Telescope lens detection system and method thereof

Abstract

The invention discloses a telescope lens detection system and method, and belongs to the technical field of optical detection.The method comprises the steps that light source equipment is adjusted until a direct light source irradiates a telescope lens; collecting image data of the telescope lens through shooting equipment; receiving the image data and extracting an initial defect contour in the image data based on an edge detection algorithm; classifying pixel points in the initial defect contour based on a clustering function to obtain a marked defect area; extracting a marked defect contour in the marked defect area, and fitting the marked defect contour based on a discrete Chebyshev polynomial to generate a corresponding target defect contour; and calculating defect characteristics of the telescope lens according to the target defect contour, comparing the defect characteristics with a preset characteristic standard, generating a corresponding defect detection report, and feeding back the defect detection report to the management end. The surface defects and the optical performance of the telescope lens can be efficiently and accurately detected, and the imaging quality and the overall performance of the telescope are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of optical detection, in particular to a telescope lens detection system and method thereof. Background Art

[0002] Telescope lens inspection is an important part of optical instrument manufacturing, and its quality directly affects the imaging effect and service life of the telescope. At present, telescope lens inspection technology has made certain progress and has been widely used in different fields.

[0003] Existing telescope lens detection devices are often complex in structure and cumbersome in operation, which leads to low detection efficiency. For example, some interferometer detection devices require precise adjustment of the positions of plane mirrors and interferometers, which requires high skills of operators and the detection process is time-consuming. Secondly, the detection accuracy of existing detection methods is limited and cannot meet the needs of high-precision telescope lenses. In addition, traditional detection methods often require cutting or sampling of telescope lenses, which not only destroys the integrity of the telescope lenses, but also may introduce new errors.

[0004] Therefore, how to efficiently and accurately detect the surface defects and optical properties of telescope lenses and improve the imaging quality and overall performance of the telescope has become a problem that needs to be solved urgently. Summary of the invention

[0005] In order to solve the above problems, the present invention proposes a telescope lens inspection system and method, which can improve the accuracy and efficiency of defect detection, reduce the operational difficulty and equipment cost of detection, and at the same time can process telescope lenses of different sizes and shapes, has good versatility and flexibility, and supports automatic data storage, analysis and report generation, so as to facilitate subsequent processing by users.

[0006] In order to achieve the above object, the present invention is implemented by the following technical solutions:

[0007] A telescope lens detection system of the present invention comprises:

[0008] An adjustment module, used for adjusting the light source device until the direct light source is irradiated on the telescope lens;

[0009] A collection module, used for collecting image data of the telescope lens through a shooting device and transmitting the image data to a processing module;

[0010] A processing module, configured to receive the image data and extract an initial defect contour in the image data based on an edge detection algorithm;

[0011] A classification module, used for classifying the pixel points in the initial defect contour based on a clustering function to obtain a plurality of marked defect areas;

[0012] A fitting module, used for extracting the marked defect contour in the marked defect area, and fitting the marked defect contour based on discrete Chebyshev polynomials to generate a corresponding target defect contour;

[0013] The result module is used to calculate the defect characteristics of the telescope lens according to the target defect profile, compare the defect characteristics with the preset feature standards, generate a corresponding defect detection report and feed it back to the management end.

[0014] A further improvement of the present invention is that the horizontal position and the irradiation angle of the light source device are adjusted according to the size and shape of the telescope lens, and the light source device is moved up and down until the direct light source is perpendicular to the surface of the telescope lens.

[0015] A further improvement of the present invention is that the processing module is used to receive the image data and extract the initial defect contour in the image data based on the edge detection algorithm, specifically comprising:

[0016] After receiving the image data, the image data is subjected to Gaussian filtering based on a Gaussian filter:

[0017]

[0018] Where G represents the Gaussian function; x and y represent the pixel coordinates in the image data; σ represents the standard deviation of the Gaussian distribution, that is, the smoothness of the Gaussian filter; e represents a natural constant;

[0019] The gradient components of the image data in the x and y directions after Gaussian filtering are calculated based on the Sobel operator:

[0020]

[0021] In the formula, G x and G y Respectively represent the gradient components of the image data in the x and y directions; I represents the image data; * represents the convolution operation;

[0022] The gradient intensity and gradient direction of the image data are calculated based on the gradient components in the x and y directions:

[0023]

[0024] In the formula, M represents the gradient strength of the image data; θ represents the gradient direction of the image data; G x and G y Represent the gradient components of the image data in the x and y directions respectively;

[0025] Comparing the gradient strength of each pixel in the image data with the adjacent pixels in the gradient direction, if the gradient strength of the current pixel is not the local maximum in the gradient direction, resetting the gradient strength of the current pixel to 0, and obtaining the edge feature corresponding to the image data by retaining the local maximum in the gradient direction of the image data;

[0026] A high threshold and a low threshold are set, and a strong edge of the image data is identified by the high threshold, and connectivity of the strong edge is determined; a weak edge of the image data is restored by the low threshold, and edge points of the image data are determined according to the identification result;

[0027] An initial defect contour in the image data is determined based on the edge features and edge points.

[0028] A further improvement of the present invention is that the classification module is used to classify the pixel points in the initial defect contour based on the clustering function to obtain a plurality of marked defect areas, specifically including:

[0029] For the initial defect contour, calculate the cluster center of the pixel classification:

[0030]

[0031] In the formula, C k represents the center of the kth cluster; S k represents the pixel set of the kth cluster; a i represents the i-th pixel; |S k | represents the number of pixels in the kth cluster; Σ represents the summation symbol;

[0032] Each pixel point in the initial defect contour is divided into the cluster center C closest to it. k The cluster to which it belongs;

[0033] The pixel points in the initial defect contour are classified according to the segmentation result, and the classified pixel points are post-processed based on morphological operation and connectivity analysis to determine a plurality of marked defect areas.

[0034] A further improvement of the present invention is that: the morphological operation includes expansion, erosion, opening operation and closing operation; the connectivity analysis includes traversing the pixel points in the initial defect contour and determining the connectivity according to the neighborhood relationship of the pixel points.

[0035] A further improvement of the present invention is that the result module is used to calculate the defect characteristics of the telescope lens according to the target defect profile, specifically including:

[0036] The defect characteristics include defect size characteristics and defect shape characteristics;

[0037] The defect size characteristics include defect length, width and area;

[0038] Determine the boundary of the target defect contour, and calculate the maximum distance between two points on the boundary as the defect length, the second largest distance as the defect width, and the second largest distance is perpendicular to the maximum distance;

[0039] The defect area is calculated based on the pixel counting method:

[0040]

[0041] Where A represents the defect area; N represents the total number of pixels in the area corresponding to the target defect contour; L(x j ,y j ) represents the coordinates (x j ,y j ) at the pixel value; Σ represents the summation symbol;

[0042] The defect shape characteristics include defect roundness and rectangularity;

[0043] The defect perimeter is obtained by traversing the boundary pixels of the area corresponding to the target defect contour, and the defect roundness is calculated based on the defect area and the defect perimeter:

[0044]

[0045] Where, K represents the roundness of the defect; A represents the defect area; P represents the circumference of the defect;

[0046] The target defect contour corresponding area is rotated, the circumscribed rectangle area at each rotation angle is calculated, the minimum circumscribed rectangle area is determined, and the defect rectangularity is calculated based on the defect area and the minimum circumscribed rectangle area:

[0047]

[0048] Where R represents the defect rectangularity; A represents the defect area; A0 represents the minimum circumscribed rectangular area.

[0049] A further improvement of the present invention is that the defect detection report includes the defect location, quantity, type and severity of the telescope lens.

[0050] A telescope lens detection method of the present invention comprises:

[0051] Adjust the light source equipment until the direct light source shines on the telescope lens;

[0052] Collect image data of the telescope lens by means of a photographing device and transmit the image data to a processing module;

[0053] Receiving the image data, and extracting an initial defect contour in the image data based on an edge detection algorithm;

[0054] Classifying the pixel points in the initial defect contour based on a clustering function to obtain a plurality of marked defect areas;

[0055] Extracting a marked defect contour in the marked defect area, and fitting the marked defect contour based on discrete Chebyshev polynomials to generate a corresponding target defect contour;

[0056] The defect characteristics of the telescope lens are calculated according to the target defect profile, and the defect characteristics are compared with the preset characteristic standards, and a corresponding defect detection report is generated and fed back to the management end.

[0057] The beneficial effects of the present invention are as follows: the present invention proposes a telescope lens detection system and method thereof, which can adjust a light source device until a direct light source is irradiated on the telescope lens through an adjustment module, an acquisition module, a processing module, a classification module, a fitting module and a result module; collect image data of the telescope lens through a shooting device and transmit it to a processing module; receive image data, and extract an initial defect contour in the image data based on an edge detection algorithm; classify pixel points in the initial defect contour based on a clustering function to obtain a plurality of marked defect regions; extract a marked defect contour in the marked defect region, and fit the marked defect contour based on a discrete Chebyshev polynomial , generate the corresponding target defect contour; calculate the defect characteristics of the telescope lens according to the target defect contour, compare the defect characteristics with the preset characteristic standard, generate the corresponding defect detection report and feed it back to the management end, so that the pixel points can be fitted by clustering function and discrete Chebyshev polynomial, thereby improving the accuracy of defect detection; and the system of the present invention is simple to assemble and easy to carry, reducing the difficulty of operation and equipment cost; at the same time, it can process telescope lenses of different sizes and shapes, with good versatility and flexibility; and it can efficiently process the received image data, support automatic data storage, analysis and report generation, and facilitate users to perform subsequent processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 A schematic diagram of the structure of a telescope lens detection system provided by an embodiment of the present invention;

[0059] Figure 2 A schematic diagram of a process for detecting a telescope lens provided by an embodiment of the present invention;

[0060] Figure 3 A schematic diagram of the hardware structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0061] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings 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.

[0062] See also Figure 1 , is a schematic diagram of the structure of a telescope lens detection system provided by an embodiment of the present invention, the detection system comprising:

[0063] An adjustment module, used for adjusting the light source device until the direct light source is irradiated on the telescope lens;

[0064] A collection module, used for collecting image data of the telescope lens through a shooting device and transmitting the image data to a processing module;

[0065] A processing module, configured to receive the image data and extract an initial defect contour in the image data based on an edge detection algorithm;

[0066] A classification module, used for classifying the pixel points in the initial defect contour based on a clustering function to obtain a plurality of marked defect areas;

[0067] A fitting module, used for extracting the marked defect contour in the marked defect area, and fitting the marked defect contour based on discrete Chebyshev polynomials to generate a corresponding target defect contour;

[0068] The result module is used to calculate the defect characteristics of the telescope lens according to the target defect profile, compare the defect characteristics with the preset feature standards, generate a corresponding defect detection report and feed it back to the management end.

[0069] Among them, the adjustment module has a high positioning accuracy and flexible adjustment capabilities, which can accurately adjust the position and angle of the light source equipment until the high-intensity direct light source is accurately focused on the surface of the telescope lens.

[0070] The acquisition module can capture the image data of the telescope lens under direct light source through integrated shooting equipment, and is equipped with an efficient data transmission mechanism to ensure that the image data can be transmitted to the processing module in real time and losslessly for further analysis.

[0071] The processing module can receive the image data from the acquisition module and process it using an image processing algorithm. Specifically, the processing module can analyze the image data based on an edge detection algorithm to accurately extract the initial defect profile of the telescope lens surface.

[0072] The classification module can further classify the pixels in the initial defect contour and use the clustering function to group the pixels with similar features into one category to form multiple independent marked defect areas, thereby helping to simplify the complex defect image into multiple independent units that are easy to analyze and facilitate subsequent fitting operations.

[0073] The fitting module can first extract the contour information of each marked defect area, and use discrete Chebyshev polynomials to perform high-precision fitting on these contours to generate smoother and more accurate target defect contours, thereby accurately quantifying defect characteristics and improving detection accuracy.

[0074] The result module can calculate the defect characteristic parameters of the telescope lens according to the fitted target defect profile, and compare and analyze these characteristic parameters with the preset characteristic standards, automatically generate a test report containing detailed defect information, and promptly feedback to the management end through the network or other communication means so that corresponding quality control measures can be taken in time.

[0075] The horizontal position and the irradiation angle of the light source device are adjusted according to the size and shape of the telescope lens, and the light source device is moved up and down until the direct light source is perpendicular to the surface of the telescope lens.

[0076] It should be noted that the horizontal position and illumination angle of the light source device can be adjusted accordingly according to the size and shape of the telescope lens. At the same time, a precision adjustment mechanism can be used to move the light source device up and down, and repeated calibration can be performed until the axis of the direct light source is completely perpendicular to the surface of the telescope lens.

[0077] The processing module is used to receive the image data and extract the initial defect contour in the image data based on an edge detection algorithm, specifically comprising:

[0078] After receiving the image data, the image data is subjected to Gaussian filtering based on a Gaussian filter:

[0079]

[0080] Where G represents the Gaussian function; x and y represent the pixel coordinates in the image data; σ represents the standard deviation of the Gaussian distribution, that is, the smoothness of the Gaussian filter; e represents a natural constant;

[0081] The gradient components of the image data in the x and y directions after Gaussian filtering are calculated based on the Sobel operator:

[0082]

[0083] In the formula, G x and G yRespectively represent the gradient components of the image data in the x and y directions; I represents the image data; * represents the convolution operation;

[0084] The gradient intensity and gradient direction of the image data are calculated based on the gradient components in the x and y directions:

[0085]

[0086] In the formula, M represents the gradient strength of the image data; θ represents the gradient direction of the image data; G x and G y Represent the gradient components of the image data in the x and y directions respectively;

[0087] Comparing the gradient strength of each pixel in the image data with the adjacent pixels in the gradient direction, if the gradient strength of the current pixel is not the local maximum in the gradient direction, resetting the gradient strength of the current pixel to 0, and obtaining the edge feature corresponding to the image data by retaining the local maximum in the gradient direction of the image data;

[0088] A high threshold and a low threshold are set, and a strong edge of the image data is identified by the high threshold, and connectivity of the strong edge is determined; a weak edge of the image data is restored by the low threshold, and edge points of the image data are determined according to the identification result;

[0089] An initial defect contour in the image data is determined based on the edge features and edge points.

[0090] The classification module is used to classify the pixel points in the initial defect contour based on the clustering function to obtain multiple marked defect areas, specifically including:

[0091] For the initial defect contour, calculate the cluster center of the pixel classification:

[0092]

[0093] In the formula, C k represents the center of the kth cluster; S k represents the pixel set of the kth cluster; a i represents the i-th pixel; |S k | represents the number of pixels in the kth cluster; Σ represents the summation symbol;

[0094] Each pixel point in the initial defect contour is divided into the cluster center C closest to it. k The cluster to which it belongs;

[0095] The pixel points in the initial defect contour are classified according to the segmentation result, and the classified pixel points are post-processed based on morphological operation and connectivity analysis to determine a plurality of marked defect areas.

[0096] The morphological operation includes dilation, erosion, opening operation and closing operation; the connectivity analysis includes traversing the pixel points in the initial defect contour and determining the connectivity according to the neighborhood relationship of the pixel points.

[0097] It should be noted that the classified pixels can be post-processed, including image processing techniques such as morphological operations and connectivity analysis, so as to remove isolated points, merge adjacent similar points, and mark defective areas.

[0098] The morphological operation includes dilation, erosion, opening operation and closing operation; the connectivity analysis includes traversing the pixel points in the initial defect contour and determining the connectivity according to the neighborhood relationship of the pixel points.

[0099] Specifically, the dilation operation is used to expand the boundary of an object; the erosion operation is used to shrink the boundary of an object; the opening operation is used to remove small objects or noise by first corroding and then dilating; the closing operation is used to fill small holes or connect adjacent objects by first dilating and then corroding.

[0100] Furthermore, the marked defect contour in the marked defect area can be extracted, and the marked defect contour can be fitted based on discrete Chebyshev polynomials to generate the corresponding target defect contour. By adjusting the fitting parameters, such as the order of the polynomial, the number of fitting points, etc., and comparing different fitting results, the fitting accuracy can be improved. The fitted contour can be subsequently verified and evaluated to ensure that the fitting result conforms to the shape and characteristics of the actual defect.

[0101] The result module is used to calculate the defect characteristics of the telescope lens according to the target defect profile, and specifically includes:

[0102] The defect characteristics include defect size characteristics and defect shape characteristics;

[0103] The defect size characteristics include defect length, width and area;

[0104] Determine the boundary of the target defect contour, and calculate the maximum distance between two points on the boundary as the defect length, the second largest distance as the defect width, and the second largest distance is perpendicular to the maximum distance;

[0105] The defect area is calculated based on the pixel counting method:

[0106]

[0107] Where A represents the defect area; N represents the total number of pixels in the area corresponding to the target defect contour; L(x j ,y j ) represents the coordinates (x j ,y j ) at the pixel value; Σ represents the summation symbol;

[0108] The defect shape characteristics include defect roundness and rectangularity;

[0109] The defect perimeter is obtained by traversing the boundary pixels of the area corresponding to the target defect contour, and the defect roundness is calculated based on the defect area and the defect perimeter:

[0110]

[0111] Where, K represents the roundness of the defect; A represents the defect area; P represents the circumference of the defect;

[0112] The target defect contour corresponding area is rotated, the circumscribed rectangle area at each rotation angle is calculated, the minimum circumscribed rectangle area is determined, and the defect rectangularity is calculated based on the defect area and the minimum circumscribed rectangle area:

[0113]

[0114] Where R represents the defect rectangularity; A represents the defect area; A0 represents the minimum circumscribed rectangular area.

[0115] The defect detection report includes the defect location, quantity, type and severity of the telescope lens.

[0116] It is understandable that defect characteristics, as an important parameter to measure the nature of lens defects, mainly include defect size characteristics and defect shape characteristics. The defect size characteristics directly reflect the physical scale of the defect, while the defect shape characteristics reveal the geometric shape of the defect.

[0117] For the defect size characteristics, we can first determine the precise boundary of the target defect contour through high-precision image processing technology, and use the distance calculation logic between boundary pixels to identify the maximum distance between two points on the boundary as the defect length; at the same time, the distance between two points that is perpendicular to the maximum distance and the second largest distance can be used as the defect width to ensure the accuracy of the defect length and width measurement.

[0118] Based on the pixel counting method, we can first determine the corresponding area of ​​the target defect contour, and then count the total number of all pixels in the area, and then add up the grayscale value of each pixel to obtain the corresponding defect area.

[0119] The defect roundness is a measure of how close the defect shape is to a circle. By traversing the boundary pixels of the area corresponding to the target defect contour, the defect perimeter can be calculated, and then the defect roundness can be obtained by combining the defect area. It should be noted that the closer the defect roundness value is to 1, the closer the defect shape is to an ideal circle.

[0120] Defect rectangularity refers to the degree of similarity between the defect shape and a rectangle. By rotating the corresponding area of ​​the target defect contour, calculating the circumscribed rectangle area at each rotation angle, and determining the minimum value, i.e., the minimum circumscribed rectangle area, the corresponding defect rectangularity can be obtained by combining the defect area. It should be noted that the closer the rectangularity value is to 1, the closer the defect shape is to a rectangle.

[0121] The defect detection report includes the location, quantity, type and severity of defects in the telescope lens, which helps to optimize the production process, reduce defects, and improve the overall optical performance of the telescope and user satisfaction.

[0122] Specifically, the location of defects can be obtained through precise positioning through the coordinate system; the number of defects can be determined by counting the frequency of occurrence of various defects; the type of defects can be obtained through classification based on the characteristics of the defects, such as scratches, bubbles and inclusions; and the severity of the defects can be comprehensively determined by combining the size and shape characteristics of the defects.

[0123] Among them, see Figure 2 , is a flow chart of a telescope lens detection method provided by an embodiment of the present invention, the detection method comprising:

[0124] S1, adjust the light source equipment until the direct light source shines on the telescope lens;

[0125] S2, collecting image data of the telescope lens through a shooting device and transmitting it to a processing module;

[0126] S3, receiving the image data, and extracting an initial defect contour in the image data based on an edge detection algorithm;

[0127] S4, classifying the pixel points in the initial defect contour based on a clustering function to obtain a plurality of marked defect areas;

[0128] S5, extracting the marked defect contour in the marked defect area, and fitting the marked defect contour based on discrete Chebyshev polynomials to generate a corresponding target defect contour;

[0129] S6, calculating the defect characteristics of the telescope lens according to the target defect profile, comparing the defect characteristics with the preset characteristic standards, generating a corresponding defect detection report and feeding it back to the management end.

[0130] It should be noted that Figure 2 The execution subject of the method shown may be software and / or hardware devices. The execution subject of the present application may include but is not limited to at least one of the following: user equipment, network equipment, etc. Among them, the user equipment may include but is not limited to computers, smart phones, personal digital assistants (Personal Digital Assistant, referred to as: PDA) and the electronic devices mentioned above. The network equipment may include but is not limited to a single network server, a server group consisting of multiple network servers, or a cloud based on cloud computing consisting of a large number of computers or network servers, wherein cloud computing is a kind of distributed computing, a super virtual computer composed of a group of loosely coupled computers. This embodiment does not limit this.

[0131] See also Figure 3 , is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present invention, the electronic device 30 includes: a processor 31, a memory 32 and a computer program; wherein:

[0132] The memory 32 is used to store the computer program, and the memory may also be a flash memory. The computer program is, for example, an application program, a functional module, etc. for implementing the above method.

[0133] The processor 31 is used to execute the computer program stored in the memory to implement each step performed by the device in the above method. For details, please refer to the relevant description in the above method embodiment.

[0134] Optionally, the memory 32 may be independent or integrated with the processor 31 .

[0135] When the memory 32 is a device independent of the processor 31, the device may further include:

[0136] The bus 33 is used to connect the memory 32 and the processor 31 .

[0137] The present invention also provides a readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, it is used to implement the methods provided by the various embodiments described above.

[0138] Among them, the readable storage medium can be a computer storage medium or a communication medium. The communication medium includes any medium that facilitates the transmission of a computer program from one place to another. The computer storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer. For example, a readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application-specific integrated circuit (Application Specific Integrated Circuits, abbreviated as: ASIC). In addition, the ASIC can be located in a user device. Of course, the processor and the readable storage medium can also exist in a communication device as discrete components. The readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, and an optical data storage device, etc.

[0139] The present invention also provides a program product, which includes an execution instruction, which is stored in a readable storage medium. At least one processor of a device can read the execution instruction from the readable storage medium, and at least one processor executes the execution instruction so that the device implements the methods provided in the above various embodiments.

[0140] In the embodiments of the above-mentioned devices, it should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the present invention may be directly implemented as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.

[0141] Through the introduction of the above embodiments, the present invention uses a telescope lens detection system and method thereof, through an adjustment module, an acquisition module, a processing module, a classification module, a fitting module and a result module, to adjust the light source device until the direct light source is irradiated on the telescope lens; collect image data of the telescope lens through a shooting device and transmit it to the processing module; receive image data, and extract the initial defect contour in the image data based on an edge detection algorithm; classify the pixel points in the initial defect contour based on a clustering function to obtain multiple marked defect areas; extract the marked defect contour in the marked defect area, and fit the marked defect contour based on a discrete Chebyshev polynomial. Generate a corresponding target defect contour; calculate the defect characteristics of the telescope lens according to the target defect contour, compare the defect characteristics with the preset characteristic standard, generate a corresponding defect detection report and feed it back to the management end, so that pixel points can be fitted by clustering functions and discrete Chebyshev polynomials, thereby improving the accuracy of defect detection; and the system of the present invention is simple to assemble and easy to carry, reducing the difficulty of operation and equipment cost; at the same time, it can process telescope lenses of different sizes and shapes, with good versatility and flexibility; and can efficiently process the received image data, support automatic data storage, analysis and report generation, and facilitate users to perform subsequent processing.

[0142] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A telescope lens detection system, characterized in that: The system comprises: An adjustment module, used for adjusting the light source device until the direct light source is irradiated on the telescope lens; A collection module, used for collecting image data of the telescope lens through a shooting device and transmitting the image data to a processing module; A processing module, configured to receive the image data and extract an initial defect contour in the image data based on an edge detection algorithm; A classification module, used for classifying the pixel points in the initial defect contour based on a clustering function to obtain a plurality of marked defect areas; A fitting module, used for extracting the marked defect contour in the marked defect area, and fitting the marked defect contour based on discrete Chebyshev polynomials to generate a corresponding target defect contour; The result module is used to calculate the defect characteristics of the telescope lens according to the target defect profile, compare the defect characteristics with the preset feature standards, generate a corresponding defect detection report and feed it back to the management end.

2. A telescope lens detection system according to claim 1, characterized in that: The horizontal position and the irradiation angle of the light source device are adjusted according to the size and shape of the telescope lens, and the light source device is moved up and down until the direct light source is perpendicular to the surface of the telescope lens.

3. A telescope lens detection system according to claim 1, characterized in that: The processing module is used to receive the image data and extract the initial defect contour in the image data based on an edge detection algorithm, specifically comprising: After receiving the image data, the image data is subjected to Gaussian filtering based on a Gaussian filter: Where G represents the Gaussian function; x and y represent the pixel coordinates in the image data; σ represents the standard deviation of the Gaussian distribution, that is, the smoothness of the Gaussian filter; e represents a natural constant; The gradient components of the image data in the x and y directions after Gaussian filtering are calculated based on the Sobel operator: In the formula, G x and G y Respectively represent the gradient components of the image data in the x and y directions; I represents the image data; * represents the convolution operation; The gradient intensity and gradient direction of the image data are calculated based on the gradient components in the x and y directions: In the formula, M represents the gradient strength of the image data; θ represents the gradient direction of the image data; G x and G y Represent the gradient components of the image data in the x and y directions respectively; Comparing the gradient strength of each pixel in the image data with the adjacent pixels in the gradient direction, if the gradient strength of the current pixel is not the local maximum in the gradient direction, resetting the gradient strength of the current pixel to 0, and obtaining the edge feature corresponding to the image data by retaining the local maximum in the gradient direction of the image data; A high threshold and a low threshold are set, and a strong edge of the image data is identified by the high threshold, and connectivity of the strong edge is determined; a weak edge of the image data is restored by the low threshold, and edge points of the image data are determined according to the identification result; An initial defect contour in the image data is determined based on the edge features and edge points.

4. A telescope lens detection system according to claim 1, characterized in that: The classification module is used to classify the pixel points in the initial defect contour based on the clustering function to obtain multiple marked defect areas, specifically including: For the initial defect contour, calculate the cluster center of the pixel classification: In the formula, C k represents the center of the kth cluster; S k represents the pixel set of the kth cluster; a i represents the i-th pixel; |S k | represents the number of pixels in the kth cluster; Σ represents the summation symbol; Each pixel point in the initial defect contour is divided into the cluster center C closest to it. k The cluster to which it belongs; The pixel points in the initial defect contour are classified according to the segmentation result, and the classified pixel points are post-processed based on morphological operation and connectivity analysis to determine a plurality of marked defect areas.

5. A telescope lens detection system according to claim 4, characterized in that: The morphological operation includes dilation, erosion, opening operation and closing operation; the connectivity analysis includes traversing the pixel points in the initial defect contour and determining the connectivity according to the neighborhood relationship of the pixel points.

6. A telescope lens detection system according to claim 1, characterized in that: The result module is used to calculate the defect features of the telescope lens according to the target defect profile, specifically including: the defect features include defect size features and defect shape features; The defect size characteristics include defect length, width and area; Determine the boundary of the target defect contour, and calculate the maximum distance between two points on the boundary as the defect length, the second largest distance as the defect width, and the second largest distance is perpendicular to the maximum distance; The defect area is calculated based on the pixel counting method: Where A represents the defect area; N represents the total number of pixels in the area corresponding to the target defect contour; L(x j ,y j ) represents the coordinates (x j ,y j ) at the pixel value; Σ represents the summation symbol; The defect shape characteristics include defect roundness and rectangularity; The defect perimeter is obtained by traversing the boundary pixels of the area corresponding to the target defect contour, and the defect roundness is calculated based on the defect area and the defect perimeter: Where, K represents the roundness of the defect; A represents the defect area; P represents the circumference of the defect; The target defect contour corresponding area is rotated, the circumscribed rectangle area at each rotation angle is calculated, the minimum circumscribed rectangle area is determined, and the defect rectangularity is calculated based on the defect area and the minimum circumscribed rectangle area: Where R represents the defect rectangularity; A represents the defect area; A0 represents the minimum circumscribed rectangular area.

7. A telescope lens detection system according to claim 1, characterized in that: The defect detection report includes the defect location, quantity, type and severity of the telescope lens.

8. A detection method for a telescope lens detection system according to any one of claims 1 to 7, characterized in that: The method comprises: Adjust the light source equipment until the direct light source shines on the telescope lens; Collect image data of the telescope lens by means of a photographing device and transmit the image data to a processing module; Receiving the image data, and extracting an initial defect contour in the image data based on an edge detection algorithm; Classifying the pixel points in the initial defect contour based on a clustering function to obtain a plurality of marked defect areas; Extracting a marked defect contour in the marked defect area, and fitting the marked defect contour based on discrete Chebyshev polynomials to generate a corresponding target defect contour; The defect characteristics of the telescope lens are calculated according to the target defect profile, and the defect characteristics are compared with the preset characteristic standards, and a corresponding defect detection report is generated and fed back to the management end.

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