Lens detection platform and method and machine vision detection device

Abstract

The invention relates to the technical field of machine vision detection, and discloses a lens detection platform and method and a machine vision detection device, and the detection platform comprises a reference platform which has preset rigidity and is used for providing a physical reference; the Z-axis movement assembly is installed on the reference platform and used for bearing the to-be-measured lens and driving the to-be-measured lens to move in the Z-axis direction, and the Z-axis movement assembly comprises a linear driving unit and a grating ruler used for feeding back position data; the optical imaging module is arranged opposite to the to-be-measured lens; the control processing unit is electrically connected with the Z-axis movement assembly and the image sensor; the reference platform with the preset rigidity is arranged to provide sufficient supporting physical rigidity, and the Z-axis movement assembly with feedback is arranged, so that a reliable and accurate infrastructure is laid for multi-parameter automatic detection of the lens, and subsequent automatic control, image processing and parameter calculation can be established on a solid and credible data basis.

Description

Technical Field

[0001] This invention relates to the field of machine vision inspection technology, and in particular to a lens inspection platform, method and machine vision inspection device. Background Technology

[0002] In the production and quality inspection of industrial lenses, it is necessary to accurately measure their key optical performance parameters (such as resolution, depth of field, distortion, and uniformity). Traditional testing methods typically have the following problems:

[0003] 1. The separate devices result in low efficiency and accuracy. Although multiple detection functions can be integrated into a single device, when performing measurements such as resolution and depth of field that require extremely high reference stability and displacement accuracy, the base is easily affected by changes in ambient temperature and its own stress, resulting in micro-deformation. This causes the optical calibration reference to drift, making it difficult to establish a reliable correlation between the measurement results of different parameters.

[0004] 2. Insufficient automation and motion control precision: Existing integrated platforms mostly employ open-loop or low-precision feedback motion mechanisms, which cannot achieve highly repeatable and accurate positioning, nor can they acquire and track the actual axial position of the lens in real time. This results in the reliability and repeatability of the results for detection projects that rely on precise displacement feedback, such as depth-of-field measurement, failing to meet industrial-grade requirements.

[0005] Therefore, improvements to existing technologies are necessary. Summary of the Invention

[0006] This invention provides a lens inspection platform, method, and machine vision inspection device to solve the problems existing in the prior art.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] A lens inspection platform, comprising:

[0009] A reference platform with preset stiffness is used to provide a physical reference.

[0010] The Z-axis motion assembly is mounted on the reference platform and is used to carry the lens under test and drive the lens under test to move along the Z-axis direction. The Z-axis motion assembly includes a linear drive unit and a grating ruler for feedback position data.

[0011] An optical imaging module, disposed opposite to the lens under test, includes a standard light source, an image sensor, and a test calibration component;

[0012] The control and processing unit is electrically connected to the Z-axis motion assembly and the image sensor, respectively.

[0013] The control processing unit is used to control the Z-axis motion component to drive the lens under test to perform the detection process based on the position data fed back by the grating ruler, and to call the image analysis algorithm to process the acquired image in order to obtain the detection parameters of the lens under test.

[0014] In some alternative implementations, the reference platform is a marble platform base.

[0015] In some alternative implementations, the test calibration components include one or more of a transmission test chart and a grid distortion transmission test chart.

[0016] In some optional implementations, when the control processing unit executes the detection process, it first executes focusing logic to determine the focal plane position of the lens under test through image sharpness analysis.

[0017] In some optional implementations, the detection parameters include depth of field. The control processing unit controls the Z-axis motion component to perform boundary scanning on both sides of the focal plane position based on the focal plane position and its corresponding peak sharpness value, and determines the depth of field parameters based on the displacement range when the sharpness value drops to a preset threshold.

[0018] In some optional implementations, the detection parameters include resolution, the control processing unit invokes the image analysis algorithm to identify oblique regions in the image, and calculates the modulation transfer function (MTF) value based on the spatial frequency response (SFR) method.

[0019] In some alternative implementations, the detection parameters include distortion, and the control processing unit obtains TV distortion parameters by identifying feature points in the calibration image and calculating the geometric deviation of the feature points relative to the distortion-free model.

[0020] In some alternative implementations, the detection parameters include uniformity, and the control processing unit assesses illumination attenuation by calculating the ratio of gray values ​​between the central and edge regions of the image.

[0021] This invention also provides an automated multi-parameter lens inspection method, implemented based on the lens inspection platform described in any of the preceding claims, comprising the following steps:

[0022] The Z-axis motion component is controlled to drive the lens under test to scan within a preset range. The focal plane position is determined by the image analysis algorithm and the lens under test is driven to reset to the focal plane position.

[0023] Based on the focal plane position or the offset of the focal plane position, images are acquired in conjunction with the corresponding test calibration components, and multiple parameters including resolution, depth of field, distortion, and uniformity are extracted.

[0024] The test data of each parameter are summarized and combined with the preset pass / fail criteria to generate an integrated test report containing test images.

[0025] The present invention also provides a machine vision inspection device, including a memory and a processor, wherein the processor executes a computer program stored in the memory to implement the lens multi-parameter automated inspection method as described above.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] The present invention provides a lens inspection platform, method and machine vision inspection device. By setting a reference platform with preset stiffness to provide sufficient physical stiffness for support, and setting a Z-axis motion component with feedback, a reliable and accurate infrastructure is laid for the automated inspection of multiple parameters of lenses, so that subsequent automated control, image processing and parameter calculation can be based on a solid and reliable data foundation.

[0028] The present invention has other features and advantages, which will be apparent from or will be set forth in detail in the accompanying drawings and the following detailed description, which together serve to explain the particular principles of the invention. Attached Figure Description

[0029] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0030] Figure 1 This is a schematic diagram of the structure of a lens inspection platform provided in an embodiment of the present invention;

[0031] Figure 2 This is a flowchart of an automated multi-parameter detection method for lenses provided in an embodiment of the present invention.

[0032] Reference numerals: 10, reference platform; 20, Z-axis motion assembly; 30, optical imaging module. Detailed Implementation

[0033] To illustrate the possible application scenarios, technical principles, implementable specific solutions, and achievable objectives and effects of this application in detail, the following description, in conjunction with the listed specific embodiments and accompanying drawings, provides a detailed explanation. The embodiments described herein are merely illustrative of the technical solutions of this application and are therefore intended to limit the scope of protection of this application.

[0034] In this document, the term "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The term "embodiment" appearing in various places throughout the specification does not necessarily refer to the same embodiment, nor does it specifically limit its independence or connection with other embodiments. In principle, in this application, as long as there are no technical contradictions or conflicts, the technical features mentioned in each embodiment can be combined in any way to form corresponding implementable technical solutions.

[0035] Unless otherwise defined, the technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the use of related terms herein is merely for the purpose of describing particular embodiments and is not intended to limit this application.

[0036] In the description of this application, the term "and / or" is used to describe the logical relationship between objects, indicating that three relationships can exist. For example, A and / or B means: A exists, B exists, and A and B exist simultaneously. Additionally, the character " / " in this document generally indicates that the preceding and following objects have an "or" logical relationship.

[0037] In this application, terms such as “first” and “second” are used only to distinguish one entity or operation from another, and do not necessarily require or imply any actual quantity, hierarchy or order relationship between these entities or operations.

[0038] Unless otherwise specified, the use of terms such as “comprising,” “including,” “having,” or other similar expressions in this application is intended to cover non-exclusive inclusion, which does not exclude the presence of additional elements in a process, method, or product that includes the stated elements, such that a process, method, or product that includes a list of elements may include not only those defined elements but also other elements not expressly listed, or elements inherent to such a process, method, or product.

[0039] Similar to the understanding in the Examination Guidelines, in this application, expressions such as "greater than," "less than," and "exceeding" are understood to exclude the stated number; expressions such as "above," "below," and "within" are understood to include the stated number. Furthermore, in the description of the embodiments in this application, "multiple" means two or more (including two), and similar expressions related to "multiple" are also understood in this way, such as "multiple groups" and "multiple times," unless otherwise explicitly specified.

[0040] In the description of the embodiments of this application, the space-related expressions used, such as "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "vertical," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," and "circumferential," indicate the orientation or positional relationship based on the orientation or positional relationship shown in the specific embodiments or drawings. They are only for the purpose of describing the specific embodiments of this application or for the reader's understanding, and do not indicate or imply that the device or component referred to must have a specific position, a specific orientation, or be constructed or operated in a specific orientation. Therefore, they should not be construed as limitations on the embodiments of this application.

[0041] Unless otherwise expressly specified or limited, the terms "installation," "connection," "linking," "fixing," and "setting," as used in the description of the embodiments of this application, should be interpreted broadly. For example, "connection" can be a fixed connection, a detachable connection, or an integral setting; it can be a mechanical connection, an electrical connection, or a communication connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be the internal connection of two components or the interaction between two components. For those skilled in the art to which this application pertains, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0042] Please refer to Figure 1 This invention provides a lens inspection platform.

[0043] In this embodiment, the detection platform includes a reference platform 10, a Z-axis motion component 20, an optical imaging module 30, and a control and processing unit. The components work together to achieve automated and high-precision detection of multiple lens parameters.

[0044] Specifically, the reference platform 10 has a preset stiffness to provide a physical reference; it provides a stable physical reference for the entire detection system and effectively resists interference caused by external vibration and temperature changes.

[0045] The Z-axis motion assembly 20 is mounted on the reference platform 10 and is used to support the lens under test and drive the lens under test to move along the Z-axis direction. The Z-axis motion assembly 20 includes a linear drive unit and a grating ruler for feedback of position data, which is used to provide real-time feedback of position data and ensure the accuracy of displacement control.

[0046] The optical imaging module 30 is positioned opposite the lens under test and includes a standard light source, an image sensor, and a test calibration component. The standard light source provides a stable and uniform illumination environment, the test calibration component provides a standard imaging target for parameter detection, and the image sensor is responsible for acquiring images from the lens under test.

[0047] The control and processing unit is electrically connected to the Z-axis motion component 20 and the image sensor, respectively. The control and processing unit is used to control the Z-axis motion component 20 to drive the lens under test to perform the detection process according to the position data fed back by the grating ruler, and to call the image analysis algorithm to process the acquired image to obtain the detection parameters of the lens under test, thereby realizing the fully automated detection process.

[0048] In some alternative implementations, the reference platform 10 is a marble platform base.

[0049] Understandably, marble has an extremely low coefficient of thermal expansion and excellent thermal stability, which can effectively reduce the impact of ambient temperature fluctuations on the platform itself and prevent the reference plane from warping or expanding due to temperature changes. At the same time, marble has a high rigidity and dense structure, strong vibration resistance, and minimal deformation under external force or external vibration, which can effectively suppress vibration transmission and provide a stable installation and working reference for the Z-axis motion component 20 and the optical imaging module 30.

[0050] In some alternative implementations, the test calibration components include one or more of a transmission test chart and a grid distortion transmission test chart.

[0051] Using at least two test cards, both fixed on a standard-sized glass plate, ensures the stability and consistency of the imaging position.

[0052] In this embodiment, the transmission test card is designed in accordance with the ISO12233 standard, and black and white line patterns of different spatial frequencies are printed on it to meet the needs of lens resolution detection and provide a clear physical benchmark for the quantitative calculation of resolution parameters.

[0053] In addition, the grid distortion transmission test card is printed with a regular square grid pattern. The actual physical spacing of the grid is preset and can be used to detect lens distortion parameters. It quantifies the degree of distortion by capturing the geometric changes after the grid is imaged.

[0054] In some optional implementations, when the control processing unit executes the detection process, it first executes the focusing logic and determines the focal plane position of the lens under test through image sharpness analysis, providing a clear imaging basis for subsequent parameter detection.

[0055] Furthermore, the autofocus process is based on the coordinated operation of the Z-axis motion component 20 and the image sensor, as detailed below:

[0056] First, the control processing unit controls the Z-axis motion component 20 to drive the lens under test to perform step scanning within the estimated focal plane range. During the scanning process, the image sensor continuously acquires images at a preset frequency.

[0057] Subsequently, each captured image frame is transmitted to the control processing unit, where it is processed by its built-in image sharpness enhancement algorithm. The image sharpness is quantified by calculating the image's grayscale gradient, variance, and other indicators.

[0058] Finally, as the Z-axis position changes, the image sharpness exhibits a single-peak variation pattern of first increasing and then decreasing. The control processing unit identifies the image corresponding to the maximum sharpness value, locks the Z-axis position corresponding to that image, which is the optimal focal plane position of the lens under test, and drives the Z-axis motion component 20 to move the lens under test back to that focal plane position.

[0059] Understandably, the aforementioned autofocus method requires no manual intervention and is completed by the collaboration of algorithms and hardware, avoiding the subjectivity and instability of manual focusing, ensuring the accuracy and repeatability of focal plane positioning, and providing key support for the accuracy of subsequent detection of parameters such as resolution and depth of field.

[0060] In some optional implementations, the detection parameters include depth of field. The control processing unit controls the Z-axis motion component 20 to perform boundary scanning on both sides of the focal plane position based on the focal plane position and its corresponding peak sharpness value, and determines the depth of field parameters based on the displacement range when the sharpness value drops to a preset threshold.

[0061] After completing autofocus and determining the focal plane position, the control processing unit performs a depth-of-field detection process based on the focal plane position and the corresponding peak sharpness value (MTF_peak).

[0062] The specific implementation process of autofocus is as follows:

[0063] First, record the current Z-axis coordinate as Z0 at the optimal focal plane position, move forward and backward by 1 / 2 of the estimated distance, and measure the MTF value at each step position through an image analysis algorithm, denoted as MTF_peak;

[0064] Subsequently, the control processing unit plans the depth boundary scanning path. The Z-axis motion component 20 starts from position Z0 and performs step scanning in the directions closer to and farther from the test calibration component (i.e., the positive and negative directions of the Z-axis). During the scanning process, an image is acquired and the corresponding MTF value is calculated at each step position. When the MTF value at a certain position drops to a preset threshold, the control processing unit records the Z-axis position at this time, which is used as the near boundary Z_near and the far boundary Z_far of the depth of field, respectively.

[0065] Finally, the depth-of-field parameters of the lens under test are calculated using the formula “depth of field = |Z_far - Z_near|”.

[0066] Understandably, preset thresholds can be flexibly set according to industry standards or application requirements.

[0067] This detection method determines the depth-of-field boundary by using a quantitative MTF value threshold, avoiding the subjectivity of human visual judgment. It achieves automated and quantitative detection of depth-of-field parameters, with accurate and reliable results. Furthermore, the detection process is seamlessly integrated with the autofocus process, significantly improving detection efficiency.

[0068] In some alternative implementations, the detection parameters include resolution, the control processing unit calls an image analysis algorithm to identify oblique regions in the image, and calculates the modulation transfer function (MTF) value based on the spatial frequency response (SFR) method.

[0069] In practice, for the core detection parameter of resolution, the control processing unit achieves quantitative detection by calling an image analysis algorithm based on the spatial frequency response (SFR) method.

[0070] The specific testing process is as follows:

[0071] First, switch to the resolution test scheme, move the ISO12233 transmission test card corresponding to the field of view into the imaging optical path, and acquire the image at the already located optimal focal plane position; the control processing unit sets the required spatial frequency range and ROI (region of interest) size according to preset parameters;

[0072] The algorithm then automatically identifies the slanted area of ​​the transmission test card in the image and generates ROI detection areas at the slanted positions of multiple representative areas in the center and around the image to comprehensively evaluate the resolution uniformity of the lens. Next, the software performs fine analysis on each ROI area according to the preset SFR algorithm. By sampling and Fourier transforming the gray-scale transition area of ​​the slanted image, the modulation transfer function (MTF) values ​​at different spatial frequencies are obtained.

[0073] Finally, the MTF values ​​of the image center and surrounding areas are output as a quantitative evaluation index of lens resolution. This detection method conforms to international standards, and the test results are objective and accurate, comprehensively reflecting the lens's ability to transmit image details at different spatial frequencies, providing a scientific basis for determining lens resolution levels.

[0074] In some alternative implementations, the detection parameters include distortion, and the control processing unit obtains TV distortion parameters by identifying feature points in the calibration image and calculating the geometric deviation of the feature points relative to the distortion-free model.

[0075] The detection process for TV distortion parameters is based on a grid distortion transmission test chart and a feature point analysis algorithm, as detailed below:

[0076] First, the control processing unit switches to the distortion detection mode, places the grid distortion transmission test card in the imaging optical path, and acquires the grid image at the optimal focal plane position.

[0077] Subsequently, the corner points in the middle and two sides of the grid in the image are automatically identified by the corner detection algorithm. These corner points are used as feature points that reflect the geometry of the grid, and their coordinate positions are accurately recorded.

[0078] Next, using the image center point as a reference, and based on the actual physical spacing of the grid distortion transmission test card and the lens imaging magnification, an ideal distortion-free grid model is constructed to clarify the theoretical coordinates of each feature point under distortion-free conditions.

[0079] Finally, by calculating the geometric deviation between the coordinates of feature points in the actual acquired image and the corresponding coordinates of feature points in the ideal mesh model, the TV distortion parameters are calculated according to the formula: TV distortion = (Y sides_max - Y sides_min) / actual spacing × 100%, where Y sides_max is the maximum actual Y coordinate of the feature points on both sides, and Y sides_min is the minimum actual Y coordinate of the feature points on both sides.

[0080] This detection method enables automated quantitative calculation of distortion parameters, avoiding the tediousness and errors of manual measurement, and can accurately reflect the degree of geometric distortion in lens imaging.

[0081] In some alternative implementations, the detection parameters include uniformity, and the control processing unit assesses illumination attenuation by calculating the ratio of gray values ​​between the central and edge regions of the image.

[0082] The core objective of uniformity detection is to measure the attenuation of light intensity at the lens edge relative to the center (i.e., vignetting). The detection process is achieved through the coordinated use of a uniform surface light source, a high-resolution image sensor, and a grayscale analysis algorithm.

[0083] The specific process is as follows:

[0084] First, the control processing unit activates the uniform surface light source to ensure a stable and uniform lighting environment. Then, the image sensor acquires a full-frame image at the optimal focal plane position.

[0085] Next, the control processing unit defines ROI regions on the acquired image, including one central ROI region and four edge ROI regions (e.g., located at the four corners of the image respectively). The size of each ROI region is consistent to ensure the fairness of the measurement.

[0086] Next, the average gray value of all pixels in each ROI area is calculated using an algorithm. Since the pixel gray value is linearly related to the incident light intensity, the average gray value can indirectly reflect the light intensity of the area. Finally, the uniformity result is calculated according to the following formula: Uniformity = (lowest gray value around the perimeter / gray value in the center) × 100%. The closer the uniformity value is to 100%, the better the illumination uniformity of the lens.

[0087] This detection method can comprehensively reflect the illumination distribution of the lens image. The detection process is highly automated, and the results are quantitative and intuitive, providing a reliable basis for evaluating the uniformity quality of the lens.

[0088] Please refer to Figure 2 Based on the foregoing embodiments, this invention provides an automated multi-parameter lens inspection method, implemented using any of the lens inspection platforms described above, comprising the following steps:

[0089] S1. Control the Z-axis motion component 20 to drive the lens under test to scan within a preset range, determine the focal plane position through image analysis algorithm, and drive the lens under test to reset to the focal plane position;

[0090] S2. Based on the focal plane position or the offset of the focal plane position, acquire images with the corresponding test calibration components and extract various parameters including resolution, depth of field, distortion and uniformity.

[0091] S3. Summarize the test data of various parameters and generate an integrated test report including test images based on the preset pass / fail criteria.

[0092] In practical applications, the specific detection process is as follows:

[0093] 1) Lens clamping and system initialization: The operator installs the lens to be tested on the bracket of the Z-axis motion component 20, starts the detection system, and the platform automatically resets to complete initialization operations such as light source preheating and sensor calibration;

[0094] 2) Autofocus and focal plane positioning: The control processing unit controls the Z-axis motion component 20 to drive the lens under test to scan within a preset range. The image sensor continuously acquires images, and the optimal focal plane position is determined by the image sharpness analysis algorithm. Then, the lens under test is driven to reset to the focal plane position.

[0095] 3) Multi-parameter continuous detection: According to the preset detection items, the system automatically switches the corresponding test calibration components, acquires images at the focal plane position or at a preset offset based on the focal plane position, and sequentially completes the extraction of multiple parameters such as resolution, depth of field, distortion and uniformity. The detection of each parameter is achieved by calling the corresponding image analysis algorithm, and the process is seamlessly connected.

[0096] 4) Integrated Report Generation: After testing, the control and processing unit summarizes the test data of various parameters, corresponding test images, and pass / fail judgment results. It automatically generates an integrated test report according to a preset report template. The report clearly includes detailed values ​​of each parameter, the pass / fail judgment conclusion, and the corresponding test images. It supports automatic saving, printing, or network uploading, achieving traceability and visualization of test results. This testing method automates the entire process from clamping to report generation, significantly improving testing efficiency and ensuring the integrity and accuracy of test data.

[0097] Based on the above embodiments, the present invention also provides a machine vision inspection device, including a memory and a processor, wherein the processor executes a computer program stored in the memory to implement the above-described automated multi-parameter lens inspection method.

[0098] Finally, it should be noted that although the above embodiments have been described in the text and drawings of this application, this should not be construed as limiting the scope of protection of this application. Any technical solutions resulting from equivalent structural or procedural substitutions or modifications made based on the essential concept of this application and utilizing the content described in the text and drawings of this application, as well as the direct or indirect application of the technical solutions of the above embodiments to other related technical fields, are all included within the scope of protection of this application.

Claims

1. A lens inspection platform, characterized in that, include: The reference platform (10) has a preset stiffness and is used to provide a physical reference. Z-axis motion assembly (20) is installed on the reference platform (10) to carry the lens under test and drive the lens under test to move along the Z-axis direction. The Z-axis motion assembly (20) includes a linear drive unit and a grating ruler for feedback position data. An optical imaging module (30) is disposed opposite to the lens under test and includes a standard light source, an image sensor, and a test calibration component; The control processing unit is electrically connected to the Z-axis motion assembly (20) and the image sensor, respectively; The control processing unit is used to control the Z-axis motion component (20) to drive the lens under test to perform the detection process based on the position data fed back by the grating ruler, and to call the image analysis algorithm to process the acquired image in order to obtain the detection parameters of the lens under test.

2. The lens inspection platform according to claim 1, characterized in that, The reference platform (10) is a marble platform base.

3. The lens inspection platform according to claim 1, characterized in that, The test calibration components include one or more of the following: a transmission test card and a grid distortion transmission test card.

4. The lens inspection platform according to claim 1, characterized in that, When the control processing unit executes the detection process, it first executes the focusing logic and determines the focal plane position of the lens under test through image sharpness analysis.

5. The lens inspection platform according to claim 4, characterized in that, The detection parameters include depth of field. The control processing unit controls the Z-axis motion component (20) to perform boundary scanning on both sides of the focal plane position according to the focal plane position and its corresponding peak sharpness value, and determines the depth of field parameters according to the displacement range when the sharpness value drops to a preset threshold.

6. The lens inspection platform according to claim 1, characterized in that, The detection parameters include resolution. The control processing unit calls the image analysis algorithm to identify the oblique regions in the image and calculates the modulation transfer function (MTF) value based on the spatial frequency response (SFR) method.

7. The lens inspection platform according to claim 1, characterized in that, The detection parameters include distortion. The control processing unit obtains TV distortion parameters by identifying feature points in the calibration image and calculating the geometric deviation of the feature points relative to the distortion-free model.

8. The lens inspection platform according to claim 1, characterized in that, The detection parameters include uniformity, and the control processing unit evaluates the illumination attenuation by calculating the ratio of gray values ​​between the central region and the edge region of the image.

9. A multi-parameter automated detection method for lenses, characterized in that, Based on the lens inspection platform as described in any one of claims 1-8, the implementation includes the following steps: The Z-axis motion component (20) is controlled to drive the lens under test to scan within a preset range, and the focal plane position is determined by the image analysis algorithm and the lens under test is driven to reset to the focal plane position. Based on the focal plane position or the offset of the focal plane position, images are acquired in conjunction with the corresponding test calibration components, and multiple parameters including resolution, depth of field, distortion, and uniformity are extracted. The test data of each parameter are summarized and combined with the preset pass / fail criteria to generate an integrated test report containing test images.

10. A machine vision inspection device, comprising a memory and a processor, characterized in that, The processor executes the computer program stored in the memory to implement the lens multi-parameter automated detection method as described in claim 9.

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