CN116989691A - Visual measurement method and device for material strain, visual strain gauge and storage medium - Google Patents
Visual measurement method and device for material strain, visual strain gauge and storage medium Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
- G01B11/16—Measuring arrangements characterised by the use of optical techniques for measuring the deformation in a solid, e.g. optical strain gauge
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
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Abstract
The application relates to photogrammetry technology, and discloses a visual measurement method of material strain, which comprises the following steps: when a preset pattern is detected in the picture acquisition range of the vision sensor, taking a material provided with the preset pattern as a measured material, wherein the preset pattern is printed on the surface of the measured material in advance; controlling the vision sensor to acquire a series of pictures of the measured material in the strain process; identifying the display area of the preset pattern in the picture as a characteristic area, and extracting a series of characteristic area patterns from a series of pictures; and identifying the displacement amount of the characteristic points in a series of characteristic region patterns, and determining the strain data of the measured material based on the displacement amount. The application also discloses a vision measuring device, computer equipment, a vision strain gauge and a computer readable storage medium. The application aims to carry out high-efficiency and accurate strain measurement on a measured material.
Description
Technical Field
The present application relates to the field of photogrammetry, and in particular, to a visual measurement method, a visual measurement device, a computer device, a visual strain gauge, and a computer readable storage medium for measuring material strain.
Background
With the wide spread of detection of material strain in mechanical properties in industrial applications, it is increasingly important how to accurately and efficiently detect material strain. The strain detection can be applied to various material tests and structural tests, and is used for ensuring the qualified product quality on one hand and verifying the rationality of material components and structural design on the other hand.
The existing material strain testing mode generally needs to polish the surface of a tested material smoothly, then paste the resistance strain gauge on the surface of the tested object by using glue, weld the connecting terminal of the resistance strain gauge with a wire after the glue is firmly pasted, connect the wire of the resistance strain gauge into a data acquisition device after welding is completed, and read the numerical value of the resistance strain gauge by the data acquisition device so as to obtain the data related to the material strain.
However, the application range of the resistance strain gauge is narrow, and the resistance strain gauge is generally only applicable to the fields of static or low-frequency mechanical tests, material mechanics, building structures and the like, and is not applicable to measurement of high-frequency vibration and impact response; the resistance strain gauge is composed of metal foil or wire, so that the resistance value is easy to change due to the influence of temperature, and therefore, temperature compensation is needed during measurement, otherwise, the measurement accuracy is affected; in addition, the arrangement of the resistance strain gauge is complex and tedious, and the resistance strain gauge has certain technical requirements for the operations of pasting, connecting, debugging the battery strain gauge and the like of related testers, and errors are easy to occur due to improper operation. These factors all make it difficult to achieve efficient and accurate measurement of material strain data using resistive strain gages.
The foregoing is provided merely for the purpose of facilitating understanding of the technical solutions of the present application and is not intended to represent an admission that the foregoing is prior art.
Disclosure of Invention
The application mainly aims to provide a visual measurement method, a visual measurement device, computer equipment, a visual strain gauge and a computer readable storage medium for measuring the strain of a measured material with high efficiency and accuracy.
In order to achieve the above object, the present application provides a visual measurement method of material strain, comprising the steps of:
when a preset pattern is detected in the picture acquisition range of the vision sensor, taking a material provided with the preset pattern as a measured material, wherein the preset pattern is printed on the surface of the measured material in advance;
controlling the vision sensor to acquire a series of pictures of the measured material in the strain process;
identifying the display area of the preset pattern in the picture as a characteristic area, and extracting a series of characteristic area patterns from a series of pictures;
and identifying the displacement amount of the characteristic points in a series of characteristic region patterns, and determining the strain data of the measured material based on the displacement amount.
Optionally, the step of identifying the display area of the preset pattern in the picture as the feature area includes:
generating a plurality of candidate areas in the picture based on an Anchor-based detection algorithm, carrying out position regression and category identification on the plurality of candidate areas to identify the display areas of the preset patterns from the plurality of candidate areas, and taking the identified display areas as characteristic areas.
Optionally, the step of identifying the displacement of the feature point in the series of feature region patterns includes:
and identifying characteristic points in a series of characteristic region patterns, and calculating characteristic point identification results by utilizing a sub-pixel displacement measurement algorithm to obtain corresponding displacement.
Optionally, before the step of calculating the feature point identification result by using the subpixel displacement measurement algorithm to obtain the corresponding displacement, the method further includes:
identifying a strain scene represented by the preset pattern, wherein the preset pattern is an identification code pattern, and the identification code pattern is associated with a corresponding strain scene;
and invoking a sub-pixel displacement measurement algorithm corresponding to the strain scene.
Optionally, the visual measurement method of material strain further includes:
if a plurality of characteristic regions are identified in the same picture, corresponding strain data is calculated for each characteristic region.
Optionally, after the step of identifying the displacement amount of the feature point in the series of feature region patterns and determining the strain data of the measured material based on the displacement amount, the method further includes:
and outputting the strain data to an interaction interface and/or generating a corresponding report file.
In order to achieve the above object, the present application also provides a vision measuring device including:
the detection module is used for taking a material provided with a preset pattern as a detected material when the preset pattern is detected in the picture acquisition range of the visual sensor, wherein the preset pattern is printed on the surface of the detected material in advance;
the acquisition module is used for controlling the vision sensor to acquire a series of pictures of the measured material in the strain process;
the identification module is used for identifying the display area of the preset pattern in the picture as a characteristic area and extracting a series of characteristic area patterns from a series of pictures;
and the calculation module is used for identifying the displacement amount of the characteristic points in a series of characteristic region patterns and determining the strain data of the measured material based on the displacement amount.
To achieve the above object, the present application also provides a computer apparatus comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, which when executed by the processor, performs the steps of a method for visual measurement of material strain as described above.
To achieve the above object, the present application also provides a visual strain gauge comprising a visual sensor and a computer device as described above, wherein the visual sensor is communicatively connected to the computer device.
To achieve the above object, the present application also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of a visual measurement method of material strain as described above.
The visual measurement method, the visual measurement device, the computer equipment, the visual strain gauge and the computer readable storage medium for the material strain provided by the application not only can accurately obtain the strain data of the measured material, but also can measure the material strain in various temperature environments without the influence of temperature on the implementation conditions of the scheme, thereby providing greater flexibility and reliability and enabling the scheme to be normally implemented in different temperature environment conditions; and the scheme is not limited to the shape and the size of the measured material, and can be applied to strain calculation as long as the preset pattern can be covered and captured by the vision sensor, so that the scheme has wide applicability and can be applied to multiple fields and material types.
Compared with the traditional method for detecting the strain of the material by adopting the resistance strain gauge, the method provided by the application does not need to use a complex resistance strain gauge, does not need to carry out complex wiring and connection, only needs to print a preset pattern on the measured material in advance, and uses a vision sensor to acquire pictures for processing and analysis, thereby simplifying the implementation process, reducing the complexity and technical requirements of the implementation, greatly reducing the operation time and the workload and improving the efficiency of measuring the strain of the material because the method does not need to be manually operated and measured.
Drawings
FIG. 1 is a schematic diagram showing steps of a method for visually measuring strain of a material according to an embodiment of the present application;
FIG. 2 is a schematic diagram showing the relative positions of a device and a measured material according to an embodiment of the present application;
FIG. 3 is a schematic diagram of an apparatus for visual measurement according to an embodiment of the present application;
fig. 4 is a schematic block diagram illustrating an internal structure of a computer device according to an embodiment of the present application.
The achievement of the objects, functional features and advantages of the present application will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below are exemplary and intended to illustrate the present application and should not be construed as limiting the application, and all other embodiments, based on the embodiments of the present application, which may be obtained by persons of ordinary skill in the art without inventive effort, are within the scope of the present application.
Furthermore, the description of "first," "second," etc. in this disclosure is for descriptive purposes only (e.g., to distinguish between identical or similar elements) and is not to be construed as indicating or implying a relative importance or an implicit indication of the number of features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In addition, the technical solutions of the embodiments may be combined with each other, but it is necessary to base that the technical solutions can be realized by those skilled in the art, and when the technical solutions are contradictory or cannot be realized, the combination of the technical solutions should be considered to be absent and not within the scope of protection claimed in the present application.
Referring to fig. 1, in one embodiment, the method for visual measurement of material strain comprises:
step S10, when a preset pattern is detected in the picture acquisition range of the vision sensor, taking a material provided with the preset pattern as a measured material, wherein the preset pattern is printed on the surface of the measured material in advance;
step S20, controlling the vision sensor to acquire a series of pictures of the measured material in the strain process;
step S30, recognizing the display area of the preset pattern in the picture as a characteristic area, and extracting a series of characteristic area patterns from a series of pictures;
and S40, identifying the displacement amount of the characteristic points in a series of characteristic region patterns, and determining the strain data of the measured material based on the displacement amount.
In this embodiment, the execution terminal of the embodiment may be a computer device, or a visual strain gauge provided with the computer device, or a virtual device (such as a visual measurement device) or other devices for controlling the computer device; the following description will take an example in which an execution terminal is a computer device.
As described in step S10, referring to fig. 2, a computer device and a vision sensor are used to perform strain measurement on a measured material, wherein the computer device and the vision sensor are in communication connection, and the vision sensor not only transmits the acquired picture to the computer device, but also is controlled by the computer device; the measured materials are various objects which need to be measured in the actual measuring site according to the scheme.
Optionally, a desired preset pattern is printed on the surface of the measured material in advance, and the preset pattern may be a pattern (preferably an identification code pattern, such as a two-dimensional code pattern) with specific marks, patterns or characters for subsequent identification and detection. The seal corresponding to the preset pattern can be manufactured in advance, and then the corresponding preset pattern is covered on the tested material by using the seal.
After the computer equipment and the vision sensor are correspondingly started, when the material printed with the preset pattern enters the picture acquisition range of the vision sensor, the computer equipment can judge the material provided with the preset pattern as the measured material if the preset pattern exists in the picture after analyzing the picture acquired by the vision sensor.
It should be appreciated that the computer device is preloaded with the identification program corresponding to the predetermined pattern, and the computer device may be running in the environment of the operating system of the window 10.
Therefore, as long as the preset pattern of the measured material is within the picture acquisition range of the visual sensor, the preset pattern can be automatically identified without manual adjustment, so that the error of manual intervention is reduced, the labor cost is saved, and the visual sensor can be horizontally placed, vertically placed, overlooked and looking up and placed, and even placed at any angle, and can be supported.
As described in step S20, the vision sensor is deployed in the strain field of the measured material, and when the computer equipment determines the measured material, a corresponding strain picture of the measured material can be acquired; tension/compression devices (not shown) may be disposed at two ends of the measured material, so as to stretch or compress the measured material by the tension/compression devices, thereby strain the measured material. Of course, the strain process of the measured material can be completed by adopting a fully-automatic robot to realize the processes of automatic loading, clamping, stretching/compressing and the like.
It should be noted that the above description of the way in which the material is strained is merely exemplary, and that in practice other ways of straining the material may be used.
Optionally, the vision sensor is responsible for collecting pictures of the measured material in the strain process, and when the vision sensor operates, the strain pictures of the measured material in the picture collecting range can be continuously captured according to the preset shooting frequency, in view of the fact that strain treatment of the measured material is a continuous process, a plurality of pictures of the measured material at different moments can be shot within a certain period of time, and the shot pictures are transmitted to the computer equipment for further processing by the computer equipment.
It should be appreciated that the computer device may capture pattern information of the measured material under different strain states through a series of pictures.
Of course, if the computer device is provided with the interactive interface, the computer device may further wait for the user to trigger the image acquisition button at the interactive interface and then control the vision sensor to acquire the strain image of the measured material.
In step S30, the computer device first puts the acquired pictures into an internal allocated memory pool, and allocates a sequence number and an acquisition time point to each picture for later time axis display.
Optionally, by an image processing technology, identifying a display area matched with a preset pattern in the picture, marking the display area as a characteristic area, and extracting a corresponding characteristic area pattern, so that a series of characteristic area patterns can be obtained after the characteristic area extraction is performed on a series of pictures.
Optionally, the step of identifying the display area of the preset pattern in the picture as the feature area includes:
generating a plurality of candidate areas in the picture based on an Anchor-based detection algorithm, carrying out position regression and category identification on the plurality of candidate areas to identify the display areas of the preset patterns from the plurality of candidate areas, and taking the identified display areas as characteristic areas.
As described in step S40, feature points are identified in a series of feature region patterns, and their displacement amounts between the different patterns are calculated, which can be used to quantify the degree of strain of the measured material during the strain process.
The feature region may include a plurality of feature points. Taking the preset pattern as an identification code pattern as an example, the feature points may be mark points on four corners in the identification code pattern.
Optionally, the step of identifying the displacement of the feature point in the series of feature region patterns includes: and identifying characteristic points in a series of characteristic region patterns, and calculating characteristic point identification results by utilizing a sub-pixel displacement measurement algorithm to obtain corresponding displacement.
For example, a high-precision and high-efficiency sub-pixel displacement measurement algorithm (or digital correlation calculation algorithm) is adopted to identify characteristic points in each pattern, and coordinates corresponding to the characteristic points are collected and tracked along with the patterns, so that displacement of the characteristic points in the strain process of the measured material, such as displacement of the characteristic points in the longitudinal direction, namely a strain result corresponding to the measured material in the longitudinal direction, is obtained; the displacement of the characteristic point in the transverse direction corresponds to the strain result of the measured material in the transverse direction.
Further, strain data of the measured material is determined based on the analysis result of the displacement amount, and the strain data may be strain amount, poisson ratio, or the like. It should be noted that strain data may be used to describe deformation of a material during strain, such as stretching, compression, bending, etc.
In this way, the situation that the preset pattern is stretched/compressed in the strain process of the measured material is calculated by analyzing the displacement of the characteristic points in the preset pattern on the surface of the measured material, and the corresponding strain data of the measured material can be calculated.
In an embodiment, strain data of a measured material can be accurately obtained, and implementation conditions of the scheme are not affected by temperature, so that measurement can be performed in various temperature environments, and greater flexibility and reliability are provided, so that the scheme can be normally implemented under different temperature environments; and the scheme is not limited to the shape and the size of the measured material, and can be applied to strain calculation as long as the preset pattern can be covered and captured by the vision sensor, so that the scheme has wide applicability and can be applied to multiple fields and material types.
Compared with the traditional method for detecting the strain of the material by adopting the resistance strain gauge, the method does not need to use a complicated resistance strain gauge, does not need to carry out complicated wiring and connection, only needs to print a preset pattern on the measured material in advance, and uses a visual sensor to acquire pictures for processing and analysis, thereby simplifying the implementation process, reducing the complexity and technical requirements of the implementation, greatly reducing the operation time and the workload because the method does not need manual operation and measurement, and improving the efficiency of the material strain measurement.
Besides, the implementation of the scheme only needs to prefabricate the preset pattern and use proper image processing software besides the visual strain gauge, so that the implementation cost of the scheme is low.
In an embodiment, based on the foregoing embodiment, the step of identifying the display area of the preset pattern in the picture as the feature area includes:
generating a plurality of candidate areas in the picture based on an Anchor-based detection algorithm, carrying out position regression and category identification on the plurality of candidate areas to identify the display areas of the preset patterns from the plurality of candidate areas, and taking the identified display areas as characteristic areas.
In this embodiment, in each picture, a plurality of candidate regions are generated using an anchor-based detection algorithm, and these candidate regions are candidate choices of potential preset pattern display regions.
Optionally, for the generated candidate region, position regression and category recognition are performed. The position regression is to accurately position the boundary frame of the candidate region through a trained model so as to accurately identify the display region of the preset pattern; the category identification is to classify the candidate areas and determine whether the candidate areas contain preset patterns.
Optionally, identifying a display area of the preset pattern from a plurality of candidate areas according to the results of position regression and category identification; wherein the identified display area is considered as a characteristic area, i.e. an area of the preset pattern in the picture.
Thus, accuracy and efficiency of identification of the display area of the preset pattern can be improved, and accurate characteristic area patterns can be provided for calculation of strain data.
In an embodiment, on the basis of the foregoing embodiment, the step of identifying the displacement amount of the feature point in the series of feature area patterns includes:
and identifying characteristic points in a series of characteristic region patterns, and calculating characteristic point identification results by utilizing a sub-pixel displacement measurement algorithm to obtain corresponding displacement.
In this embodiment, for a series of feature region patterns, feature points are first extracted from each pattern using a feature point detection algorithm (e.g., SIFT, SURF, etc.). The feature points can be specific positions in a preset pattern or protruding features on the surface of the material, so that the stability and the traceability are good.
Optionally, after the feature points are identified, the positions and displacements of the feature points are calculated using a subpixel displacement measurement algorithm. The sub-pixel displacement measurement algorithm is a method for accurately calculating the displacement of the characteristic points, and utilizes the change of the gray value of the image and the local characteristics to determine the sub-pixel level displacement of the characteristic points, and the accuracy of displacement measurement can be improved by carrying out sub-pixel level calculation on the displacement of the characteristic points.
According to the time sequence of a series of characteristic region patterns, the first pattern in the series of characteristic region patterns and other patterns are subjected to characteristic point matching calculation in sequence, displacement amounts of the first pattern in different patterns are calculated for each characteristic point based on a sub-pixel displacement measurement algorithm, and the displacement amounts reflect strain conditions of measured materials under different time or loading conditions.
Alternatively, in order to improve the accuracy of the matching result, sub-pixel level matching and interpolation algorithms may be used, which make fine adjustment of the position of the feature point at the sub-pixel level by changing the pixel gray value around the feature point. For example, bilinear interpolation, gaussian pyramid interpolation, sub-pixel curve fitting, or other methods may be used to fine tune the location of the matching feature points.
It should be understood that, with the feature point position in the initial reference state (i.e., the feature point position of the first pattern) as a reference, the displacement information matched between the feature point position in the initial reference state and the sub-pixel displacement measurement algorithm is accumulated and calculated, so as to obtain the displacement amount of the feature point at each moment.
Optionally, after the displacement information of the feature area is calculated by the displacement amount, the corresponding deformation amount can be further calculated according to the geometric shape of the feature area and the position of the feature point. This can be calculated by applying an interpolation algorithm to the entire feature area, in combination with the reference state and the displacement amounts at the respective moments.
Therefore, the accuracy of calculating the characteristic point displacement can be improved by utilizing the sub-pixel displacement measurement algorithm, and further high-accuracy strain data is obtained and used for analyzing and researching the strain characteristics of the material.
In an embodiment, on the basis of the foregoing embodiment, before the step of calculating the feature point identification result by using a subpixel displacement measurement algorithm to obtain the corresponding displacement, the method further includes:
identifying a strain scene represented by the preset pattern, wherein the preset pattern is an identification code pattern, and the identification code pattern is associated with a corresponding strain scene;
and invoking a sub-pixel displacement measurement algorithm corresponding to the strain scene.
In this embodiment, the preset pattern is an identification code pattern, and a corresponding strain scene is associated on the identification code pattern, and strain scene information related to the pattern can be obtained by identifying and decoding the identification code pattern through an image processing technology.
Wherein the types of strain scenarios include static or low frequency mechanical tests, material mechanics, building structures, high frequency vibrations and measurements of impulse response.
Optionally, according to the identified strain scene information, invoking a sub-pixel displacement measurement algorithm corresponding to the strain scene. Different strain scenarios may require displacement calculation of feature points using different sub-pixel displacement measurement algorithms to improve accuracy and accommodate specific strain conditions.
It should be appreciated that the computer device is pre-programmed with sub-pixel displacement measurement algorithms suitable for different strain scenarios; the related engineers can wait by adjusting some algorithm parameters of the sub-pixel displacement measurement algorithm and the type of interpolation algorithm used so as to set up a plurality of sub-pixel displacement measurement algorithms which can be respectively suitable for different strain scenes.
And then, continuing to calculate the characteristic point identification result by using the sub-pixel displacement measurement algorithm to obtain a corresponding displacement. The position of the characteristic point is accurately calculated by using a sub-pixel displacement measurement algorithm, and the sub-pixel level displacement of the characteristic point is obtained.
In this way, the accuracy and precision of displacement calculation can be further improved, and proper sub-pixel displacement measurement algorithms can be selected for different strain scenes, namely, by identifying and classifying different types of strain scenes, proper measurement methods and algorithms can be selected to meet the accurate measurement requirement of strain in specific scenes.
In addition, if the strain process of the measured material is accomplished by a fully automated robot, the identification code pattern may also be correlated to control information of the robot to set the strength and/or direction of the robot to stretch/compress the material.
When the computer collects an initial picture of the measured material containing a preset pattern (namely an identification code pattern) through the visual sensor, the initial picture is transmitted to the robot, so that the robot reads the identification code pattern in the initial picture, and parameters related to control information can be obtained after the robot decodes the identification code pattern, wherein the parameters comprise stretching/compressing force, direction, speed and the like. The robot automatically adjusts its motion according to these parameters and starts stretching/compressing the measured material based on the adjusted parameters. Meanwhile, the vision sensor can continuously acquire corresponding pictures and transmit the corresponding pictures to the computer equipment in the process of measuring the strain of the material to be measured, so that the computer equipment can complete subsequent operation.
Thus, by associating the control information of the robot with the identification code pattern, the control of the stretching/compressing process of the measured material can be realized fully automatically, and the design can improve the efficiency of operation and obtaining the material strain data, and simultaneously reduce the cost of manual operation.
In an embodiment, based on the foregoing embodiment, the visual measurement method of material strain further includes:
if a plurality of characteristic regions are identified in the same picture, corresponding strain data is calculated for each characteristic region.
In this embodiment, if a plurality of feature regions are identified for the same acquired picture, corresponding strain data is calculated for each identified feature region based on the method adopted in step S40.
Optionally, the calculated strain data may be further analyzed and recorded. The strain data of different characteristic areas can be counted and compared to obtain comprehensive strain distribution information. Wherein strain data may be visualized in the form of charts, images, etc. to better understand and interpret strain conditions.
Thus, after the tester prints corresponding preset patterns on a plurality of positions of the tested material, the computer equipment automatically calculates the strain data of the characteristic areas corresponding to the preset patterns, so that more detailed and comprehensive strain information of the tested material is obtained.
In an embodiment, after the step of identifying the displacement amount of the feature point in the series of feature area patterns and determining the strain data of the measured material based on the displacement amount, the method further includes:
and outputting the strain data to an interaction interface and/or generating a corresponding report file.
In this embodiment, the calculated strain data is output to the interaction interface, so that the user can check and analyze the strain data. The interactive interface may be a graphical interface or command line interface for presenting graphs, images, etc. of strain data and providing interactive operation and visualization functions.
Optionally, a corresponding report file may also be generated, and the strain data may be sorted and exported in a tabular form. The report file can comprise statistical information, chart display, related analysis results and the like of the strain data, so that a user can conveniently conduct further research, report or archive.
Therefore, after the strain data of the measured material is determined, the strain data is output to the interactive interface, and a corresponding report file is generated, so that a user can conveniently review the strain data through the interactive interface, carry out visual analysis, and conveniently archive and share the strain data through the report file.
Referring to fig. 3, there is also provided a vision measuring device Z10 according to an embodiment of the present application, including:
the detection module Z11 is used for taking a material provided with a preset pattern as a detected material when the preset pattern is detected in the picture acquisition range of the vision sensor, wherein the preset pattern is printed on the surface of the detected material in advance;
the acquisition module Z12 is used for controlling the vision sensor to acquire a series of pictures of the measured material in the strain process;
the identification module Z13 is used for identifying the display area of the preset pattern in the picture as a characteristic area and extracting a series of characteristic area patterns from a series of pictures;
and the calculation module Z14 is used for identifying the displacement amount of the characteristic points in a series of characteristic region patterns and determining the strain data of the measured material based on the displacement amount.
Alternatively, the vision measurement device may be a virtual control device (such as a virtual machine), or may be a physical device (such as a physical device other than a computer device that performs the corresponding method).
The embodiment of the application also provides computer equipment, and the internal structure of the computer equipment can be shown in fig. 4. The computer device includes a processor, a memory, a communication interface, and a database connected by a system bus. Wherein the processor is configured 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, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used to store data of computer program calls. The communication interface of the computer device is used for data communication with an external terminal. The input device of the computer device is used for receiving signals input by external equipment. The computer program is executed by a processor to implement a method of visual measurement of material strain as described in the above embodiments.
It will be appreciated by those skilled in the art that the architecture shown in fig. 4 is merely a block diagram of a portion of the architecture in connection with the present inventive arrangements and is not intended to limit the computer devices to which the present inventive arrangements are applicable.
In addition, the application also provides a visual strain gauge, which comprises a visual sensor and the computer equipment according to the embodiment, wherein the visual sensor is in communication connection with the computer equipment.
Furthermore, the application proposes a computer-readable storage medium comprising a computer program which, when executed by a processor, implements the steps of the visual measurement method of material strain as described in the above embodiments. It is understood that the computer readable storage medium in this embodiment may be a volatile readable storage medium or a nonvolatile readable storage medium.
In summary, in the visual measurement method, the visual measurement device, the computer equipment, the visual strain gauge and the computer readable storage medium for the material strain provided by the embodiment of the application, not only can strain data of the measured material be accurately obtained, but also the implementation condition of the scheme is not affected by temperature, and the measurement can be performed under various temperature environments, which provides greater flexibility and reliability, so that the scheme can be normally implemented under different temperature environment conditions; and the scheme is not limited to the shape and the size of the measured material, and can be applied to strain calculation as long as the preset pattern can be covered and captured by the vision sensor, so that the scheme has wide applicability and can be applied to multiple fields and material types.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium provided by the present application and used in embodiments may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual speed data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, apparatus, article or method that comprises the element.
The foregoing description is only of the preferred embodiments of the present application and is not intended to limit the scope of the application, and all equivalent structures or equivalent processes using the descriptions and drawings of the present application or direct or indirect application in other related technical fields are included in the scope of the present application.
Claims (10)
1. A method for visually measuring strain in a material, comprising:
when a preset pattern is detected in the picture acquisition range of the vision sensor, taking a material provided with the preset pattern as a measured material, wherein the preset pattern is printed on the surface of the measured material in advance;
controlling the vision sensor to acquire a series of pictures of the measured material in the strain process;
identifying the display area of the preset pattern in the picture as a characteristic area, and extracting a series of characteristic area patterns from a series of pictures;
and identifying the displacement amount of the characteristic points in a series of characteristic region patterns, and determining the strain data of the measured material based on the displacement amount.
2. The method of visual measurement of material strain according to claim 1, wherein the step of identifying the developed area of the predetermined pattern in the picture as a feature area comprises:
generating a plurality of candidate areas in the picture based on an Anchor-based detection algorithm, carrying out position regression and category identification on the plurality of candidate areas to identify the display areas of the preset patterns from the plurality of candidate areas, and taking the identified display areas as characteristic areas.
3. The method for visual measurement of material strain according to claim 1 or 2, wherein the step of identifying the amount of displacement of the feature point in the series of feature region patterns comprises:
and identifying characteristic points in a series of characteristic region patterns, and calculating characteristic point identification results by utilizing a sub-pixel displacement measurement algorithm to obtain corresponding displacement.
4. The method for visually measuring strain of a material according to claim 3, wherein before the step of calculating the feature point recognition result by using a sub-pixel displacement measurement algorithm to obtain the corresponding displacement, the method further comprises:
identifying a strain scene represented by the preset pattern, wherein the preset pattern is an identification code pattern, and the identification code pattern is associated with a corresponding strain scene;
and invoking a sub-pixel displacement measurement algorithm corresponding to the strain scene.
5. The visual measurement method of material strain according to claim 1 or 2, further comprising:
if a plurality of characteristic regions are identified in the same picture, corresponding strain data is calculated for each characteristic region.
6. The method of visual measurement of strain in a material according to claim 1, wherein the step of identifying the displacement of the feature point in a series of feature area patterns and determining strain data for the measured material based on the displacement further comprises:
and outputting the strain data to an interaction interface and/or generating a corresponding report file.
7. A vision measurement device, comprising:
the detection module is used for taking a material provided with a preset pattern as a detected material when the preset pattern is detected in the picture acquisition range of the visual sensor, wherein the preset pattern is printed on the surface of the detected material in advance;
the acquisition module is used for controlling the vision sensor to acquire a series of pictures of the measured material in the strain process;
the identification module is used for identifying the display area of the preset pattern in the picture as a characteristic area and extracting a series of characteristic area patterns from a series of pictures;
and the calculation module is used for identifying the displacement amount of the characteristic points in a series of characteristic region patterns and determining the strain data of the measured material based on the displacement amount.
8. A computer device, characterized in that it comprises a memory, a processor and a computer program stored on the memory and executable on the processor, which computer program, when being executed by the processor, implements the steps of the visual measuring method of material strain according to any one of claims 1 to 6.
9. A visual strain gauge comprising a visual sensor and a computer device as recited in claim 8, wherein the visual sensor is communicatively coupled to the computer device.
10. A computer-readable storage medium, characterized in that it has stored thereon a computer program which, when executed by a processor, implements the steps of the visual measurement method of material strain according to any of claims 1 to 6.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5726907A (en) * | 1995-07-31 | 1998-03-10 | Southwest Research Institute | Biaxial non-contacting strain measurement using machine vision |
| CN105651198A (en) * | 2016-01-14 | 2016-06-08 | 清华大学 | Stress monitoring method and stress monitoring device |
| CN109883333A (en) * | 2019-03-14 | 2019-06-14 | 武汉理工大学 | A kind of non-contact displacement strain measurement method based on characteristics of image identification technology |
| CN111256607A (en) * | 2020-02-19 | 2020-06-09 | 北京林业大学 | Deformation measurement method based on three-channel mark points |
| CN114689644A (en) * | 2022-03-29 | 2022-07-01 | 清华大学 | High-temperature environment parameter measuring method and device |
| CN115031650A (en) * | 2022-05-24 | 2022-09-09 | 深圳市海塞姆科技有限公司 | Relative deformation measuring method and system based on mark point combination |
| CN115143895A (en) * | 2022-06-21 | 2022-10-04 | 深圳市海塞姆科技有限公司 | Deformation vision measurement method, device, equipment, medium and double-shaft measurement extensometer |
-
2023
- 2023-09-26 CN CN202311247528.4A patent/CN116989691B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5726907A (en) * | 1995-07-31 | 1998-03-10 | Southwest Research Institute | Biaxial non-contacting strain measurement using machine vision |
| CN105651198A (en) * | 2016-01-14 | 2016-06-08 | 清华大学 | Stress monitoring method and stress monitoring device |
| CN109883333A (en) * | 2019-03-14 | 2019-06-14 | 武汉理工大学 | A kind of non-contact displacement strain measurement method based on characteristics of image identification technology |
| CN111256607A (en) * | 2020-02-19 | 2020-06-09 | 北京林业大学 | Deformation measurement method based on three-channel mark points |
| CN114689644A (en) * | 2022-03-29 | 2022-07-01 | 清华大学 | High-temperature environment parameter measuring method and device |
| CN115031650A (en) * | 2022-05-24 | 2022-09-09 | 深圳市海塞姆科技有限公司 | Relative deformation measuring method and system based on mark point combination |
| CN115143895A (en) * | 2022-06-21 | 2022-10-04 | 深圳市海塞姆科技有限公司 | Deformation vision measurement method, device, equipment, medium and double-shaft measurement extensometer |
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