Method and system for detecting surface roughness of optical glass

Abstract

The invention discloses an optical glass surface roughness detection method and system, and the method comprises the steps: placing the whole optical glass in a pure white background, collecting the whole image of the optical glass through a digital optical camera device, and carrying out the graying processing and image enhancement processing, and generating an entity image; determining the center of circle of optical glass in the entity image, and averagely dividing the optical glass into N fan-shaped blocks with the angle of A through the center of circle of the optical glass; all the fan-shaped blocks are filled with pure white pixels to form rectangles with the width equal to the chord length of a fan and the length equal to the radius of the fan, and N filling rectangles are generated; sequentially inputting the N filling rectangles into a pre-trained convolutional neural network model, and calculating the number S of fan-shaped blocks with rough surfaces; the ratio of S to N is calculated, and the optical glass surface roughness measurement value is generated. According to the invention, automatic roughness detection of the surface of the circular optical glass is realized.

Description

Technical Field

[0001] The present invention relates to the field of optical glass, and in particular to a method and system for detecting the surface roughness of optical glass. Background Art

[0002] Optical glass is the foundation and an important part of the photography and videography industry, optoelectronic technology industry, and medical device industry. As a basic material, optical glass is increasingly used in various fields. Due to the rapid increase in the output and demand of optical glass, traditional manufacturing technology can no longer meet the market's needs for high-efficiency and high-precision manufacturing.

[0003] At present, the production process of optical glass components mainly relies on manual inspection of the surface roughness of optical glass. This method is inefficient, restricts the improvement of production efficiency, and cannot meet the current market demand. In addition, due to the transparent characteristics of optical glass, directly using convolutional neural networks to inspect the entire optical glass will result in low accuracy. Summary of the invention

[0004] In view of the above-mentioned technical deficiencies, the purpose of the present invention is to provide a method and system for detecting the surface roughness of optical glass, aiming to solve the problem that the current optical glass components mainly rely on manual roughness detection on the surface of optical glass during the production process. This method is inefficient and restricts the improvement of production efficiency. In addition, due to the transparent characteristics of optical glass, directly using convolutional neural networks to detect the entire optical glass will result in low accuracy.

[0005] In view of the above problems, the present application provides a method and system for detecting the surface roughness of optical glass.

[0006] In a first aspect disclosed in the present application, a method for detecting the surface roughness of an optical glass is provided, the method comprising the following steps: Step 1: placing the entire optical glass on a pure white background, using a digital optical camera device to capture an entire image of the optical glass, and performing grayscale processing and image enhancement processing to generate a physical image, wherein the optical glass is circular; Step 2: Determine the center of the optical glass in the physical image, and divide the optical glass into N sector blocks with an angle of A through the center of the optical glass, where N=2π / A, A is less than π / 2, and N is a positive integer; Step 3: Use pure white pixels to fill all the fan-shaped blocks into rectangles with a width equal to the chord length of the fan and a length equal to the radius of the fan, generating N filled rectangles; Step 4: Input N filled rectangles into the pre-trained convolutional neural network model in sequence, determine whether there is surface roughness in the fan-shaped blocks in each filled rectangle, and calculate the number S of filled rectangles with surface roughness in the fan-shaped blocks; Step 5: Calculate the ratio of S to N to generate a roughness measurement value for the optical glass surface.

[0007] Preferably, the step 1 specifically includes the following steps: Step 1.1: Place the entire optical glass on a pure white background, and use a digital optical camera device to collect a three-channel color image of the entire optical glass to generate an initial image; Step 1.2: Take the average value of the values ​​of each pixel in the initial image on the three channels as the grayscale value to generate a grayscale image; Step 1.3: For each pixel of the grayscale image, add 1 to the grayscale value and perform a logarithmic transformation with a natural constant as the base to generate a transformed value, and then scale the transformed value to the range of [0, 255] to generate a solid image.

[0008] Preferably, the step 2 specifically includes the following steps: Step 2.1: Determine the center of the optical glass in the physical image and generate the center coordinates, where the center coordinates consist of the pixel position in the horizontal direction and the pixel position in the vertical direction; Step 2.2: Create N binary images of the same size as the physical image, and set all pixel values ​​of each binary image to 0. On all binary images, take the center coordinates of the circle as the center of the fan-shaped block, take angle A as the angle of the fan-shaped block, take the radius of the optical glass in the physical image as the radius of the fan-shaped block, draw sectors in sequence at non-overlapping continuous positions, and set the pixel values ​​in the drawn sectors to 1; Step 2.3: Perform logical AND operations on the N binary images and the physical image respectively, and divide the optical glass into N sector-shaped blocks with an angle of A.

[0009] Preferably, the step 4 specifically includes the following steps: Step 4.1: Input N filled rectangles into the pre-trained convolutional neural network model in sequence, and the convolutional neural network model outputs the probability that the fan-shaped block in each filled rectangle has a rough surface, wherein the convolutional neural network model includes a convolutional layer, a pooling layer, and a fully connected layer; Step 4.2: If the probability that the fan-shaped block in the i-th filled rectangle has a rough surface is greater than 0.5, it is determined that the fan-shaped block in the i-th filled rectangle has a rough surface, where i=1,2,...,N; Step 4.3: Count the number S of filling rectangles with rough surfaces in the fan-shaped blocks.

[0010] The second aspect disclosed in the present application provides a system for detecting the surface roughness of optical glass, the system being used for the above-mentioned method for detecting the surface roughness of optical glass, the system comprising: A collection module, wherein the collection module is used to place the entire optical glass on a pure white background, use a digital optical camera device to collect an overall image of the optical glass, and perform grayscale processing and image enhancement processing to generate a physical image, wherein the optical glass is circular; A sector module, the sector module is used to determine the center of the optical glass in the solid image, and divide the optical glass into N sector blocks with an angle of A through the center of the optical glass, where N=2π / A, A is less than π / 2, and N is a positive integer; A filling module, the filling module is used to use pure white pixels to fill all the fan-shaped blocks into rectangles with a width equal to the chord length of the fan and a length equal to the radius of the fan, to generate N filled rectangles; A determination module, wherein the determination module is used to sequentially input N filled rectangles into a pre-trained convolutional neural network model, determine whether there is surface roughness in the fan-shaped blocks in each filled rectangle, and calculate the number S of filled rectangles with surface roughness in the fan-shaped blocks; The measurement module is used to calculate the ratio of S to N and generate a measurement value of the surface roughness of the optical glass.

[0011] The third aspect disclosed in the present application 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 method for detecting the surface roughness of optical glass when executing the computer program.

[0012] The fourth aspect disclosed in the present application 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 method for detecting the surface roughness of optical glass are implemented.

[0013] The fifth aspect disclosed in the present application provides a computer program product, including a computer program or instructions, which, when executed by a processor, implement the steps of the above-mentioned method for detecting the surface roughness of optical glass.

[0014] The beneficial effects of the present invention are: (1) The automated roughness detection of the surface of circular optical glass components is realized, solving the problem of low efficiency of roughness detection relying on manual work.

[0015] (2) The circular optical glass element is divided into multiple sector-shaped areas, which are filled into a standard input shape and then input into a convolutional neural network for surface roughness detection. This method is region-specific and solves the problem of low accuracy in directly using a convolutional neural network to detect the entire optical glass. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. 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 creative work.

[0017] Figure 1 The figure is an overall flow chart of a method for detecting the surface roughness of optical glass.

[0018] Figure 2 This is the overall structure diagram of an optical glass surface roughness detection system. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] like Figure 1 As shown, the embodiment of the present application provides a method for detecting the surface roughness of optical glass, the method comprising the following steps: Step 1: placing the entire optical glass on a pure white background, using a digital optical camera device to capture an entire image of the optical glass, and performing grayscale processing and image enhancement processing to generate a physical image, wherein the optical glass is circular; Step 2: Determine the center of the optical glass in the physical image, and divide the optical glass into N sector blocks with an angle of A through the center of the optical glass, where N=2π / A, A is less than π / 2, and N is a positive integer; Step 3: Use pure white pixels to fill all the fan-shaped blocks into rectangles with a width equal to the chord length of the fan and a length equal to the radius of the fan, generating N filled rectangles; Step 4: Input N filled rectangles into the pre-trained convolutional neural network model in sequence, determine whether there is surface roughness in the fan-shaped blocks in each filled rectangle, and calculate the number S of filled rectangles with surface roughness in the fan-shaped blocks; Step 5: Calculate the ratio of S to N to generate a roughness measurement value for the optical glass surface.

[0021] Furthermore, the step 1 specifically includes the following steps: Step 1.1: Place the entire optical glass on a pure white background, and use a digital optical camera device to collect a three-channel color image of the entire optical glass to generate an initial image; Step 1.2: Take the average value of the values ​​of each pixel in the initial image on the three channels as the grayscale value to generate a grayscale image; Step 1.3: For each pixel of the grayscale image, add 1 to the grayscale value and perform a logarithmic transformation with a natural constant as the base to generate a transformed value, and then scale the transformed value to the range of [0, 255] to generate a solid image.

[0022] Furthermore, the step 2 specifically includes the following steps: Step 2.1: Determine the center of the optical glass in the physical image and generate the center coordinates, where the center coordinates consist of the pixel position in the horizontal direction and the pixel position in the vertical direction; Step 2.2: Create N binary images of the same size as the physical image, and set all pixel values ​​of each binary image to 0. On all binary images, take the center coordinates of the circle as the center of the fan-shaped block, take angle A as the angle of the fan-shaped block, take the radius of the optical glass in the physical image as the radius of the fan-shaped block, draw sectors in sequence at non-overlapping continuous positions, and set the pixel values ​​in the drawn sectors to 1; Step 2.3: Perform logical AND operations on the N binary images and the physical image respectively, and divide the optical glass into N sector-shaped blocks with an angle of A.

[0023] Furthermore, the step 4 specifically includes the following steps: Step 4.1: Input N filled rectangles into the pre-trained convolutional neural network model in sequence, and the convolutional neural network model outputs the probability that the fan-shaped block in each filled rectangle has a rough surface, wherein the convolutional neural network model includes a convolutional layer, a pooling layer, and a fully connected layer; Step 4.2: If the probability that the fan-shaped block in the i-th filled rectangle has a rough surface is greater than 0.5, it is determined that the fan-shaped block in the i-th filled rectangle has a rough surface, where i=1,2,...,N; Step 4.3: Count the number S of filling rectangles with rough surfaces in the fan-shaped blocks.

[0024] Specifically, the convolutional neural network model includes 4 convolutional layers, 4 pooling layers and 1 fully connected layer. The training data set of the convolutional neural network model is constructed by collecting previous optical glass surface images and processing them in the same way as steps 1 to 3. Finally, the positive and negative samples are manually labeled to construct the training data set. The training data set is divided into training set and test set in a ratio of 8 to 2, and a positive and negative sample balance strategy is adopted.

[0025] Specifically, the learning rate of the convolutional neural network model during training is set to 0.001. The Adam optimizer is used in the training process, and Dropout is performed to prevent overfitting. The loss function uses the binary cross entropy loss function.

[0026] In summary, the optical glass surface roughness detection method provided in the embodiment of the present application has the following technical effects: (1) The automated roughness detection of the surface of circular optical glass components is realized, solving the problem of low efficiency of roughness detection relying on manual work.

[0027] (2) The circular optical glass element is divided into multiple sector-shaped areas, which are filled into a standard input shape and then input into a convolutional neural network for surface roughness detection. This method is region-specific and solves the problem of low accuracy in directly using a convolutional neural network to detect the entire optical glass.

[0028] Based on the same inventive concept as the method for detecting the surface roughness of optical glass in the aforementioned embodiment, Figure 2 As shown, the present application provides a system for detecting the surface roughness of optical glass, the system comprising: A collection module, wherein the collection module is used to place the entire optical glass on a pure white background, use a digital optical camera device to collect an overall image of the optical glass, and perform grayscale processing and image enhancement processing to generate a physical image, wherein the optical glass is circular; A sector module, the sector module is used to determine the center of the optical glass in the solid image, and divide the optical glass into N sector blocks with an angle of A through the center of the optical glass, where N=2π / A, A is less than π / 2, and N is a positive integer; A filling module, the filling module is used to use pure white pixels to fill all the fan-shaped blocks into rectangles with a width equal to the chord length of the fan and a length equal to the radius of the fan, to generate N filled rectangles; A determination module, wherein the determination module is used to sequentially input N filled rectangles into a pre-trained convolutional neural network model, determine whether there is surface roughness in the fan-shaped blocks in each filled rectangle, and calculate the number S of filled rectangles with surface roughness in the fan-shaped blocks; The measurement module is used to calculate the ratio of S to N and generate a measurement value of the roughness of the optical glass surface.

[0029] Through the above-mentioned detailed description of a method for detecting the surface roughness of optical glass in this specification, those skilled in the art can clearly understand a system for detecting the surface roughness of optical glass in this embodiment. Since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the description of the method part.

[0030] In one embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps of the above-mentioned method for detecting the surface roughness of optical glass when executing the computer program.

[0031] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned method for detecting the surface roughness of optical glass are implemented.

[0032] In one embodiment, a computer program product is provided, including a computer program or instructions, which implement the steps of the above-mentioned method for detecting the surface roughness of optical glass when the computer program or instructions are executed by a processor.

[0033] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0034] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for detecting the surface roughness of optical glass, characterized in that: The method comprises the following steps: Step 1: placing the entire optical glass on a pure white background, using a digital optical camera device to capture an entire image of the optical glass, and performing grayscale processing and image enhancement processing to generate a physical image, wherein the optical glass is circular; Step 2: Determine the center of the optical glass in the physical image, and divide the optical glass into N sector blocks with an angle of A through the center of the optical glass, where N=2π / A, A is less than π / 2, and N is a positive integer; Step 3: Use pure white pixels to fill all the fan-shaped blocks into rectangles with a width equal to the chord length of the fan and a length equal to the radius of the fan, generating N filled rectangles; Step 4: Input N filled rectangles into the pre-trained convolutional neural network model in sequence, determine whether there is surface roughness in the fan-shaped blocks in each filled rectangle, and calculate the number S of filled rectangles with surface roughness in the fan-shaped blocks; Step 5: Calculate the ratio of S to N to generate a roughness measurement value for the optical glass surface.

2. A method for detecting the surface roughness of optical glass according to claim 1, characterized in that: The step 1 specifically comprises the following steps: Step 1.1: Place the entire optical glass on a pure white background, and use a digital optical camera device to collect a three-channel color image of the entire optical glass to generate an initial image; Step 1.2: Take the average value of the values ​​of each pixel in the initial image on the three channels as the grayscale value to generate a grayscale image; Step 1.3: For each pixel of the grayscale image, add 1 to the grayscale value and perform a logarithmic transformation with a natural constant as the base to generate a transformed value, and then scale the transformed value to the range of [0, 255] to generate a solid image.

3. A method for detecting the surface roughness of optical glass according to claim 1, characterized in that: The step 2 specifically includes the following steps: Step 2.1: Determine the center of the optical glass in the physical image and generate the center coordinates, where the center coordinates consist of the pixel position in the horizontal direction and the pixel position in the vertical direction; Step 2.2: Create N binary images of the same size as the physical image, and set all pixel values ​​of each binary image to 0. On all binary images, take the center coordinates of the circle as the center of the sector block, angle A as the angle of the sector block, and the radius of the optical glass in the physical image as the radius of the sector block. Draw sectors in sequence at non-overlapping continuous positions, and set the pixel values ​​in the drawn sectors to 1. Step 2.3: Perform logical AND operations on the N binary images and the physical image respectively, and divide the optical glass into N sector-shaped blocks with an angle of A.

4. A method for detecting the surface roughness of optical glass according to claim 1, characterized in that: The step 4 specifically comprises the following steps: Step 4.1: Input N filled rectangles into the pre-trained convolutional neural network model in sequence, and the convolutional neural network model outputs the probability that the fan-shaped blocks in each filled rectangle have surface roughness, wherein the convolutional neural network model includes a convolutional layer, a pooling layer, and a fully connected layer; Step 4.2: If the probability that the fan-shaped block in the i-th filled rectangle has a rough surface is greater than 0.5, it is determined that the fan-shaped block in the i-th filled rectangle has a rough surface, where i=1,2,...,N; Step 4.3: Count the number S of filling rectangles with rough surfaces in the fan-shaped blocks.

5. A system for detecting the surface roughness of optical glass, the system comprising: A collection module, wherein the collection module is used to place the entire optical glass on a pure white background, use a digital optical camera device to collect an overall image of the optical glass, and perform grayscale processing and image enhancement processing to generate a physical image, wherein the optical glass is circular; A sector module, the sector module is used to determine the center of the optical glass in the solid image, and divide the optical glass into N sector blocks with an angle of A through the center of the optical glass, where N=2π / A, A is less than π / 2, and N is a positive integer; A filling module, wherein the filling module is used to use pure white pixels to fill all the fan-shaped blocks into rectangles with a width equal to the chord length of the fan and a length equal to the radius of the fan, thereby generating N filled rectangles; A determination module, wherein the determination module is used to sequentially input N filled rectangles into a pre-trained convolutional neural network model, determine whether there is surface roughness in the fan-shaped blocks in each filled rectangle, and calculate the number S of filled rectangles with surface roughness in the fan-shaped blocks; The measurement module is used to calculate the ratio of S to N and generate a measurement value of the roughness of the optical glass surface.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of a method for detecting the surface roughness of optical glass according to any one of claims 1 to 4 are implemented.

7. 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 a method for detecting the surface roughness of optical glass according to any one of claims 1 to 4 are implemented.

8. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed by a processor, the steps of a method for detecting the surface roughness of optical glass according to any one of claims 1 to 4 are implemented.

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