Laser displacement testing method

Abstract

The invention discloses a laser displacement testing method, which belongs to the technical field of laser displacement testing, and comprises the following steps: on the basis of sample image data and corresponding displacement data, optimizing a pre-constructed deep learning image recognition model to obtain a displacement testing model, and then obtaining real-time testing data; and the real-time test data is identified by using the displacement test model to obtain the laser displacement test result, and the laser displacement test is performed in combination with deep learning, so that the displacement test precision and stability can be effectively improved.

Description

Technical Field

[0001] The invention belongs to the technical field of laser displacement testing, and in particular relates to a laser displacement testing method. Background Art

[0002] Existing distance measurement technologies include ultrasonic ranging, infrared ranging, radar ranging, etc., but these technologies have certain limitations in practical applications, such as low measurement accuracy and great environmental impact. Laser ranging technology has obvious advantages in the field of precise distance measurement due to its high directivity, high monochromaticity and high brightness. In the existing laser ranging field, laser displacement sensors are widely used due to their advantages such as non-contact, high precision and high speed. However, traditional laser displacement measurement methods are often limited by environmental factors and the performance of the sensor itself, resulting in the need to improve measurement accuracy and stability. Summary of the invention

[0003] The invention provides a laser displacement testing method, which is used to solve the technical problems of low precision and poor stability in the prior art.

[0004] A laser displacement testing method, comprising:

[0005] Determine first test data between the laser transmitter and the object to be measured and second test data after the object to be measured is continuously moved;

[0006] Wherein, the first test data and the second test data both include a test distance and a test image reflected by the surface of the object under test and captured by the area array image sensor;

[0007] Performing test data fusion according to the first test data and the second test data to determine sample image data and corresponding displacement data;

[0008] Based on the sample image data and the corresponding displacement data, the pre-built deep learning image recognition model is optimized to obtain a displacement test model;

[0009] Real-time test data is acquired, and after the real-time test data is identified using the displacement test model, a laser displacement test result is obtained.

[0010] Further, test data fusion is performed according to the first test data and the second test data to determine sample image data and corresponding displacement data, including:

[0011] Taking out the test image in the second test data, and determining the pixel positions corresponding to the light spots in the test image corresponding to any two adjacent positions;

[0012] Based on the pixel positions corresponding to the light spots in the test images corresponding to the two adjacent positions and the test distance, a new test image and the test distance corresponding to the new test image are generated by linear interpolation to generate new test data at all distances within the measurement limit;

[0013] For any second test data or any new test data, the test image is spliced ​​with the test image in the first test data to obtain a spliced ​​image, and the spliced ​​image is used as sample image data, and the test distance in the second test data or the new test data is used as the corresponding displacement data.

[0014] Further, taking out the test image in the second test data, and determining the pixel positions corresponding to the light spots in the test image corresponding to any two adjacent positions, comprises:

[0015] Taking out a test image from the second test data;

[0016] For any test image in the second test data, determine the target pixel point where the laser spot reflected by the surface of the object to be tested is located;

[0017] The pixel positions of the target pixels are averaged to obtain the pixel position corresponding to the light spot in the test image.

[0018] Further, based on the pixel position corresponding to the light spot in the test image corresponding to the two adjacent positions and the test distance, a new test image and a test distance corresponding to the new test image are generated by linear interpolation to generate new test data at all distances within the measurement limit, including:

[0019] Determining the distance between the light spots in the test images corresponding to the two adjacent positions based on the pixel positions corresponding to the light spots in the test images corresponding to the two adjacent positions;

[0020] Determine the actual distance corresponding to the test images corresponding to the two adjacent positions based on the actual test distance corresponding to the test images corresponding to the two adjacent positions;

[0021] A distance ratio is obtained according to the actual distance and the spot distance; wherein the distance ratio is used to characterize the pixel distance corresponding to the unit distance corresponding to the actual distance;

[0022] According to the distance ratio, the change in pixel distance caused by moving the unit distance is determined;

[0023] Based on the test images corresponding to the two adjacent positions and the pixel distance variation, the light spot in the test image is moved by a fixed distance to obtain a new test image, and the actual distance is modified by a unit distance to obtain a test distance corresponding to the new test image;

[0024] The test distances corresponding to the new test images are collectively used as new test data.

[0025] Further, based on the test images corresponding to the two adjacent positions and the pixel distance variation, the light spot in the test image is moved by a fixed distance to obtain a new test image, and the actual distance is modified by a unit distance to obtain a test distance corresponding to the new test image, including:

[0026] For the test images corresponding to the two adjacent positions, the light spot corresponding to the test image corresponding to one position is taken as the starting point, the light spot corresponding to the test image corresponding to the other position is taken as the end point, and the light spot in the test image is moved by a fixed distance to obtain a new test image;

[0027] While moving the light spot, the actual distance corresponding to the starting point is modified by the unit distance to obtain the test distance corresponding to the new test image.

[0028] Furthermore, based on the sample image data and the corresponding displacement data, the pre-built deep learning image recognition model is optimized to obtain a displacement test model, including:

[0029] For a pre-built deep learning image recognition model, randomly initialize model parameters corresponding to the deep learning image recognition model, and form the model parameters into a vector to obtain a model parameter vector;

[0030] Based on the above-mentioned model parameter vector acquisition method, multiple different model parameter vectors are acquired to complete the generation of the initial solution;

[0031] In the current optimization process, the model parameter vector is subjected to local information exploration in random directions to obtain the model parameter vector after local information exploration;

[0032] For the model parameter vector after local information exploration, a probabilistic decision method is used to perform global information exploration on the model parameter vector in a random direction to obtain the model parameter vector after global information exploration;

[0033] For the model parameter vector after global information exploration, a multi-information interaction method is used to perform optimal information exploration on the model parameter vector to obtain the model parameter vector after optimal information exploration;

[0034] Determine whether the current number of optimizations has reached the maximum number of optimizations. If so, determine the final parameters of the deep learning image recognition model based on the model parameter vector after optimal information exploration to obtain the displacement test model. Otherwise, return to the local information exploration step.

[0035] Furthermore, the model parameter vector is subjected to local information exploration in random directions, and the model parameter vector after local information exploration is obtained as follows:

[0036]

[0037] in, represents the i-th model parameter vector in the t-th optimization process, represents the i-th model parameter vector in the t+1-th optimization process, that is, the model parameter vector after the i-th local information exploration; i = 1, 2, ..., I, I represents the total number of model parameter vectors, In addition to the model parameter vector Other model parameter vectors except , rand1 represents a random number between (0,1), and π represents the ratio of pi.

[0038] Furthermore, for the model parameter vector after local information exploration, a probabilistic decision method is used to perform global information exploration on the model parameter vector in a random direction, and the model parameter vector after global information exploration is obtained as follows:

[0039] For each model parameter vector after global information exploration, its mutation probability is determined as:

[0040]

[0041] Among them, θ represents the mutation probability, represents the fitness of the model parameter vector after the k-th global information exploration, represents the fitness corresponding to the optimal model parameter vector, and exp represents the exponential function with the natural constant e as the base;

[0042] Determine the number of mutations:

[0043] η t =INT(αcos(πt / T)*η t-1 )

[0044] Among them, η t represents the number of mutations in the t-th optimization process, η t-1 represents the number of mutations in the t-1th optimization process, INT represents the integer function, α represents the attenuation factor, T represents the maximum number of optimizations, and π represents the circumference of a circle;

[0045] Arrange the model parameter vectors after global information exploration in descending order of fitness, and sort the first η t The model parameter vector is taken out as the target model parameter vector;

[0046] For the target model parameter vector, based on the mutation probability, the roulette wheel method is used for exploration:

[0047]

[0048] in, represents the mth target model parameter vector in the tth optimization process, represents the model parameter vector after the mth global information exploration, m = 1, 2, ..., I, represents the model parameter vector obtained through Levy flight, rand2 represents a random number between (0,1), π represents pi, and || represents the absolute value.

[0049] Furthermore, for the model parameter vector after global information exploration, a multi-information interaction method is used to perform optimal information exploration on the model parameter vector, and the model parameter vector after optimal information exploration is obtained as follows:

[0050]

[0051] in, represents the model parameter vector after the nth global information exploration, represents the model parameter vector after the nth optimal information exploration, rand3 represents a random number between (0,1), and rand4 represents a random number between (0,1). represents the optimal model parameter vector, Represents the center of the model parameter vector.

[0052] Furthermore, real-time test data is acquired, and after the real-time test data is identified using the displacement test model, a laser displacement test result is obtained, including:

[0053] Acquire real-time test data; wherein the real-time test data is a real-time image reflected by the surface of the object being tested and collected by the area array image sensor;

[0054] The real-time test data is input into the displacement test model to obtain the laser displacement test result.

[0055] A laser displacement test method provided by the present invention is based on sample image data and corresponding displacement data, optimizes a pre-constructed deep learning image recognition model to obtain a displacement test model, then obtains real-time test data, and uses the displacement test model to identify the real-time test data to obtain a laser displacement test result. Combining deep learning with laser displacement testing can effectively improve the accuracy and stability of displacement testing. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0057] Figure 1 A flow chart of a laser displacement testing method provided by an embodiment of the present invention.

[0058] Figure 2 A basic principle diagram of the laser displacement test provided by an embodiment of the present invention.

[0059] The above drawings have shown clear embodiments of the present invention, which will be described in more detail below. These drawings and text descriptions are not intended to limit the scope of the present invention in any way, but to illustrate the concept of the present invention for those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0060] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of devices and methods consistent with some aspects of the present invention as detailed in the appended claims.

[0061] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0062] like Figure 1 As shown, an embodiment of the present invention provides a laser displacement testing method, comprising:

[0063] S101, determining first test data between a laser transmitter and a measured object and second test data after the measured object is continuously moved;

[0064] Wherein, the first test data and the second test data both include a test distance and a test image reflected by the surface of the object under test and captured by the area array image sensor;

[0065] In this embodiment, the first test data is data at a fixed distance, which is used as a reference target and combined with deep learning to effectively implement displacement testing. For example, the first test data at a position of 1m can be used as a reference target, and then the second test data can be learned to implement displacement testing.

[0066] like Figure 2As shown, in order for those skilled in the art to better understand the technical solution of the present invention, the basic principle of laser displacement testing is explained, which may include: the laser transmitter emits a visible red laser through a lens to the surface of the object to be measured, and the laser reflected by the object passes through the receiver lens and is received by the internal position detector (PSD). According to different distances, the PSD can "see" this light spot at different angles. According to this angle and the known distance between the laser and the PSD, the digital signal processor can calculate the distance between the sensor and the object to be measured. At the same time, the position of the light beam at the receiving element is processed by analog and digital circuits, and analyzed by the microprocessor to calculate the corresponding output value, and output the standard data signal in proportion within the analog window set by the user. If a switch output is used, it is turned on within the set window and turned off outside the window.

[0067] S102, performing test data fusion according to the first test data and the second test data to determine sample image data and corresponding displacement data;

[0068] Test data fusion can be performed in the following way: the first test data can be used as a fixed feature, the second test data can be used as a variable feature, and then the two types of data can be fused to obtain the features corresponding to each second test data. Finally, deep learning can be performed based on this to achieve displacement testing.

[0069] Optionally, in order to further improve data recognition efficiency and accuracy, the image data may be binarized or the background may be removed to highlight the features of the light spot.

[0070] S103, based on the sample image data and the corresponding displacement data, optimizing the pre-built deep learning image recognition model to obtain a displacement test model;

[0071] A convolutional neural network can be used to build an image deep learning image recognition model, and then the sample image can be used as the actual input and the corresponding displacement data can be used as the expected output to optimize the pre-built deep learning image recognition model, thereby realizing displacement testing.

[0072] S104, acquiring real-time test data, and using the displacement test model to identify the real-time test data to obtain a laser displacement test result.

[0073] A laser displacement test method provided by the present invention is based on sample image data and corresponding displacement data, optimizes a pre-constructed deep learning image recognition model to obtain a displacement test model, then obtains real-time test data, and uses the displacement test model to identify the real-time test data to obtain a laser displacement test result. Combining deep learning with laser displacement testing can effectively improve the accuracy and stability of displacement testing.

[0074] In an embodiment of the present invention, test data fusion is performed according to the first test data and the second test data to determine sample image data and corresponding displacement data, including:

[0075] The test image in the second test data is taken out, and the pixel position corresponding to the light spot in the test image corresponding to any two adjacent positions is determined; the pixel position may refer to the center point of the light spot, or may be the average of all positions of the pixel points where the light spot is located.

[0076] Based on the pixel positions corresponding to the light spots in the test images corresponding to the two adjacent positions and the test distance, a new test image and the test distance corresponding to the new test image are generated by linear interpolation to generate new test data at all distances within the measurement limit;

[0077] For any second test data or any new test data, the test image is spliced ​​with the test image in the first test data to obtain a spliced ​​image, and the spliced ​​image is used as sample image data, and the test distance in the second test data or the new test data is used as the corresponding displacement data.

[0078] In the embodiment of the present invention, taking out the test image in the second test data and determining the pixel positions corresponding to the light spots in the test image corresponding to any two adjacent positions includes:

[0079] Taking out a test image from the second test data;

[0080] For any test image in the second test data, determine the target pixel point where the laser spot reflected by the surface of the object to be tested is located;

[0081] The pixel positions of the target pixels are averaged to obtain the pixel position corresponding to the light spot in the test image.

[0082] Optionally, in addition to the above method for obtaining the pixel position, other methods may be used to obtain the pixel position, for example, using the center point of the light spot as the pixel position, using any leftmost pixel as the pixel position, and so on.

[0083] In an embodiment of the present invention, based on the pixel position corresponding to the light spot in the test image corresponding to the two adjacent positions and the test distance, a new test image and a test distance corresponding to the new test image are generated by linear interpolation to generate new test data at all distances within the measurement range, including:

[0084] Determining the distance between the light spots in the test images corresponding to the two adjacent positions based on the pixel positions corresponding to the light spots in the test images corresponding to the two adjacent positions;

[0085] Determine the actual distance corresponding to the test images corresponding to the two adjacent positions based on the actual test distance corresponding to the test images corresponding to the two adjacent positions;

[0086] A distance ratio is obtained according to the actual distance and the spot distance; wherein the distance ratio is used to characterize the pixel distance corresponding to the unit distance corresponding to the actual distance;

[0087] According to the distance ratio, the change in pixel distance caused by moving the unit distance is determined;

[0088] Based on the test images corresponding to the two adjacent positions and the pixel distance variation, the light spot in the test image is moved by a fixed distance to obtain a new test image, and the actual distance is modified by a unit distance to obtain a test distance corresponding to the new test image;

[0089] The test distances corresponding to the new test images are collectively used as new test data.

[0090] The embodiment of the present invention adopts linear interpolation method to generate a new test image and the test distance corresponding to the new test image, so as to generate new test data at all distances within the measurement limit. By utilizing the characteristic that linear displacement causes linear feature change, only a small amount of data can be collected to realize deep learning, simplify the operation process, and improve the accuracy of displacement testing.

[0091] In an embodiment of the present invention, based on the test images corresponding to the two adjacent positions and the pixel distance variation, the light spot in the test image is moved by a fixed distance to obtain a new test image, and the actual distance is modified by a unit distance to obtain a test distance corresponding to the new test image, including:

[0092] For the test images corresponding to the two adjacent positions, the light spot corresponding to the test image corresponding to one position is taken as the starting point, the light spot corresponding to the test image corresponding to the other position is taken as the end point, and the light spot in the test image is moved by a fixed distance to obtain a new test image;

[0093] While moving the light spot, the actual distance corresponding to the starting point is modified by the unit distance to obtain the test distance corresponding to the new test image.

[0094] For example, when operating on a test data, after the light spot is moved according to the pixel distance variation, the test distance corresponding to the test data is increased by a unit distance.

[0095] In the prior art, gradient descent is often used to optimize deep learning image recognition models, but this can easily lead to inadequate optimization of model parameters, poor final data recognition effect, and failure to perform displacement testing normally. Therefore, an embodiment of the present invention provides an optimization algorithm for a deep learning image recognition model to improve the accuracy of displacement testing.

[0096] In an embodiment of the present invention, based on sample image data and corresponding displacement data, a pre-built deep learning image recognition model is optimized to obtain a displacement test model, including:

[0097] For a pre-built deep learning image recognition model, randomly initialize model parameters corresponding to the deep learning image recognition model, and form the model parameters into a vector to obtain a model parameter vector;

[0098] Based on the above-mentioned model parameter vector acquisition method, multiple different model parameter vectors are acquired to complete the generation of the initial solution;

[0099] In the current optimization process, the model parameter vector is subjected to local information exploration in random directions to obtain the model parameter vector after local information exploration;

[0100] For the model parameter vector after local information exploration, a probabilistic decision method is used to perform global information exploration on the model parameter vector in a random direction to obtain the model parameter vector after global information exploration;

[0101] For the model parameter vector after global information exploration, a multi-information interaction method is used to perform optimal information exploration on the model parameter vector to obtain the model parameter vector after optimal information exploration;

[0102] Determine whether the current number of optimizations has reached the maximum number of optimizations. If so, determine the final parameters of the deep learning image recognition model based on the model parameter vector after optimal information exploration to obtain the displacement test model. Otherwise, return to the local information exploration step.

[0103] In the embodiment of the present invention, local information exploration is performed on the model parameter vector in a random direction, and the model parameter vector obtained after the local information exploration is:

[0104]

[0105] in, represents the i-th model parameter vector in the t-th optimization process, represents the i-th model parameter vector in the t+1-th optimization process, that is, the model parameter vector after the i-th local information exploration; i = 1, 2, ..., I, I represents the total number of model parameter vectors, In addition to the model parameter vector Other model parameter vectors except , rand1 represents a random number between (0,1), and π represents the ratio of pi.

[0106] The local information exploration in random directions provided by the embodiments of the present invention can enable the model parameter vector to explore in random directions. At the same time, it combines the information contained in other model parameter vectors to avoid repeated exploration, and can effectively improve the exploration efficiency of the algorithm.

[0107] In the embodiment of the present invention, for the model parameter vector after local information exploration, a probabilistic decision method is used to perform global information exploration on the model parameter vector in a random direction, and the model parameter vector after global information exploration is obtained as follows:

[0108] For each model parameter vector after global information exploration, its mutation probability is determined as:

[0109]

[0110] Among them, θ represents the mutation probability, represents the fitness of the model parameter vector after the k-th global information exploration, represents the fitness corresponding to the optimal model parameter vector, and exp represents the exponential function with the natural constant e as the base;

[0111] Determine the number of mutations:

[0112] η t =INT(αcos(πt / T)*η t-1 )

[0113] Among them, η t represents the number of mutations in the t-th optimization process, η t-1 represents the number of mutations in the t-1th optimization process, INT represents the integer function, α represents the attenuation factor, T represents the maximum number of optimizations, and π represents the circumference of a circle;

[0114] Arrange the model parameter vectors after global information exploration in descending order of fitness, and sort the first η t The model parameter vector is taken out as the target model parameter vector;

[0115] For the target model parameter vector, based on the mutation probability, the roulette wheel method is used for exploration:

[0116]

[0117] in, represents the mth target model parameter vector in the tth optimization process, represents the model parameter vector after the mth global information exploration, m = 1, 2, ..., I, represents the model parameter vector obtained through Levy flight, rand2 represents a random number between (0,1), π represents pi, and || represents the absolute value.

[0118] The global information exploration provided by the embodiment of the present invention can provide more mutation probabilities in the early stage of the algorithm and gradually reduce the mutation probability in the later stage of the algorithm, which can effectively ensure the convergence speed of the algorithm, and globally explore the model parameter vectors in more concentrated positions, which can effectively avoid the defect that the prior art is prone to falling into local optimality.

[0119] In the embodiment of the present invention, for the model parameter vector after global information exploration, a multi-information interaction method is used to perform optimal information exploration on the model parameter vector, and the model parameter vector after optimal information exploration is obtained as follows:

[0120]

[0121] in, represents the model parameter vector after the nth global information exploration, represents the model parameter vector after the nth optimal information exploration, rand3 represents a random number between (0,1), and rand4 represents a random number between (0,1). represents the optimal model parameter vector, Represents the center of the model parameter vector.

[0122] The optimal information exploration provided by the embodiment of the present invention simultaneously learns the optimal model parameter vector and the center of the model parameter vector, which can effectively improve the algorithm exploration accuracy and efficiency. The center of the model parameter vector can be a vector composed of the average of each dimension of all model parameter vectors. When the algorithm reaches the later stage, it can be more conducive to finding the optimal position.

[0123] In an embodiment of the present invention, real-time test data is acquired, and after the real-time test data is identified by using the displacement test model, a laser displacement test result is obtained, including:

[0124] Acquire real-time test data; wherein the real-time test data is a real-time image reflected by the surface of the object being tested and collected by the area array image sensor;

[0125] The real-time test data is input into the displacement test model to obtain the laser displacement test result.

[0126] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0127] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0128] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0129] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0130] A person of ordinary skill in the art can understand that all or part of the steps in realizing the above-mentioned facts and methods can be completed by instructing the relevant hardware through a program, and the program involved or the program described can be stored in a computer-readable storage medium. When the program is executed, it includes the following steps: At this time, the corresponding method steps are derived, and the storage medium can be ROM / RAM, a disk, an optical disk, etc.

[0131] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A laser displacement testing method, characterized in that: include: Determine first test data between the laser transmitter and the object to be measured and second test data after the object to be measured is continuously moved; Wherein, the first test data and the second test data both include a test distance and a test image reflected by the surface of the object under test and captured by the area array image sensor; Performing test data fusion according to the first test data and the second test data to determine sample image data and corresponding displacement data; Based on the sample image data and the corresponding displacement data, the pre-built deep learning image recognition model is optimized to obtain a displacement test model; Real-time test data is acquired, and after the real-time test data is identified using the displacement test model, a laser displacement test result is obtained.

2. The laser displacement testing method according to claim 1, characterized in that: Performing test data fusion according to the first test data and the second test data to determine sample image data and corresponding displacement data includes: Taking out the test image in the second test data, and determining the pixel positions corresponding to the light spots in the test image corresponding to any two adjacent positions; Based on the pixel positions corresponding to the light spots in the test images corresponding to the two adjacent positions and the test distance, a new test image and the test distance corresponding to the new test image are generated by linear interpolation to generate new test data at all distances within the measurement limit; For any second test data or any new test data, the test image is spliced ​​with the test image in the first test data to obtain a spliced ​​image, and the spliced ​​image is used as sample image data, and the test distance in the second test data or the new test data is used as the corresponding displacement data.

3. The laser displacement testing method according to claim 2, characterized in that: Taking out the test image in the second test data, and determining the pixel positions corresponding to the light spots in the test image corresponding to any two adjacent positions, including: Taking out a test image from the second test data; For any test image in the second test data, determine the target pixel point where the laser spot reflected by the surface of the object to be tested is located; The pixel positions of the target pixels are averaged to obtain the pixel position corresponding to the light spot in the test image.

4. The laser displacement testing method according to claim 3, characterized in that: Based on the pixel position corresponding to the light spot in the test image corresponding to the two adjacent positions and the test distance, a new test image and the test distance corresponding to the new test image are generated by linear interpolation to generate new test data at all distances within the measurement limit, including: Determining the distance between the light spots in the test images corresponding to the two adjacent positions based on the pixel positions corresponding to the light spots in the test images corresponding to the two adjacent positions; Determine the actual distance corresponding to the test images corresponding to the two adjacent positions based on the actual test distance corresponding to the test images corresponding to the two adjacent positions; A distance ratio is obtained according to the actual distance and the spot distance; wherein the distance ratio is used to characterize the pixel distance corresponding to the unit distance corresponding to the actual distance; According to the distance ratio, the change in pixel distance caused by moving the unit distance is determined; Based on the test images corresponding to the two adjacent positions and the pixel distance variation, the light spot in the test image is moved by a fixed distance to obtain a new test image, and the actual distance is modified by a unit distance to obtain a test distance corresponding to the new test image; The test distances corresponding to the new test images are collectively used as new test data.

5. The laser displacement testing method according to claim 4, characterized in that: Based on the test images corresponding to the two adjacent positions and the pixel distance variation, the light spot in the test image is moved by a fixed distance to obtain a new test image, and the actual distance is modified by a unit distance to obtain a test distance corresponding to the new test image, including: For the test images corresponding to the two adjacent positions, the light spot corresponding to the test image corresponding to one position is taken as the starting point, the light spot corresponding to the test image corresponding to the other position is taken as the end point, and the light spot in the test image is moved by a fixed distance to obtain a new test image; While moving the light spot, the actual distance corresponding to the starting point is modified by the unit distance to obtain the test distance corresponding to the new test image.

6. The laser displacement testing method according to any one of claims 1 to 5, characterized in that: Based on the sample image data and the corresponding displacement data, the pre-built deep learning image recognition model is optimized to obtain the displacement test model, including: For a pre-built deep learning image recognition model, randomly initialize model parameters corresponding to the deep learning image recognition model, and form the model parameters into a vector to obtain a model parameter vector; Based on the above-mentioned model parameter vector acquisition method, multiple different model parameter vectors are acquired to complete the generation of the initial solution; In the current optimization process, the model parameter vector is subjected to local information exploration in random directions to obtain the model parameter vector after local information exploration; For the model parameter vector after local information exploration, a probabilistic decision method is used to perform global information exploration on the model parameter vector in a random direction to obtain the model parameter vector after global information exploration; For the model parameter vector after global information exploration, a multi-information interaction method is used to perform optimal information exploration on the model parameter vector to obtain the model parameter vector after optimal information exploration; Determine whether the current number of optimizations has reached the maximum number of optimizations. If so, determine the final parameters of the deep learning image recognition model based on the model parameter vector after optimal information exploration to obtain the displacement test model. Otherwise, return to the local information exploration step.

7. The laser displacement testing method according to claim 6, characterized in that: Perform local information exploration on the model parameter vector in random directions, and obtain the model parameter vector after local information exploration: in, represents the i-th model parameter vector in the t-th optimization process, represents the i-th model parameter vector in the t+1-th optimization process, that is, the model parameter vector after the i-th local information exploration; i = 1, 2, ..., I, I represents the total number of model parameter vectors, In addition to the model parameter vector Other model parameter vectors except , rand1 represents a random number between (0,1), and π represents the ratio of pi.

8. The laser displacement testing method according to claim 7, characterized in that: For the model parameter vector after local information exploration, the probability decision method is used to explore the global information of the model parameter vector in a random direction, and the model parameter vector after global information exploration is obtained as follows: For each model parameter vector after global information exploration, its mutation probability is determined as: Among them, θ represents the mutation probability, represents the fitness of the model parameter vector after the k-th global information exploration, represents the fitness corresponding to the optimal model parameter vector, and exp represents the exponential function with the natural constant e as the base; Determine the number of mutations: or t =INT(αcos(πt / T)*η t-1 ) Among them, η t represents the number of mutations in the t-th optimization process, η t-1 represents the number of mutations in the t-1th optimization process, INT represents the integer function, α represents the attenuation factor, T represents the maximum number of optimizations, and π represents the circumference of a circle; Arrange the model parameter vectors after global information exploration in descending order of fitness, and sort the first η t The model parameter vector is taken out as the target model parameter vector; For the target model parameter vector, based on the mutation probability, the roulette wheel method is used for exploration: in, represents the mth target model parameter vector in the tth optimization process, represents the model parameter vector after the mth global information exploration, m = 1, 2, ..., I, represents the model parameter vector obtained through Levy flight, rand2 represents a random number between (0,1), π represents pi, and || represents the absolute value.

9. The laser displacement testing method according to claim 8, characterized in that: For the model parameter vector after global information exploration, the multi-information interaction method is used to perform optimal information exploration on the model parameter vector, and the model parameter vector after optimal information exploration is obtained as follows: in, represents the model parameter vector after the nth global information exploration, represents the model parameter vector after the nth optimal information exploration, rand3 represents a random number between (0,1), and rand4 represents a random number between (0,1). represents the optimal model parameter vector, Represents the center of the model parameter vector.

10. The laser displacement testing method according to claim 1, characterized in that: After acquiring real-time test data and using the displacement test model to identify the real-time test data, a laser displacement test result is obtained, including: Acquire real-time test data; wherein the real-time test data is a real-time image reflected by the surface of the object being tested and collected by the area array image sensor; The real-time test data is input into the displacement test model to obtain the laser displacement test result.

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