Defect detection method and device based on binocular structured light technology, medium and product

Abstract

The invention discloses a defect detection method and device based on a binocular structured light technology, a medium and a product, and relates to the technical field of defect detection, and the method comprises the steps: obtaining a plurality of left camera images and a plurality of right camera images of the surface of a to-be-detected component; determining an absolute phase of each pixel point on the left camera image plane and the right camera image plane by using all the left camera images, the right camera images and preset codes; matching based on the absolute phase of each pixel point to obtain multiple groups of conjugate points; one group of conjugate points comprises a pixel point on the image plane of the left camera and a pixel point on the image plane of the right camera; based on the coordinates of the two pixel points in each group of conjugate points, determining the three-dimensional coordinates of the spatial points corresponding to each group of conjugate points so as to form an actually measured three-dimensional point cloud of the surface of the to-be-measured component; and based on the actually measured three-dimensional point cloud and the standard three-dimensional point cloud of the surface of the to-be-measured component, determining the position and size information of the surface defect of the to-be-measured component. The efficiency and precision of component surface defect detection are improved.

Description

Technical Field

[0001] The present application relates to the field of defect detection technology, and in particular to a defect detection method, device, medium and product based on binocular structured light technology. Background Art

[0002] In the fields of aviation, aerospace, nuclear power, etc., some load-bearing components such as engine blades, turbine disks, gas turbine rotors, etc. are more likely to have various defects on the surface of components, such as cracks, pits, notches, ablation, etc., due to the influence of multi-axial stress loads, high-temperature airflow impact and external damage during service. These defects seriously reduce the service life and safety performance of components and even equipment, and are more likely to directly lead to economic losses and safety accidents, threatening the safety of public life and property. Therefore, it is necessary to conduct timely and accurate detection and feedback of physical information such as the size and position of surface defects of components, so as to integrate the physical information of defects with the digital model of components, simulate the damage evolution process of components, establish the correlation model between physical information of defects and the remaining life of components, and improve the component damage evaluation system, so as to provide strong support for fault diagnosis and life assessment of components and avoid the occurrence of major catastrophic accidents.

[0003] At present, there are many methods for detecting surface defects. The manual visual inspection method assisted by magnetic powder / fluorescent powder can spray powder to image the surface cracks. The inspectors determine and measure the approximate location and size of the cracks based on their experience. This method requires high prior experience and inspection level of the inspectors. After the inspection, the powder at the cracks needs to be removed. The whole process is time-consuming, labor-intensive, and cumbersome, and it is impossible to form an efficient and intelligent crack detection method. Traditional sensor detection methods such as eddy current method and ultrasonic method can make a preliminary judgment on whether there are cracks, pits and notches in a certain area on the component. However, they are still lacking in the detection of physical information such as the location and size of the defects. In addition, due to the small inspection area, the inspection path points need to be planned, and the inspection efficiency is low. The machine vision method combined with deep learning can learn a large amount of surface defect image data and quickly and automatically calculate the size information of various defects such as cracks, pits, notches and ablation in the component surface image, avoiding the instability of manual measurement. However, this method cannot obtain the position information of defects on the component surface, and thus cannot accurately locate the detected defects in the component physical model, and thus cannot further study the damage evolution process of the component. Summary of the invention

[0004] The purpose of this application is to provide a defect detection method, device, medium and product based on binocular structured light technology, which improves the efficiency and accuracy of component surface defect detection.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In a first aspect, the present application provides a defect detection method based on binocular structured light technology, wherein the defect detection method based on binocular structured light technology is implemented using a defect detection system based on binocular structured light technology, wherein the defect detection system based on binocular structured light technology includes a left camera, a right camera and a structured light projector, and the defect detection method based on binocular structured light technology includes:

[0007] Acquire multiple left camera images and multiple right camera images of the surface of the component to be measured; the left camera images are structured light images collected by the left camera, and the right camera images are structured light images collected by the right camera; the size and center position of each left camera image are the same, and the size and center position of each right camera image are the same; the structured light image is an image generated by projecting a structured light pattern generated by a structured light projector based on a preset code onto the surface of the component to be measured;

[0008] The absolute phase of each pixel on the left camera image plane is determined by utilizing the structural characteristics of all left camera images and structured light patterns;

[0009] Determine the absolute phase of each pixel on the right camera image plane by using the structural characteristics of all right camera images and structured light patterns;

[0010] Based on the absolute phase of each pixel point on the left camera image plane and the absolute phase of each pixel point on the right camera image plane, a plurality of groups of conjugate points are matched; a group of the conjugate points includes: a pixel point on the left camera image plane and a pixel point on the right camera image plane;

[0011] Based on the coordinates of two pixel points in each group of conjugate points, the three-dimensional coordinates of the space points corresponding to each group of conjugate points are determined, thereby forming a measured three-dimensional point cloud of the surface of the component to be measured;

[0012] Obtaining a standard three-dimensional point cloud; the standard three-dimensional point cloud is an original design three-dimensional point cloud of a component without defects;

[0013] Based on the measured three-dimensional point cloud of the surface of the component to be measured and the standard three-dimensional point cloud, the position and size information of the defects on the surface of the component to be measured are determined.

[0014] Optionally, before acquiring a plurality of left camera images and a plurality of right camera images of the surface of the component to be measured, the method further includes:

[0015] Adjust the postures of the left camera, the right camera, and the structured light projector in the defect detection system based on binocular structured light technology so that the angle between the optical axis of the left camera and the optical axis of the projector is 30°, and the angle between the optical axis of the right camera and the optical axis of the projector is 30°;

[0016] Adjust the position and posture of the calibration object;

[0017] The left camera and the right camera are used to collect images of the calibration object in different positions and postures, and the coordinates of the corner points of the calibration object are extracted;

[0018] The camera intrinsic parameters of the left camera, the camera intrinsic parameters of the right camera, and the homogeneous rotation and translation matrix between the left camera and the right camera are calibrated using the coordinates of the corner points of the calibration object. The calibration is continuously iterated and optimized with the goal of minimizing the reprojection error to achieve calibration of the left camera and the right camera.

[0019] Optionally, the absolute phase of each pixel on the left camera image plane is determined by utilizing the structural characteristics of all left camera images and structured light patterns, including:

[0020] Calculate the wrapped phase of the corresponding pixel coordinates based on the light intensity of each pixel point on the left camera image plane in all left camera images and the preset coded phase shift step number;

[0021] Based on the wrapping phase and period of each pixel on the left camera image plane, the absolute phase of the corresponding pixel is determined.

[0022] Optionally, the absolute phase of each pixel on the right camera image plane is determined by utilizing the structural characteristics of all right camera images and structured light patterns, including:

[0023] Calculate the wrapped phase of the corresponding pixel coordinates based on the light intensity of each pixel point on the right camera image plane in all right camera images and the preset coded phase shift step number;

[0024] Based on the wrapping phase and period of each pixel on the right camera image plane, the absolute phase of the corresponding pixel is determined.

[0025] Optionally, multiple groups of conjugate points are obtained based on the absolute phase of each pixel point on the left camera image plane and the absolute phase of each pixel point on the right camera image plane, including:

[0026] The image points corresponding to each pixel point on the left camera image plane and the image points corresponding to each pixel point on the right camera image plane with the same absolute phase are determined as a group of conjugate points, thereby obtaining multiple groups of conjugate points.

[0027] Optionally, based on the coordinates of two pixel points in each group of conjugate points, the three-dimensional coordinates of the space points corresponding to each group of conjugate points are determined to form a measured three-dimensional point cloud of the surface of the component to be measured, including:

[0028] Determine any set of conjugate points as current conjugate points;

[0029] According to the coordinates of the two pixels in the current conjugate point, calculate the disparity corresponding to the current conjugate point;

[0030] Calculate the three-dimensional coordinates of the space point corresponding to the current conjugate point based on the coordinates of the two pixel points in the current conjugate point and the disparity corresponding to the current conjugate point;

[0031] Based on the three-dimensional coordinates of each spatial point, the measured three-dimensional point cloud of the surface of the component to be measured is determined.

[0032] Optionally, determining the position and size information of the defect on the surface of the component to be measured based on the measured three-dimensional point cloud of the surface of the component to be measured and the standard three-dimensional point cloud includes:

[0033] Matching and comparing the measured three-dimensional point cloud of the surface of the component to be measured with the standard three-dimensional point cloud;

[0034] The position and size information of the defect on the surface of the component to be measured are determined according to each point in the measured three-dimensional point cloud of the surface of the component to be measured that cannot be matched.

[0035] In a second aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the above-described defect detection methods based on binocular structured light technology.

[0036] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-mentioned defect detection methods based on binocular structured light technology.

[0037] In a fourth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements any of the above-mentioned defect detection methods based on binocular structured light technology.

[0038] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0039] This application discloses a defect detection method, device, medium and product based on binocular structured light technology, which uses non-contact active projection to project structured light patterns onto the surface of the component to be tested to generate structured light images, and uses binocular vision technology to reconstruct a high-precision three-dimensional point cloud of the component surface contour. While automatically obtaining the size information of the component surface defect, it can also obtain the position of the defect on the component, and there is no need for powder cleaning and sensor planning and layout, which improves the efficiency and accuracy of component surface defect detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0041] Figure 1 A schematic diagram of a defect detection method based on binocular structured light technology provided in one embodiment of the present application;

[0042] Figure 2 This is a flowchart of the component surface fatigue crack detection method;

[0043] Figure 3 This is the structural diagram of the defect detection system based on binocular structured light technology;

[0044] Figure 4 is a schematic diagram of a structured light pattern;

[0045] Figure 5 Schematic diagram of defect detection principle based on binocular structured light technology;

[0046] Figure 6 A schematic diagram of the structure of a computer device provided in one embodiment of the present application.

[0047] Explanation of symbols:

[0048] 1-stabilizing frame, 2-base frame, 3-right lens, 4-spacer locking nut, 5-spacer spring washer, 6-spacer locking bolt, 7-right camera spacer, 8-right camera, 9-stabilizing frame fixing bolt, 10-stabilizing frame spring washer, 11-stabilizing frame fixing nut, 12-left camera spacer, 13-left camera, 14-left lens, 15-structured light projector, 16-measured component, 17-component locking bolt, 18-component locking fixture, 19-positioner. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0050] The purpose of this application is to provide a defect detection method, device, medium and product based on binocular structured light technology, aiming to improve the efficiency and accuracy of component surface defect detection.

[0051] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0052] In an exemplary embodiment, Figure 1 , Figure 2 and Figure 5 As shown, the defect detection method based on binocular structured light technology in this embodiment is implemented by using a defect detection system based on binocular structured light technology. The defect detection system based on binocular structured light technology includes a left camera 13, a right camera 8 and a structured light projector 15. The defect detection method based on binocular structured light technology includes:

[0053] Step 1: Acquire multiple left camera images and multiple right camera images of the surface of the component to be tested.

[0054] Among them, the left camera image is a structured light image captured by the left camera, and the right camera image is a structured light image captured by the right camera; the size and center position of each left camera image are the same, and the size and center position of each right camera image are the same; the structured light image is an image generated by projecting a structured light pattern generated by a structured light projector based on a preset code onto the surface of the component to be tested.

[0055] As an optional implementation manner, before step 1, the method further includes:

[0056] Adjust the positions of the left camera, right camera and structured light projector in the defect detection system based on binocular structured light technology so that the angle between the optical axis of the left camera and the optical axis of the projector is 30°, and the angle between the optical axis of the right camera and the optical axis of the projector is 30°.

[0057] Adjust the pose of the calibration object.

[0058] The left camera and the right camera are used to collect images of the calibration object in different postures, and the coordinates of the corner points of the calibration object are extracted.

[0059] The camera intrinsic parameters of the left camera, the camera intrinsic parameters of the right camera, and the homogeneous rotation and translation matrix between the left and right cameras are calibrated using the coordinates of the corner points of the calibration object. The calibration is optimized and iterated continuously to achieve the calibration of the left and right cameras with the goal of minimizing the reprojection error.

[0060] Specifically, Figure 3As shown, in addition to the left camera 13, the right camera 8 and the structured light projector 15, the defect detection system based on the binocular structured light technology also includes: a stabilizing frame 1, a base frame 2, a right lens 3, a pad locking nut 4, a pad spring washer 5, a pad locking bolt 6, a right camera pad 7, a stabilizing frame fixing bolt 9, a stabilizing frame spring washer 10, a stabilizing frame fixing nut 11, a left camera pad 12, a left lens 14, a measured component 16, a component locking bolt 17, a component locking clamp 18 and a positioner 19.

[0061] The structured light patterns generated by the preset coding include but are not limited to: (1) structured light patterns generated by single-direction coding using Gray code combined with phase shift method; (2) sinusoidal digital grating; (3) multi-frequency digital grating; (4) Gray code; (5) complementary Gray code. The number and type of structured light patterns will affect the efficiency and accuracy of subsequent phase solution. It is necessary to select the appropriate structured light pattern for projection according to the actual defect measurement requirements.

[0062] When the structured light pattern is Figure 4 When the structured light pattern is generated by unidirectional encoding using the Gray code method combined with the phase shift method, the principles include:

[0063] Gray code patterns are encoded using two grayscales, with low grayscale values ​​corresponding to code values ​​of 0 and high grayscale values ​​corresponding to code values ​​of 1. At the same time, in order to improve the anti-interference ability of Gray code encoding, two additional all-black and all-white patterns are generally added on the basis of projecting the Gray code pattern, so as to normalize the acquired Gray code image. In this example, a 4-bit Gray code encoding pattern is generated.

[0064] Gray code uses image grayscale to determine codewords. This method is sensitive to grayscale interference. In the actual imaging process, grayscale interference is inevitably affected by various factors, which leads to decoding errors and reduces the accuracy of surface contour reconstruction at grayscale interference. Phase anti-interference ability is better than grayscale, so this example combines Gray code with phase shift method, and projects phase shift method pattern on the basis of Gray code pattern to reduce decoding errors caused by image grayscale interference.

[0065] Assuming the number of phase shift steps is N, the light intensity formula in the generated structured light pattern is:

[0066]

[0067] Among them, I k is the light intensity of the kth phase-shift-coded structured light pattern; A is the background light intensity; B is the modulated light intensity; T is the number of pixels in a single period, also called the stripe period; x is the pixel point in the encoding direction.

[0068] This example uses 12-step phase shift, that is, N=12 to generate the pattern. Figure 4As shown, 12 phase-shift coded structured light patterns are generated.

[0069] Step 2: Using the structural characteristics of all left camera images and structured light patterns, determine the absolute phase of each pixel on the left camera image plane.

[0070] As an optional implementation, step 2 includes:

[0071] Step 21: Based on the light intensity of each pixel point on the left camera image plane in all left camera images and the preset coded phase shift step number, calculate the wrapping phase of the corresponding pixel coordinate.

[0072] Specifically, the calculation formula for the wrapped phase of the pixel point on the left camera image plane is:

[0073]

[0074] in, is the wrapped phase of pixel x in the left camera image; is the light intensity of pixel x in the kth left camera image.

[0075] Step 22: Based on the wrapping phase and period of each pixel point on the left camera image plane, determine the absolute phase of the corresponding pixel point.

[0076] Specifically, the calculation formula for the absolute phase of the pixel point on the left camera image plane is:

[0077]

[0078] Among them, Φ1(x) is the absolute phase of pixel x in the left camera image; k(x) is the phase shift number corresponding to pixel x.

[0079] k(x) can be obtained by decoding according to the corresponding relationship in Table 1:

[0080]

[0081] k(x)=F(V(x)).

[0082] Where V(x) is the Gray code value; GC i (x) is the i-th bit in the Gray code, i=1, 2, 3, 4; F(·) is the mapping relationship.

[0083] Table 1 Gray code decoding table

[0084] <![CDATA[GC1(x)]]> 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 <![CDATA[GC2(x)]]> 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 <![CDATA[GC3(x)]]> 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 <![CDATA[GC4(x)]]> 0 1 1 0 0 1 1 0 0 1 1 0 0 1 1 0 V(x) 0 1 3 2 6 7 5 4 12 13 15 14 10 11 9 8 k(x) 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

[0085] Step 3: Using the structural characteristics of all right camera images and structured light patterns, determine the absolute phase of each pixel on the right camera image plane.

[0086] As an optional implementation, step 3 includes:

[0087] Step 31: Calculate the wrapping phase of the corresponding pixel coordinates based on the light intensity of each pixel point on the right camera image plane in all right camera images and the preset coded phase shift step number.

[0088] Specifically, the calculation formula for the wrapped phase of the pixel point on the right camera image plane is:

[0089]

[0090] in, is the wrapped phase of pixel x in the right camera image; is the light intensity of pixel x in the kth right camera image.

[0091] Step 32: Based on the wrapping phase and period of each pixel point on the right camera image plane, determine the absolute phase of the corresponding pixel point.

[0092] Specifically, the calculation formula for the absolute phase of the pixel point on the right camera image plane is:

[0093]

[0094] Where Φ2(x) is the absolute phase of pixel x in the right camera image.

[0095] Step 4: Matching is performed based on the absolute phase of each pixel point on the left camera image plane and the absolute phase of each pixel point on the right camera image plane to obtain multiple groups of conjugate points.

[0096] The group of conjugate points includes: a pixel point on the left camera image plane and a pixel point on the right camera image plane.

[0097] As an optional implementation, step 4 includes:

[0098] The image points corresponding to each pixel point on the left camera image plane and the image points corresponding to each pixel point on the right camera image plane with the same absolute phase are determined as a group of conjugate points, thereby obtaining multiple groups of conjugate points.

[0099] Specifically, according to the conditions that the same spatial point is on the left camera image plane and the right camera image plane, the absolute phase of the image point is equal and the epipolar constraint, stereo vision matching is performed with the absolute phase as the matching primitive to find the conjugate point formed by the spatial point on the left camera image plane and the right camera image plane.

[0100] Step 5: Based on the coordinates of two pixel points in each group of conjugate points, the three-dimensional coordinates of the space points corresponding to each group of conjugate points are determined, thereby forming a measured three-dimensional point cloud of the surface of the component to be measured.

[0101] As an optional implementation, step 5 includes:

[0102] Step 51: Determine any group of conjugate points as current conjugate points.

[0103] Step 52: Calculate the disparity corresponding to the current conjugate point according to the coordinates of two pixel points in the current conjugate point.

[0104] Specifically, the calculation formula of parallax is:

[0105] d = x l -x r .

[0106] Where d is the parallax; x l is the x-axis component of the pixel point on the left camera image plane in the conjugate point; r is the x-axis component of the pixel point on the right camera image plane in the conjugate point.

[0107] Step 53: Based on the coordinates of the two pixel points in the current conjugate point and the disparity corresponding to the current conjugate point, calculate the three-dimensional coordinates of the space point corresponding to the current conjugate point.

[0108] Specifically, the calculation formula for the three-dimensional coordinates of a space point is:

[0109]

[0110] Among them, Z is the z-axis component of the three-dimensional coordinates of the spatial point; f is the focal length; b is the camera baseline; X is the x-axis component of the three-dimensional coordinates of the spatial point; and Y is the y-axis component of the three-dimensional coordinates of the spatial point.

[0111] Step 54: Based on the three-dimensional coordinates of each spatial point, determine the measured three-dimensional point cloud of the surface of the component to be measured.

[0112] In fact, the size of the images captured by the left camera and the right camera is generally much smaller than the size of the component to be measured. Therefore, it is necessary to execute steps 1 to 5 for each block of the surface of the component to be measured to obtain three-dimensional point clouds of multiple blocks. Then, the three-dimensional point clouds of each block are subjected to Gaussian filtering to filter out invalid point clouds, and with the help of information such as the point cloud normal vector, redundant data is removed, and the point clouds with overlapping areas are registered. The three-dimensional point clouds of each block are spliced ​​to obtain the measured three-dimensional point cloud of the surface of the component to be measured.

[0113] Step 6: Obtain a standard 3D point cloud; the standard 3D point cloud is the original design 3D point cloud of a component without defects.

[0114] Step 7: Based on the measured three-dimensional point cloud and the standard three-dimensional point cloud of the surface of the component to be measured, determine the position and size information of the defects on the surface of the component to be measured.

[0115] As an optional implementation, step 7 includes:

[0116] Step 71: Match and compare the measured three-dimensional point cloud of the surface of the component to be measured with the standard three-dimensional point cloud.

[0117] Step 72: Determine the position and size information of the defect on the surface of the component to be measured according to each point in the measured three-dimensional point cloud of the surface of the component to be measured that cannot be matched.

[0118] Generally, defects are cracks, pits, notches and ablations.

[0119] Beneficial effects of this application:

[0120] (1) Low technical requirements for inspectors. Compared with magnetic powder / fluorescent powder assisted manual inspection methods, the method in this application combines vision and structured light technology to capture, identify and automatically calculate the position and length size information of defects, which can reduce the uncertainty of manual inspection and detect surface defects of components stably and quickly; (2) Fast defect detection speed and high accuracy. Compared with eddy current method, ultrasonic method and other methods, this application projects large-area structured light images, calculates high-density three-dimensional point clouds, quickly reconstructs the surface contour of components, and accurately obtains the size information of surface defects of components, thereby realizing rapid detection of surface defects of components; (3) Ability to locate defect positions. Compared with machine vision methods combined with deep learning, this application can obtain the position information of surface defects of components, which is conducive to accurately locating the position of surface defects in the physical model of components, so as to simulate the damage evolution process of components, and further evaluate the remaining service life of damaged components.

[0121] In an exemplary embodiment, a computer device is provided, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a defect detection method based on binocular structured light technology.

[0122] In an exemplary embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, a defect detection method based on binocular structured light technology is implemented.

[0123] In an exemplary embodiment, a computer program product is provided, including a computer program, which implements a defect detection method based on binocular structured light technology when executed by a processor.

[0124] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 6As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a defect detection method based on binocular structured light technology is implemented.

[0125] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0126] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0127] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0128] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0129] The technical features of the above embodiments may be combined arbitrarily. 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.

[0130] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A defect detection method based on binocular structured light technology, wherein the defect detection method based on binocular structured light technology is implemented by a defect detection system based on binocular structured light technology, wherein the defect detection system based on binocular structured light technology comprises a left camera, a right camera and a structured light projector, It is characterized in that The defect detection method based on binocular structured light technology includes: Acquire multiple left camera images and multiple right camera images of the surface of the component to be measured; the left camera images are structured light images collected by the left camera, and the right camera images are structured light images collected by the right camera; the size and center position of each left camera image are the same, and the size and center position of each right camera image are the same; the structured light image is an image generated by projecting a structured light pattern generated by a structured light projector based on a preset code onto the surface of the component to be measured; The absolute phase of each pixel on the left camera image plane is determined by utilizing the structural characteristics of all left camera images and structured light patterns; Determine the absolute phase of each pixel on the right camera image plane by using the structural characteristics of all right camera images and structured light patterns; Based on the absolute phase of each pixel point on the left camera image plane and the absolute phase of each pixel point on the right camera image plane, a plurality of groups of conjugate points are matched; a group of the conjugate points includes: a pixel point on the left camera image plane and a pixel point on the right camera image plane; Based on the coordinates of two pixel points in each group of conjugate points, the three-dimensional coordinates of the space points corresponding to each group of conjugate points are determined, thereby forming a measured three-dimensional point cloud of the surface of the component to be measured; Obtaining a standard three-dimensional point cloud; the standard three-dimensional point cloud is an original design three-dimensional point cloud of a component without defects; Based on the measured three-dimensional point cloud of the surface of the component to be measured and the standard three-dimensional point cloud, the position and size information of the defects on the surface of the component to be measured are determined.

2. The defect detection method based on binocular structured light technology according to claim 1, It is characterized in that Before acquiring a plurality of left camera images and a plurality of right camera images of the surface of the component to be measured, the method further includes: Adjust the postures of the left camera, the right camera, and the structured light projector in the defect detection system based on binocular structured light technology so that the angle between the optical axis of the left camera and the optical axis of the projector is 30°, and the angle between the optical axis of the right camera and the optical axis of the projector is 30°; Adjust the position and posture of the calibration object; The left camera and the right camera are used to collect images of the calibration object in different positions and postures, and the coordinates of the corner points of the calibration object are extracted; The camera intrinsic parameters of the left camera, the camera intrinsic parameters of the right camera, and the homogeneous rotation and translation matrix between the left camera and the right camera are calibrated using the coordinates of the corner points of the calibration object. The calibration is continuously iterated and optimized with the goal of minimizing the reprojection error to achieve calibration of the left camera and the right camera.

3. The defect detection method based on binocular structured light technology according to claim 1, It is characterized in that The absolute phase of each pixel on the left camera image plane is determined by utilizing the structural characteristics of all left camera images and structured light patterns, including: Calculate the wrapped phase of the corresponding pixel coordinates based on the light intensity of each pixel point on the left camera image plane in all left camera images and the preset coded phase shift step number; Based on the wrapping phase and period of each pixel on the left camera image plane, the absolute phase of the corresponding pixel is determined.

4. The defect detection method based on binocular structured light technology according to claim 1, It is characterized in that The absolute phase of each pixel on the right camera image plane is determined by utilizing the structural characteristics of all right camera images and structured light patterns, including: Calculate the wrapped phase of the corresponding pixel coordinates based on the light intensity of each pixel point on the right camera image plane in all right camera images and the preset coded phase shift step number; Based on the wrapping phase and period of each pixel on the right camera image plane, the absolute phase of the corresponding pixel is determined.

5. The defect detection method based on binocular structured light technology according to claim 1, It is characterized in that Based on the absolute phase of each pixel point on the left camera image plane and the absolute phase of each pixel point on the right camera image plane, multiple groups of conjugate points are obtained, including: The image points corresponding to each pixel point on the left camera image plane and the image points corresponding to each pixel point on the right camera image plane with the same absolute phase are determined as a group of conjugate points, thereby obtaining multiple groups of conjugate points.

6. The defect detection method based on binocular structured light technology according to claim 1, It is characterized in that Based on the coordinates of two pixel points in each group of conjugate points, the three-dimensional coordinates of the space points corresponding to each group of conjugate points are determined, thereby forming a measured three-dimensional point cloud of the surface of the component to be measured, including: Determine any set of conjugate points as current conjugate points; According to the coordinates of the two pixels in the current conjugate point, calculate the disparity corresponding to the current conjugate point; Calculate the three-dimensional coordinates of the space point corresponding to the current conjugate point based on the coordinates of the two pixel points in the current conjugate point and the disparity corresponding to the current conjugate point; Based on the three-dimensional coordinates of each spatial point, the measured three-dimensional point cloud of the surface of the component to be measured is determined.

7. The defect detection method based on binocular structured light technology according to claim 1, It is characterized in that Determining the position and size information of the defect on the surface of the component to be measured based on the measured three-dimensional point cloud of the surface of the component to be measured and the standard three-dimensional point cloud, including: Matching and comparing the measured three-dimensional point cloud of the surface of the component to be measured with the standard three-dimensional point cloud; The position and size information of the defect on the surface of the component to be measured are determined according to each point in the measured three-dimensional point cloud of the surface of the component to be measured that cannot be matched.

8. A computer device, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the defect detection method based on binocular structured light technology as described in any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, It is characterized in that When the computer program is executed by a processor, the defect detection method based on binocular structured light technology described in any one of claims 1 to 7 is implemented.

10. A computer program product comprising a computer program, It is characterized in that When the computer program is executed by a processor, the defect detection method based on binocular structured light technology described in any one of claims 1 to 7 is implemented.

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