y), and the reflected light intensity R(x, y) is greater than the preset reflected light intensity threshold T r The area is defined as a high-reflection area, and brightness compensation is performed on the high-reflection area to obtain the first optimized fusion image L c (x,y);

[0079] For the first optimized fusion image L c (x, y) is processed with grayscale equalization to obtain the second optimized fusion image L n (x,y);

[0080] It should be noted that for the fused image L f The purpose of optical property correction of (x, y) is to reduce the interference of high reflective areas on subsequent texture decomposition and hole identification. The high value area of reflected light intensity R(x, y) is prone to cause local overexposure, which makes the real texture features of the fiber surface obscured. Therefore, for reflected light intensity R(x, y)>T r Brightness compensation for highly reflective areas can effectively balance the brightness distribution of the image and reduce the loss of image details.

[0081] It can be understood that the first optimized fusion image L c (x, y) is obtained by compensating for the brightness difference of the high-reflective area. In the brightness compensation process, the compensation coefficient α determines the degree of weakening of the reflected light intensity R(x, y). Its value needs to be set according to the reflective characteristics of the material surface. In this way, L c (x,y) retains more fiber texture details and provides a more balanced image basis for subsequent equalization processing.

[0082] It should be understood that the main function of grayscale equalization is to enhance the contrast of the image and avoid the loss of important defect features caused by excessively high or low local brightness. c (x, y) is normalized to generate the second optimized fusion image L n (x,y) can ensure that texture features are evenly distributed throughout the image, providing high-quality data input for subsequent texture decomposition and feature extraction.

[0083] For example, in a certain glass fiber cloth area, the reflected light intensity R(x,y) exceeds the threshold value T r =200. In the uncorrected fused image, the high reflective area causes the hole edge to be blurred. After brightness compensation, the generated L c (x,y) Adjust the grayscale value of the high reflective area to below T r , thus restoring the texture features of the region, and then c After grayscale equalization processing of (x,y), the generated L n (x,y) has uniform brightness across the entire image, ensuring that the texture at the edge of the hole is clearly visible.

[0084] Step S30: Use a multi-scale texture decomposition algorithm to decompose the second optimized fusion image L n(x, y) is processed to decompose the texture signal of the fiber interweaving area into:

[0085] Low-frequency texture feature T l (x,y), used to represent local uniformity;

[0086] High-frequency abnormal characteristics T h (x,y), used to represent the characteristics of the hole;

[0087] For high frequency abnormal features T h (x, y) applies the hole template matching algorithm to calculate the hole template matching value R(x, y), retains the pixel coordinates of the hole template matching value R(x, y) greater than the preset candidate threshold, and then generates the candidate area; calculates the light and shadow consistency score S for the extracted candidate area g (x,y), and remove the light and shadow consistency score S g Pixels whose (x, y) is lower than the preset light and shadow consistency score threshold are then used to generate optimized hole candidate areas;

[0088] It should be noted that the texture signal of the fiber interweaving area is decomposed into low-frequency texture features T by the multi-scale texture decomposition method. l (x,y) and high-frequency abnormal features T h (x,y), which helps to separate the uniform characteristics of the fiber surface and the abnormal characteristics of the holes, and the low-frequency characteristics T l (x, y) represents the uniform brightness distribution of the local area, which mainly retains the overall texture of the glass fiber cloth surface, and the high-frequency feature T h (x, y) highlights the edge and detail characteristics of the hole area, providing an accurate feature basis for subsequent defect extraction.

[0089] It is understandable that the hole template matching algorithm is used to find the high-frequency abnormal features T h Search for areas similar to the hole features in (x, y). The template matching value R(x, y) is a quantitative indicator of the similarity between the high-frequency signal and the hole feature template. By retaining pixels whose R(x, y) is greater than the preset candidate threshold and generating candidate areas, the interference of irrelevant textures on defect detection can be effectively reduced, providing preliminary hole feature segmentation for subsequent steps.

[0090] It should be understood that the light and shadow consistency score S g (x,y) is an indicator calculated based on the light and shadow changes in the candidate area, which is used to further optimize the boundary of the hole area. Pixels with low light and shadow consistency scores are usually caused by noise or pseudo-defects. By eliminating these low-scoring points, the optimized hole candidate area can more accurately reflect the shape and location of the actual hole defect.

[0091] For example, the high-frequency abnormal characteristics T of a fiber interweaving areah (x, y) shows the location of potential holes. During the template matching process, the hole template matching value R(x, y) exceeds the threshold T h = 0.8 pixels are retained as candidate regions. However, in the light and shadow consistency calculation, the light and shadow consistency score S of some regions is g (x,y) is below the threshold T g =0.5, indicating that these areas may be affected by light and shadow noise. By eliminating these low-score points, the optimized hole candidate region generated can accurately identify the boundary and range of the hole.

[0092] Step S40: Obtaining image sequence data L of cloth movement t (x, y), to optimize the hole candidate area, use the time series analysis model combined with the image sequence data L of the cloth movement t (x,y), calculate the time variation variance of the optimized hole candidate area Combined time-varying variance The reliability score P of the hole is calculated using the reliability score model. defect (x,y), the formula is:

[0093]

[0094] Among them, μ is the time feature mean of the optimized hole candidate area, and exp is the exponential function;

[0095] It should be noted that the image sequence data L of the cloth movement t (x,y), combined with the time series analysis model, the time variation variance of the optimized hole candidate area can be calculated The temporal variation variance reflects the dynamic stability of the candidate region in the image sequence. For the hole region, the temporal variation is small and shows a consistent feature; while the pseudo-defect region is usually affected by light or noise, resulting in a larger temporal variation variance. Through this analysis, real holes and pseudo-defect regions can be effectively distinguished.

[0096] It is understandable that the reliability score P defect (x, y) is an indicator calculated based on the time column change, which is used to evaluate the possibility that a candidate area is a hole defect. In the scoring model, the mean μ and variance of the time change feature are The impact on the scoring results is greater. Due to the stability of the dynamic characteristics of the real hole area, its reliability score P defect (x,y) approaches 1, while the pseudo-defect area has a score close to 0 due to its large variance.

[0097] For example, assuming that the temporal feature mean of a candidate region is μ = 120, its temporal variation variance is The image index value of this area in a certain frame is L t (x,y)=122. According to the formula, the reliability score of this area is:

[0098]

[0099] A lower score indicates that the area may be a false defect and will ultimately be rejected.

[0100] Step S50: According to the reliability score P defect (x, y), determine whether the optimized hole candidate area is a hole defect and output the glass fiber cloth production quality inspection report, including the hole location, hole size and hole reliability score.

[0101] It should be noted that the glass fiber cloth production quality inspection report contains key information on hole defects, including the hole location, which is accurately marked by image coordinates (x, y); the hole size, which is estimated based on the number of image points in the candidate area; and the hole reliability score, which indicates the detection credibility of each hole defect, facilitating quality analysis and grading.

[0102] Furthermore, the present invention provides a glass fiber cloth production quality inspection system that utilizes the glass fiber cloth production quality inspection method described in the aforementioned embodiment, thereby resolving the technical issues surrounding glass fiber cloth production quality inspection. Compared to the prior art, the present invention provides the same beneficial effects as the glass fiber cloth production quality inspection method described in the aforementioned embodiment. Other technical features of the present invention are the same as those disclosed in the aforementioned embodiment and are not further elaborated upon here.

[0103] The present invention provides a glass fiber cloth production quality inspection equipment, please refer to Figure 2A glass fiber cloth production quality inspection device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can perform a glass fiber cloth production quality inspection method in the above-mentioned embodiment 1. A glass fiber cloth production quality inspection device in the embodiment of the present invention may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. A glass fiber cloth production quality inspection device is merely an example and should not impose any limitations on the functions and scope of use of the embodiments of the present invention. A glass fiber cloth production quality inspection device may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes based on programs stored in a read-only memory (ROM) 1002 or programs loaded from a storage device 1003 into a random access memory (RAM) 1004. RAM 1004 also stores various programs and data required for the operation of the glass fiber cloth production quality inspection device. Processing device 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input device 1007 including, for example, a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output device 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage device 1003 including, for example, a magnetic tape, hard disk, etc.; and communication device 1009. Communication device 1009 can allow a fiberglass cloth production quality inspection device to communicate wirelessly or wired with other devices to exchange data. Although the figure shows a fiberglass cloth production quality inspection device with various systems, it should be understood that implementation or presence of all the illustrated systems is not required. More or fewer systems may alternatively be implemented or present.

[0104] The present invention also provides a computer program product comprising a computer program. When executed by a processor, the computer program implements the steps of the above-described method for testing the production quality of glass fiber cloth. The computer program product provided by the present invention can solve the technical problem of testing the production quality of glass fiber cloth. Compared with the prior art, the beneficial effects of the computer program product provided by the present invention are the same as those of the method for testing the production quality of glass fiber cloth provided in the above-described embodiment, and are not further elaborated here.

[0105] In particular, according to the embodiments disclosed in the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present invention are performed.

[0106] It should be understood that the various parts disclosed in the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any appropriate manner in any one or more embodiments or examples.

[0107] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A method for detecting the production quality of glass fiber cloth, characterized in that: Methods include: Step S10: collecting multi-channel image data of the glass fiber cloth surface by multispectral imaging; The multi-channel image data includes: Visible light images , used to obtain fiber surface texture characteristics; Infrared image , used to obtain thermal radiation characteristics; UV image , used to obtain the microscopic abnormal characteristics of materials; Perform geometric calibration and registration on the multi-channel image data to generate a fused image , where x is the horizontal coordinate of the image pixel and y is the vertical coordinate of the image pixel; Step S20: fusion image Perform optical property calibration, including: From the fused image Get the reflected light intensity , the reflected light intensity The area with a reflection intensity greater than the preset threshold is defined as a high-reflection area, and brightness compensation is performed on the high-reflection area to obtain the first optimized fusion image. ; First optimized fusion image Perform grayscale equalization processing to obtain the second optimized fusion image ; Step S30: Using a multi-scale texture decomposition algorithm to optimize the second fused image Processing is performed to decompose the texture signal of the fiber interweaving area into: Low-frequency texture features , used to represent local uniformity; High-frequency abnormal characteristics , used to represent the characteristics of holes; High-frequency abnormal features Apply hole template matching algorithm to calculate hole template matching value , retain the hole template matching value The pixel coordinates that are greater than the preset candidate threshold are then used to generate candidate areas; the light and shadow consistency score is calculated for the extracted candidate areas. , and remove the light and shadow consistency score Pixels below the preset light and shadow consistency score threshold are used to generate optimized hole candidate areas; Step S40: Acquire image sequence data of cloth movement To optimize the hole candidate area, we use the time series analysis model combined with the image sequence data of the cloth movement , calculate the time variation variance of the optimized hole candidate area ; Combined with time-varying variance , using the reliability scoring model to calculate the hole reliability score , the formula is: in, In order to optimize the temporal feature mean of the hole candidate area, is an exponential function; Step S50: Scoring based on reliability , determine whether the optimized hole candidate area is a hole defect and output the glass fiber cloth production quality inspection report, including the hole location, hole size and hole reliability score.

2. A glass fiber cloth production quality inspection method according to claim 1, characterized in that: In step S20, the first optimized fusion image The calculation formula is: in, It is the light compensation coefficient of the high reflective area, which is determined by the optical properties of the glass fiber cloth.

3. A glass fiber cloth production quality inspection method according to claim 1, characterized in that: In step S30, low-frequency texture features and high-frequency abnormal characteristics Satisfy the formula .

4. A glass fiber cloth production quality inspection method according to claim 1, characterized in that: In step S30, the hole template matching value The calculation formula is: in, is the hole template weight coefficient matrix, and is the horizontal and vertical pixel coordinate size of the preset hole template, i is the horizontal offset of the pixel relative to the center point (x, y) in the filter window, the unit is pixel, j is the vertical offset of the pixel relative to the center point (x, y) in the filter window, the unit is pixel.

5. A glass fiber cloth production quality inspection method according to claim 1, characterized in that: In step S30, the light and shadow consistency score The calculation formula is: Where i is the horizontal offset of the pixel relative to the center point (x, y) in the filter window, in pixels, j is the vertical offset of the pixel relative to the center point (x, y) in the filter window, in pixels, is the pixel coordinate Grayscale value, is the window mean grayscale value, is the number of pixels in the window.

6. A glass fiber cloth production quality inspection method according to claim 1, characterized in that: In step S20, the second optimized fusion image The calculation formula is: in, and They are the preset minimum grayscale value and the preset maximum grayscale value, is the preset target grayscale range.

7. A glass fiber cloth production quality inspection method according to claim 1, characterized in that: In step S40, the time variation variance of the hole candidate area is optimized The calculation formula is: Where t is the time frame, is the total number of frames in the time series of the image sequence data of the cloth motion obtained, is the time mean of the image sequence data of cloth motion.

8. A glass fiber cloth production quality inspection system, characterized in that: The glass fiber cloth production quality detection system includes: A multispectral imaging module, used for collecting multi-channel image data of the glass fiber cloth surface through a multispectral imaging method; The multi-channel image data includes: Visible light images , used to obtain fiber surface texture characteristics; Infrared image , used to obtain thermal radiation characteristics; UV image , used to obtain the microscopic abnormal characteristics of materials; Perform geometric calibration and registration on the multi-channel image data to generate a fused image , where x is the horizontal coordinate of the image pixel and y is the vertical coordinate of the image pixel; Optical property correction module for fusion image Perform optical property calibration, including: From the fused image Get the reflected light intensity , the reflected light intensity The area with a reflection intensity greater than the preset threshold is defined as a high-reflection area, and brightness compensation is performed on the high-reflection area to obtain the first optimized fusion image. ; First optimized fusion image Perform grayscale equalization processing to obtain the second optimized fusion image ; Multi-scale texture analysis module, used to use multi-scale texture decomposition algorithm to optimize the second fusion image Processing is performed to decompose the texture signal of the fiber interweaving area into: Low-frequency texture features , used to represent local uniformity; High-frequency abnormal characteristics , used to represent the characteristics of holes; High-frequency abnormal features Apply hole template matching algorithm to calculate hole template matching value , retain the hole template matching value The pixel coordinates that are greater than the preset candidate threshold are then used to generate candidate areas; the light and shadow consistency score is calculated for the extracted candidate areas. , and remove the light and shadow consistency score Pixels below the preset light and shadow consistency score threshold are used to generate optimized hole candidate areas; Time series analysis module, used to obtain image sequence data of cloth movement To optimize the hole candidate area, we use the time series analysis model combined with the image sequence data of the cloth movement , calculate the time variation variance of the optimized hole candidate area ; Combined with time-varying variance , using the reliability scoring model to calculate the hole reliability score , the formula is: in, In order to optimize the temporal feature mean of the hole candidate area, is an exponential function; Report generation module for scoring based on reliability , determine whether the optimized hole candidate area is a hole defect and output the glass fiber cloth production quality inspection report, including the hole location, hole size and hole reliability score.

9. A glass fiber cloth production quality inspection equipment, characterized in that: The glass fiber cloth production quality inspection device includes: a memory, a processor, and a glass fiber cloth production quality inspection program stored in the memory and executable on the processor. When the glass fiber cloth production quality inspection program is executed by the processor, the glass fiber cloth production quality inspection method according to any one of claims 1 to 7 is implemented.

10. A computer program product, characterized in that The computer program product includes a glass fiber cloth production quality detection program, and when the glass fiber cloth production quality detection program is executed by a processor, the glass fiber cloth production quality detection method according to any one of claims 1 to 7 is implemented.

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