A method and system for detecting the cutting of OCA optical adhesive

Abstract

The present invention relates to the field of optical adhesive slitting technology, and in particular to an OCA optical adhesive slitting detection method and system. The present invention divides the slitting surface image into multiple local images, so as to facilitate accurate temperature detection and analysis of each local image, thereby avoiding information occlusion or analysis confusion that may occur during overall visual analysis. By obtaining the temperature feature information of each local image and analyzing and judging based on a preset temperature, the flatness of the OCA optical adhesive slitting surface can be detected more accurately and meticulously. Such quantitative analysis can more accurately identify which parts of the slitting surface temperature exceed the preset range. By calculating the root mean square roughness value and evaluating it according to the preset roughness value, the flatness of the OCA optical adhesive slitting surface can be effectively detected and evaluated. By comparing the calculated root mean square roughness value with the preset roughness value, it can be quickly determined whether the flatness of the slitting surface meets the requirements.

Description

Technical Field

[0001] The present invention relates to the field of optical adhesive cutting technology, and in particular to an OCA optical adhesive cutting detection method and system. Background Art

[0002] OCA (Optically Clear Adhesive) is a special adhesive used to bond transparent optical components. It has high transparency and excellent light transmittance. It is mainly used for bonding transparent optical components, such as lenses, displays, etc. It has the advantages of high clarity, high light transmittance, high adhesion, weather resistance, water resistance, high temperature resistance and UV resistance. These characteristics enable OCA optical adhesive to effectively reduce glare, improve brightness and transmittance, thereby reducing energy consumption and increasing contrast under strong light.

[0003] Currently, the main method for cutting OCA optical glue is to use laser technology to cut the OCA optical glue, and then use a transmittance meter or a microscope to detect the optical transparency of the OCA optical glue. However, when using laser technology to cut the OCA optical glue, heat will be generated, and a heat-affected area will be formed on the cutting edge. The transmittance meter or microscope cannot detect whether this area will cause the surface unevenness of the OCA optical glue to exceed the limited range of use, thereby affecting the service life of the OCA optical glue. Summary of the invention

[0004] The main purpose of the present invention is to provide an OCA optical glue cutting detection method, aiming to solve the technical problems in the prior art.

[0005] The present invention provides an OCA optical adhesive cutting detection method, comprising:

[0006] Acquire a section image of the OCA optical adhesive, and divide the section image into a plurality of partial images;

[0007] Obtaining temperature characteristic information of each local image, and determining whether each temperature characteristic information is greater than a preset temperature;

[0008] If the temperature characteristic information is greater than a preset temperature, then obtaining contour characteristic information of the local image corresponding to the temperature characteristic information greater than the preset temperature, and obtaining a horizontal contour curve and a vertical contour curve according to the contour characteristic information;

[0009] Acquire a plurality of horizontal valley point values ​​and horizontal peak point values ​​according to the horizontal contour curve, and calculate a horizontal average contour value according to the plurality of horizontal valley point values ​​and horizontal peak point values;

[0010] Acquire a plurality of vertical valley point values ​​and vertical peak point values ​​according to the vertical contour curve, and calculate a vertical average contour value according to the plurality of vertical valley point values ​​and vertical peak point values;

[0011] Calculating a root mean square roughness value based on the horizontal average profile value and the vertical average profile value;

[0012] Determining whether the root mean square roughness value is greater than a preset value;

[0013] If the root mean square roughness value is greater than the preset roughness value, it is determined that the flatness of the cut surface of the OCA optical adhesive does not meet the use requirements;

[0014] If the root mean square roughness value is not greater than the preset roughness value, it is determined that the flatness of the cut surface of the OCA optical adhesive meets the use requirements.

[0015] Preferably, the step of dividing the section image into a plurality of local images comprises:

[0016] Using histogram equalization to enhance the contrast of the section image to obtain an enhanced image;

[0017] Obtaining a channel value of each pixel in the enhanced image;

[0018] Weighting the plurality of channel values ​​to obtain a grayscale value, and converting the enhanced image into a grayscale image according to the grayscale value;

[0019] Obtaining a preset threshold and each pixel value in the grayscale image;

[0020] Mark the pixel points corresponding to the pixel values ​​greater than the preset threshold as black;

[0021] Mark the pixel points corresponding to the pixel values ​​less than the preset threshold as white to obtain a binary image;

[0022] Get multiple connected regions of a binary image;

[0023] Obtaining edge pixel position information of each of the connected regions;

[0024] The corresponding connected area is segmented according to the edge pixel position information to obtain multiple local images.

[0025] Preferably, the step of weighting the plurality of channel values ​​to obtain grayscale values, and converting the enhanced image into a grayscale image according to the grayscale values ​​comprises:

[0026] A plurality of channel values ​​are obtained, and a grayscale value is calculated based on the plurality of channel values, wherein the calculation formula is:

[0027] ;

[0028] in, Represents the gray value, Indicates Channel values, Indicates the sequence number of the channel value, Indicates the number of channel values;

[0029] Extracting the original channel value of each pixel in the enhanced image;

[0030] Each original channel value in the enhanced image is replaced according to the grayscale value to obtain a grayscale image.

[0031] Preferably, the step of acquiring a horizontal contour curve and a vertical contour curve according to the contour feature information comprises:

[0032] Acquire a plurality of horizontal edge contours and vertical edge contours according to the contour feature information;

[0033] Extracting a plurality of first continuous pixel point sequences of each of the horizontal edge contours, and performing curve fitting on the plurality of first continuous pixel point sequences to obtain a horizontal contour curve;

[0034] A plurality of second continuous pixel point sequences of each of the vertical edge contours are extracted, and curve fitting is performed on the plurality of second continuous pixel point sequences to obtain a vertical contour curve.

[0035] Preferably, the step of calculating the horizontal average contour value according to the multiple horizontal valley point values ​​and the horizontal peak point values ​​comprises:

[0036] Acquire a horizontal start value and a horizontal end value according to the horizontal profile curve;

[0037] Acquire a horizontal length value according to the horizontal profile curve;

[0038] Acquire a horizontal reference value according to the local image;

[0039] The horizontal average contour value is calculated according to the horizontal length value, the horizontal reference value, the horizontal start value, the horizontal end value, multiple horizontal valley point values ​​and the horizontal peak point value, wherein the calculation formula is:

[0040] ;

[0041] in, represents the horizontal average profile value, Indicates the horizontal length value, Indicates the horizontal reference value, Indicates the horizontal starting value, Indicates the horizontal end value, Indicates Horizontal valley value, Indicates Horizontal peak value, Indicates the number of horizontal valley values, Indicates the number of horizontal peak values.

[0042] Preferably, the step of calculating the root mean square roughness value according to the horizontal average profile value and the vertical average profile value comprises:

[0043] Obtaining a horizontal average contour value, and obtaining a first weight factor according to the horizontal average contour value;

[0044] Obtaining a vertical average profile value, and obtaining a second weight factor according to the vertical average profile value;

[0045] The root mean square roughness value is calculated according to the horizontal average contour value, the vertical average contour value, the first weight factor and the second weight factor, wherein the calculation formula is:

[0046] ;

[0047] in, represents the root mean square roughness value, represents the horizontal average profile value, represents the vertical average profile value, represents the first weight factor, represents the second weighting factor.

[0048] The present application also provides an OCA optical glue cutting detection system, comprising:

[0049] A segmentation module, used for acquiring a section image of the OCA optical adhesive and segmenting the section image into a plurality of local images;

[0050] An acquisition module is used to acquire temperature characteristic information of each local image and determine whether each temperature characteristic information is greater than a preset temperature;

[0051] If the temperature characteristic information is greater than a preset temperature, then obtaining contour characteristic information of the local image corresponding to the temperature characteristic information greater than the preset temperature, and obtaining a horizontal contour curve and a vertical contour curve according to the contour characteristic information;

[0052] A first calculation module, configured to obtain a plurality of horizontal valley point values ​​and a horizontal peak point value according to the horizontal contour curve, and calculate a horizontal average contour value according to the plurality of horizontal valley point values ​​and the horizontal peak point values;

[0053] A second calculation module, used for obtaining a plurality of vertical valley point values ​​and vertical peak point values ​​according to the vertical contour curve, and calculating a vertical average contour value according to the plurality of vertical valley point values ​​and vertical peak point values;

[0054] A third calculation module, used for calculating a root mean square roughness value according to the horizontal average contour value and the vertical average contour value;

[0055] A judging module, used to judge whether the root mean square roughness value is greater than a preset value;

[0056] If the root mean square roughness value is greater than the preset roughness value, it is determined that the flatness of the cut surface of the OCA optical adhesive does not meet the use requirements;

[0057] If the root mean square roughness value is not greater than the preset roughness value, it is determined that the flatness of the cut surface of the OCA optical adhesive meets the use requirements.

[0058] Preferably, the segmentation module comprises:

[0059] An enhancement unit, used for enhancing the contrast of the slice image by using histogram equalization to obtain an enhanced image;

[0060] A first acquisition unit, used to acquire a channel value of each pixel in the enhanced image;

[0061] A conversion unit, configured to weight the plurality of channel values ​​to obtain a grayscale value, and convert the enhanced image into a grayscale image according to the grayscale value;

[0062] A second acquisition unit, used to acquire a preset threshold and each pixel value in the grayscale image;

[0063] A first marking unit, used to mark the pixel points corresponding to the pixel values ​​greater than a preset threshold as black;

[0064] A second marking unit, used to mark the pixel points corresponding to the pixel values ​​being less than a preset threshold as white, so as to obtain a binary image;

[0065] A third acquisition unit is used to acquire multiple connected regions of the binary image;

[0066] A fourth acquisition unit, used to acquire edge pixel position information of each of the connected regions;

[0067] The segmentation unit is used to segment the corresponding connected area according to the edge pixel position information to obtain multiple local images.

[0068] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned OCA optical glue cutting detection method when executing the computer program.

[0069] The present invention also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the above-mentioned OCA optical glue cutting detection method are implemented.

[0070] The beneficial effects of the present invention are as follows: the present invention facilitates accurate temperature detection and analysis of each local image by dividing the cutting surface image into multiple local images. Dividing the entire cutting surface image into multiple local images can avoid information occlusion or analysis confusion that may occur during overall visual analysis, making the analysis of each local image more accurate. By obtaining the temperature characteristic information of each local image and analyzing and judging based on the preset temperature, the flatness of the OCA optical glue cutting surface can be detected more accurately and meticulously. The temperature characteristic information of each local image can provide a specific temperature value. Such quantitative analysis can be more accurate. It can accurately identify which parts of the slitting surface temperature exceed the preset range. For local images where the temperature exceeds the preset value, further obtaining its contour feature information and analyzing the horizontal contour curve and the vertical contour curve can help understand the impact of temperature anomaly on the flatness of the slitting surface. By calculating the root mean square roughness value and evaluating it according to the preset roughness value, the flatness of the OCA optical glue slitting surface can be effectively detected and evaluated. By comparing the calculated root mean square roughness value with the preset roughness value, it can be quickly determined whether the flatness of the slitting surface meets the requirements. This comparison can intuitively reflect the actual quality status of the slitting surface. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 The figure is a schematic diagram of a method flow according to an embodiment of the present invention.

[0072] Figure 2 FIG. 1 is a schematic diagram of a device structure according to an embodiment of the present invention.

[0073] Figure 3 A schematic diagram of the internal structure of a computer device according to an embodiment of the present application.

[0074] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0075] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0076] like Figure 1-Figure 3 As shown, the present application provides an OCA optical glue cutting detection method, comprising:

[0077] S1, obtaining a section image of the OCA optical adhesive, and dividing the section image into a plurality of partial images;

[0078] S2, obtaining temperature characteristic information of each local image, and determining whether each temperature characteristic information is greater than a preset temperature;

[0079] If the temperature characteristic information is greater than a preset temperature, then obtaining contour characteristic information of the local image corresponding to the temperature characteristic information greater than the preset temperature, and obtaining a horizontal contour curve and a vertical contour curve according to the contour characteristic information;

[0080] S3, acquiring a plurality of horizontal valley point values ​​and a horizontal peak point value according to the horizontal contour curve, and calculating a horizontal average contour value according to the plurality of horizontal valley point values ​​and the horizontal peak point values;

[0081] S4, acquiring a plurality of vertical valley point values ​​and a vertical peak point value according to the vertical profile curve, and calculating a vertical average profile value according to the plurality of vertical valley point values ​​and the vertical peak point value;

[0082] S5, calculating the root mean square roughness value according to the horizontal average profile value and the vertical average profile value;

[0083] S6, determining whether the root mean square roughness value is greater than a preset value;

[0084] If the root mean square roughness value is greater than the preset roughness value, it is determined that the flatness of the cut surface of the OCA optical adhesive does not meet the use requirements;

[0085] If the root mean square roughness value is not greater than the preset roughness value, it is determined that the flatness of the cut surface of the OCA optical adhesive meets the use requirements.

[0086] As described in the above steps S1-S6, OCA (Optically Clear Adhesive) optical adhesive is a special adhesive used to bond transparent optical components, with high transparency and excellent light transmittance. It is mainly used for bonding transparent optical components, such as lenses, displays, etc. It has the advantages of high clarity, high light transmittance, high adhesion, weather resistance, water resistance, high temperature resistance and UV resistance. These characteristics enable OCA optical adhesive to effectively reduce glare, improve brightness and transmittance, thereby reducing energy consumption and increasing contrast under strong light. At present, the main method for cutting OCA optical adhesive is to use laser technology to cut OCA optical adhesive, and then use a transmittance meter or microscope to detect the optical transparency of OCA optical adhesive. However, when cutting OCA optical adhesive using laser technology, heat will be generated, and a heat-affected area will be formed at the cutting edge. The transmittance meter or microscope cannot detect whether this area will cause the surface unevenness of OCA optical adhesive to exceed the use limit range, thereby affecting the service life of OCA optical adhesive. The present invention obtains a cutting surface image of OCA optical glue and divides the cutting surface image into multiple local images. By dividing the cutting surface image into multiple local images, it is convenient to perform accurate temperature detection and analysis on each local image. By dividing the entire cutting surface image into multiple local images, it is possible to avoid information occlusion or analysis confusion that may occur during overall visual analysis, so that the analysis of each local image is more accurate. By obtaining the temperature characteristic information of each local image and judging whether each temperature characteristic information is greater than a preset temperature, if the temperature characteristic information is greater than the preset temperature, it is judged that the cutting surface temperature of the optical glue corresponding to the local image is too high. By obtaining the temperature characteristic information of each local image and performing analysis and judgment based on the preset temperature, the flatness of the cutting surface of the OCA optical glue can be detected more accurately and meticulously. The temperature characteristic information of each local image can provide a specific temperature value, rather than a simple overheating or normal state. Such quantitative analysis can It can more accurately identify which parts of the slitting surface temperature exceed the preset range. By analyzing the temperature information of each local image, the temperature anomaly can be discovered in time. For the local image whose temperature exceeds the preset value, the contour feature information of the local image corresponding to the excessively high temperature of the slitting surface is further obtained, and the horizontal contour curve and the vertical contour curve are obtained according to the contour feature information, which can help understand the influence of temperature anomaly on the flatness of the slitting surface. The temperature anomaly area is usually associated with the unevenness of the surface or deformation caused by thermal stress. According to the horizontal contour curve, multiple horizontal valley point values ​​and horizontal peak point values ​​are obtained, and multiple vertical valley point values ​​and vertical peak point values ​​are obtained from the vertical contour curve, and the horizontal average contour value and the vertical average contour value are calculated by multiple horizontal valley point values ​​and horizontal peak point values, and the vertical average contour value is calculated by multiple vertical valley point values ​​and vertical peak point values. By obtaining multiple horizontal valley point values, horizontal peak point values, vertical valley point values, and vertical peak point values, and further calculating the horizontal average contour value and the vertical average contour value,A more in-depth and comprehensive analysis can be conducted on the flatness of the OCA optical adhesive slitting surface. By extracting the valley points and peak points in the horizontal and vertical profile curves, the subtle changes and geometric features of the surface can be accurately captured. These feature points reflect the changes in the concave and convexity of the slitting surface. By calculating the horizontal average profile value and the vertical average profile value, the flatness of the slitting surface in the horizontal and vertical directions can be quantified. By comparing the valley points and peak points in different areas, the surface concave and convex, defects or irregular shapes can be quickly identified. The root mean square roughness value is calculated by the horizontal average profile value and the vertical average profile value, and it is determined whether the root mean square roughness value is greater than the preset value. If the root mean square roughness value is greater than the preset value, ... If the RMS roughness value is less than the preset roughness value, it is determined that the flatness of the cut surface of the OCA optical adhesive does not meet the use requirements. Otherwise, it is determined that the flatness of the cut surface of the OCA optical adhesive meets the use requirements. By calculating the RMS roughness value and evaluating it according to the preset roughness value, the flatness of the cut surface of the OCA optical adhesive can be effectively detected and evaluated. The RMS roughness value is a common parameter of surface roughness, which is used to quantify the unevenness or irregularity of the surface. By comparing the calculated RMS roughness value with the preset roughness value, it is possible to quickly determine whether the flatness of the cut surface meets the requirements. This comparison can intuitively reflect the actual quality status of the cut surface.

[0087] In one embodiment, the step S1 of dividing the section image into a plurality of partial images comprises:

[0088] S11, using histogram equalization to enhance the contrast of the section image to obtain an enhanced image;

[0089] S12, obtaining a channel value of each pixel in the enhanced image;

[0090] S13, weighting the plurality of channel values ​​to obtain a grayscale value, and converting the enhanced image into a grayscale image according to the grayscale value;

[0091] S14, obtaining a preset threshold and each pixel value in the grayscale image;

[0092] S15, marking the pixel points corresponding to the pixel values ​​greater than the preset threshold as black;

[0093] S16, marking the pixel points corresponding to the pixel values ​​less than the preset threshold as white to obtain a binary image;

[0094] S17, obtaining multiple connected regions of the binary image;

[0095] S18, obtaining edge pixel position information of each of the connected regions;

[0096] S19, segmenting the corresponding connected area according to the edge pixel position information to obtain multiple local images.

[0097] As described in the above steps S11-S19, in the present invention, the contrast of the section image is enhanced by adopting histogram equalization to obtain an enhanced image, the grayscale value is obtained by weighting the channel value of each pixel point in the enhanced image, and the enhanced image is converted into a grayscale image according to the grayscale value. Histogram equalization can effectively enhance the contrast of the image, making the details in the section image clearer and more visible. The grayscale value is obtained by weighting the channel value of each pixel point in the enhanced image, which can more accurately represent the brightness and darkness changes in the image. The grayscale image is more conducive to subsequent image processing and analysis, such as edge detection, contour extraction, etc. After conversion to a grayscale image, each pixel point only needs one grayscale value to represent its brightness, which simplifies the complexity of image processing. By setting a preset threshold, the pixel points corresponding to the pixel values ​​greater than the preset threshold are marked as black, and the pixel points corresponding to the pixel values ​​less than the preset threshold are marked as white. A binary image is obtained, and edge pixel position information of multiple connected areas of the binary image is obtained, and the corresponding connected areas are segmented according to the edge pixel position information to obtain multiple local images, wherein, in binary image processing, a connected area refers to an area in the image with the same pixel value (all white) and connected by a boundary (or spatially adjacent) (i.e., a foreground area) or an area in the image with the same pixel value (all black) and connected by a boundary (or spatially adjacent) (i.e., a background area). The binary image can clearly mark the uneven area of ​​the cutting surface, and even small differences can be effectively segmented and located. By obtaining the edge pixel position information of multiple connected areas of the binary image, a detailed feature analysis can be performed on each local area, and the multiple local images obtained by segmentation can be respectively analyzed and compared for flatness, so as to achieve more accurate and comprehensive OCA optical glue cutting surface flatness detection.

[0098] In one embodiment, the step S13 of weighting the plurality of channel values ​​to obtain grayscale values ​​and converting the enhanced image into a grayscale image according to the grayscale values ​​comprises:

[0099] S131, obtaining multiple channel values, and calculating a grayscale value according to the multiple channel values, wherein the calculation formula is:

[0100] ;

[0101] in, Represents the gray value, Indicates Channel values, Indicates the sequence number of the channel value, Indicates the number of channel values;

[0102] S132, extracting the original channel value of each pixel in the enhanced image;

[0103] S133. Replace each original channel value in the enhanced image according to the grayscale value to obtain a grayscale image.

[0104] As described in the above steps S131-S133, in the present invention, the grayscale value is calculated according to multiple channel values, the original channel value of each pixel in the enhanced image is extracted, and then each original channel value in the enhanced image is replaced according to the grayscale value to obtain a grayscale image, wherein the pixel value refers to the specific value of each pixel in the image, and the channel value refers to the value of each pixel in the color image on different color channels (such as red channel, green channel, blue channel). After the enhanced image is converted into a grayscale image, each pixel only needs one grayscale value to represent its brightness. This standardized grayscale representation helps to eliminate the influence of color differences caused by the original channel values ​​on flatness detection, making the grayscale image more stable and reliable. Through the grayscale image, edge detection, contour analysis, feature extraction and other operations can be performed more easily, thereby more accurately evaluating the flatness of the cut surface. Compared with multi-channel color images, grayscale images have lower data volume and computing cost, which not only saves storage space and processing time, but also improves analysis efficiency and response speed.

[0105] In one embodiment, the step S3 of acquiring a horizontal contour curve and a vertical contour curve according to the contour feature information comprises:

[0106] S31, acquiring a plurality of horizontal edge contours and vertical edge contours according to the contour feature information;

[0107] S32, extracting a plurality of first continuous pixel point sequences of each of the horizontal edge contours, and performing curve fitting on the plurality of first continuous pixel point sequences to obtain a horizontal contour curve;

[0108] S33, extracting a plurality of second continuous pixel point sequences of each of the vertical edge contours, and performing curve fitting on the plurality of second continuous pixel point sequences to obtain a vertical contour curve.

[0109] As described in the above steps S31-S33, in the present invention, multiple horizontal edge contours and vertical edge contours are obtained, multiple first continuous pixel point sequences of each horizontal edge contour are extracted, and the multiple first continuous pixel point sequences are curve-fitted to obtain a horizontal contour curve. Similarly, multiple second continuous pixel point sequences are extracted for each vertical edge contour, and the multiple second continuous pixel point sequences are curve-fitted to obtain a vertical contour curve, wherein the horizontal edge contour refers to the edge contour point from left to back (or from right to left) for the OCA optical adhesive cutting surface, and the vertical edge contour refers to the edge contour point from top to bottom (or from bottom to top) for the OCA optical adhesive cutting surface, and the continuous pixel point sequence refers to the The image edge profile is a collection of pixels that are continuously arranged along a specific direction (usually horizontal or vertical direction). The horizontal edge profile and the vertical edge profile can accurately describe the geometric shape and profile of the cutting surface through curve fitting. This accurate description helps to quantify the flatness of the cutting surface. By obtaining multiple horizontal and vertical edge profiles and integrating them into curves, multiple local features of the cutting surface can be comprehensively analyzed. Moreover, the curve fitting of the horizontal edge profile and the vertical edge profile can effectively smooth local noise and small errors, so that the final curve is more in line with the actual surface shape, which helps to eliminate unnecessary flatness measurement errors caused by pixel level changes.

[0110] In one embodiment, the step S4 of calculating the horizontal average contour value according to the multiple horizontal valley point values ​​and the horizontal peak point values ​​includes:

[0111] S41, acquiring a horizontal start value and a horizontal end value according to the horizontal profile curve;

[0112] S42, obtaining a horizontal length value according to the horizontal profile curve;

[0113] S43, obtaining a horizontal reference value according to the local image;

[0114] S44, calculating a horizontal average contour value according to the horizontal length value, the horizontal reference value, the horizontal start value, the horizontal end value, multiple horizontal valley point values ​​and the horizontal peak point value, wherein the calculation formula is:

[0115] ;

[0116] in, represents the horizontal average profile value, Indicates the horizontal length value, Indicates the horizontal reference value, Indicates the horizontal starting value, Indicates the horizontal end value, Indicates Horizontal valley value, Indicates Horizontal peak value, Indicates the number of horizontal valley values, Indicates the number of horizontal peak values.

[0117] As described in the above steps S41-S44, in the present invention, a horizontal starting value, a horizontal ending value and a horizontal length value are obtained through a horizontal contour curve, and then a horizontal reference value is obtained according to a local image. Then, a horizontal average contour value is calculated according to the horizontal length value, the horizontal reference value, the horizontal starting value, the horizontal ending value, multiple horizontal valley point values ​​and the horizontal peak point value. The horizontal average contour value reflects the average height or contour feature of the cutting surface in the horizontal direction. By calculating the horizontal average contour value, the overall flatness of the surface can be quantified, and the concave-convex degree and overall flatness of the surface can be evaluated. The horizontal reference value is used as a reference point to help determine the reference height or flat surface of the cutting surface. By comparing the horizontal average contour value with the horizontal reference value, it can be determined whether the surface is even. The horizontal valley point value and the horizontal peak point value provide information about the local depressions and protrusions on the surface. The existence of these points can indicate possible defects or unevenness, and help to accurately locate and analyze specific problem areas on the surface. The steps of calculating the vertical average contour value according to the multiple vertical valley point values ​​and the vertical peak point values ​​are the same as those of calculating the horizontal average contour value, that is, obtaining the vertical start value and the vertical end value according to the vertical contour curve; obtaining the vertical length value according to the vertical contour curve; obtaining the vertical reference value according to the local image; and calculating the vertical average contour value according to the vertical length value, the vertical reference value, the vertical start value, the vertical end value, the multiple vertical valley point values ​​and the vertical peak point values. The calculation formula is:

[0118] ;

[0119] in, represents the vertical average profile value, Indicates the vertical length value, Indicates the vertical reference value, Indicates the vertical starting value, Indicates the vertical end value, Indicates Vertical valley point value, Indicates Vertical peak value, Indicates the number of vertical valley values, It represents the number of vertical peak values. Similarly, the vertical average profile value reflects the average height or profile characteristics of the cutting surface in the vertical direction.

[0120] In one embodiment, the step S6 of calculating the root mean square roughness value according to the horizontal average profile value and the vertical average profile value comprises:

[0121] S61, obtaining a horizontal average contour value, and obtaining a first weight factor according to the horizontal average contour value;

[0122] S62, obtaining a vertical average profile value, and obtaining a second weight factor according to the vertical average profile value;

[0123] S63, calculating a root mean square roughness value according to the horizontal average contour value, the vertical average contour value, the first weight factor and the second weight factor, wherein the calculation formula is:

[0124] ;

[0125] in, represents the root mean square roughness value, represents the horizontal average profile value, represents the vertical average profile value, represents the first weight factor, represents the second weighting factor.

[0126] As described in the above steps S61-S63, in the present invention, a first weight factor of the horizontal average contour value and a second weight factor of the vertical average contour value are obtained, and the root mean square roughness value is calculated according to the horizontal average contour value, the vertical average contour value, the first weight factor and the second weight factor. The root mean square roughness value comprehensively considers the average contour values ​​and their weight factors in the horizontal and vertical directions. This comprehensive evaluation can more comprehensively reflect the overall flatness and surface roughness of the cut surface. By considering the weight factors in the horizontal and vertical directions, the root mean square roughness value can more accurately reflect the main features of the cut surface. This method helps to eliminate deviations or errors in a single direction and provide more stable and reliable flatness measurement results.

[0127] The present application also provides an OCA optical glue cutting detection system, comprising:

[0128] A segmentation module, used for acquiring a section image of the OCA optical adhesive and segmenting the section image into a plurality of local images;

[0129] An acquisition module is used to acquire temperature characteristic information of each local image and determine whether each temperature characteristic information is greater than a preset temperature;

[0130] If the temperature characteristic information is greater than a preset temperature, then obtaining contour characteristic information of the local image corresponding to the temperature characteristic information greater than the preset temperature, and obtaining a horizontal contour curve and a vertical contour curve according to the contour characteristic information;

[0131] A first calculation module, configured to obtain a plurality of horizontal valley point values ​​and a horizontal peak point value according to the horizontal contour curve, and calculate a horizontal average contour value according to the plurality of horizontal valley point values ​​and the horizontal peak point values;

[0132] A second calculation module, used for obtaining a plurality of vertical valley point values ​​and vertical peak point values ​​according to the vertical contour curve, and calculating a vertical average contour value according to the plurality of vertical valley point values ​​and vertical peak point values;

[0133] A third calculation module, used for calculating a root mean square roughness value according to the horizontal average contour value and the vertical average contour value;

[0134] A judging module, used to judge whether the root mean square roughness value is greater than a preset value;

[0135] If the root mean square roughness value is greater than the preset roughness value, it is determined that the flatness of the cut surface of the OCA optical adhesive does not meet the use requirements;

[0136] If the root mean square roughness value is not greater than the preset roughness value, it is determined that the flatness of the cut surface of the OCA optical adhesive meets the use requirements.

[0137] In one embodiment, the segmentation module comprises:

[0138] An enhancement unit, used for enhancing the contrast of the slice image by using histogram equalization to obtain an enhanced image;

[0139] A first acquisition unit, used to acquire a channel value of each pixel in the enhanced image;

[0140] A conversion unit, configured to weight the plurality of channel values ​​to obtain a grayscale value, and convert the enhanced image into a grayscale image according to the grayscale value;

[0141] A second acquisition unit, used to acquire a preset threshold and each pixel value in the grayscale image;

[0142] A first marking unit, used to mark the pixel points corresponding to the pixel values ​​greater than a preset threshold as black;

[0143] A second marking unit, used to mark the pixel points corresponding to the pixel values ​​being less than a preset threshold as white, so as to obtain a binary image;

[0144] A third acquisition unit is used to acquire multiple connected regions of the binary image;

[0145] A fourth acquisition unit, used to acquire edge pixel position information of each of the connected regions;

[0146] The segmentation unit is used to segment the corresponding connected area according to the edge pixel position information to obtain multiple local images.

[0147] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned OCA optical glue cutting detection method when executing the computer program.

[0148] The present invention also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the above-mentioned OCA optical glue cutting detection method are implemented.

[0149] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided in this application and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0150] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0151] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. An OCA optical glue cutting detection method, characterized in that: include: Acquire a section image of the OCA optical adhesive, and divide the section image into a plurality of partial images; Obtaining temperature characteristic information of each local image, and determining whether each temperature characteristic information is greater than a preset temperature; If the temperature characteristic information is greater than a preset temperature, then obtaining contour characteristic information of a local image corresponding to the temperature characteristic information greater than the preset temperature, and obtaining a horizontal contour curve and a vertical contour curve according to the contour characteristic information; Acquire a plurality of horizontal valley point values ​​and horizontal peak point values ​​according to the horizontal contour curve, and calculate a horizontal average contour value according to the plurality of horizontal valley point values ​​and horizontal peak point values; Acquire a plurality of vertical valley point values ​​and vertical peak point values ​​according to the vertical contour curve, and calculate a vertical average contour value according to the plurality of vertical valley point values ​​and vertical peak point values; Calculating a root mean square roughness value based on the horizontal average profile value and the vertical average profile value; Determining whether the root mean square roughness value is greater than a preset value; If the root mean square roughness value is greater than the preset roughness value, it is determined that the flatness of the cut surface of the OCA optical adhesive does not meet the use requirements; If the root mean square roughness value is not greater than the preset roughness value, it is determined that the flatness of the cut surface of the OCA optical adhesive meets the use requirements.

2. The OCA optical glue cutting detection method according to claim 1, characterized in that: The step of dividing the section image into a plurality of local images comprises: Using histogram equalization to enhance the contrast of the section image to obtain an enhanced image; Obtaining a channel value of each pixel in the enhanced image; Weighting the plurality of channel values ​​to obtain a grayscale value, and converting the enhanced image into a grayscale image according to the grayscale value; Obtaining a preset threshold and each pixel value in the grayscale image; Mark the pixel points corresponding to the pixel values ​​greater than the preset threshold as black; Mark the pixel points corresponding to the pixel values ​​less than the preset threshold as white to obtain a binary image; Obtain multiple connected regions of a binary image; Obtaining edge pixel position information of each of the connected regions; The corresponding connected area is segmented according to the edge pixel position information to obtain multiple local images.

3. The OCA optical glue cutting detection method according to claim 2, characterized in that: The step of weighting the plurality of channel values ​​to obtain a grayscale value, and converting the enhanced image into a grayscale image according to the grayscale value comprises: A plurality of channel values ​​are obtained, and a grayscale value is calculated based on the plurality of channel values, wherein the calculation formula is: ； in, Represents the gray value, Indicates Channel values, Indicates the sequence number of the channel value, Indicates the number of channel values; Extracting the original channel value of each pixel in the enhanced image; Each original channel value in the enhanced image is replaced according to the grayscale value to obtain a grayscale image.

4. The OCA optical glue cutting detection method according to claim 1, characterized in that: The step of acquiring a horizontal contour curve and a vertical contour curve according to the contour feature information comprises: Acquire a plurality of horizontal edge contours and vertical edge contours according to the contour feature information; Extracting a plurality of first continuous pixel point sequences of each of the horizontal edge contours, and performing curve fitting on the plurality of first continuous pixel point sequences to obtain a horizontal contour curve; A plurality of second continuous pixel point sequences of each of the vertical edge contours are extracted, and curve fitting is performed on the plurality of second continuous pixel point sequences to obtain a vertical contour curve.

5. The OCA optical glue cutting detection method according to claim 1, characterized in that: The step of calculating the horizontal average contour value according to the multiple horizontal valley point values ​​and the horizontal peak point values ​​comprises: Acquire a horizontal start value and a horizontal end value according to the horizontal profile curve; Acquire a horizontal length value according to the horizontal profile curve; Acquire a horizontal reference value according to the local image; The horizontal average contour value is calculated according to the horizontal length value, the horizontal reference value, the horizontal starting value, the horizontal ending value, multiple horizontal valley point values ​​and the horizontal peak point value, wherein the calculation formula is: ； in, represents the horizontal average profile value, Indicates the horizontal length value, Indicates the horizontal reference value, Indicates the horizontal starting value, Indicates the horizontal end value, Indicates Horizontal valley value, Indicates Horizontal peak value, Indicates the number of horizontal valley values, Indicates the number of horizontal peak values.

6. The OCA optical glue cutting detection method according to claim 1, characterized in that: The step of calculating the root mean square roughness value according to the horizontal average profile value and the vertical average profile value comprises: Obtaining a horizontal average contour value, and obtaining a first weight factor according to the horizontal average contour value; Obtaining a vertical average profile value, and obtaining a second weight factor according to the vertical average profile value; The root mean square roughness value is calculated according to the horizontal average contour value, the vertical average contour value, the first weight factor and the second weight factor, wherein the calculation formula is: ； in, represents the root mean square roughness value, represents the horizontal average profile value, represents the vertical average profile value, represents the first weight factor, represents the second weighting factor.

7. An OCA optical glue cutting detection system, characterized in that: include: A segmentation module, used for acquiring a section image of the OCA optical adhesive and segmenting the section image into a plurality of local images; An acquisition module is used to acquire temperature characteristic information of each local image and determine whether each temperature characteristic information is greater than a preset temperature; If the temperature characteristic information is greater than a preset temperature, then obtaining contour characteristic information of a local image corresponding to the temperature characteristic information greater than the preset temperature, and obtaining a horizontal contour curve and a vertical contour curve according to the contour characteristic information; A first calculation module, configured to obtain a plurality of horizontal valley point values ​​and a horizontal peak point value according to the horizontal contour curve, and calculate a horizontal average contour value according to the plurality of horizontal valley point values ​​and the horizontal peak point values; A second calculation module, used for obtaining a plurality of vertical valley point values ​​and vertical peak point values ​​according to the vertical contour curve, and calculating a vertical average contour value according to the plurality of vertical valley point values ​​and vertical peak point values; A third calculation module, used for calculating a root mean square roughness value according to the horizontal average contour value and the vertical average contour value; A judging module, used to judge whether the root mean square roughness value is greater than a preset value; If the root mean square roughness value is greater than the preset roughness value, it is determined that the flatness of the cut surface of the OCA optical adhesive does not meet the use requirements; If the root mean square roughness value is not greater than the preset roughness value, it is determined that the flatness of the cut surface of the OCA optical adhesive meets the use requirements.

8. The OCA optical glue cutting detection system according to claim 7, characterized in that: The segmentation module comprises: An enhancement unit, used for enhancing the contrast of the slice image by using histogram equalization to obtain an enhanced image; A first acquisition unit, used to acquire a channel value of each pixel in the enhanced image; A conversion unit, configured to weight the plurality of channel values ​​to obtain a grayscale value, and convert the enhanced image into a grayscale image according to the grayscale value; A second acquisition unit, used to acquire a preset threshold and each pixel value in the grayscale image; A first marking unit, used to mark the pixel points corresponding to the pixel values ​​greater than a preset threshold as black; A second marking unit, used to mark the pixel points corresponding to the pixel values ​​being less than a preset threshold as white, so as to obtain a binary image; A third acquisition unit is used to acquire multiple connected regions of the binary image; A fourth acquisition unit, used to acquire edge pixel position information of each of the connected regions; The segmentation unit is used to segment the corresponding connected area according to the edge pixel position information to obtain multiple local images.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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