A high-precision dimensional measurement method combining image recognition and parameter calibration

Abstract

The present invention relates to the field of image recognition technology and discloses a high-precision dimension measurement method combining image recognition with parameter calibration. The method comprises the following steps: collecting scanned image data of a flame arrester in its initial operating state and initial non-operating state; collecting scanned image data of the flame arrester in its current state; inputting the scanned image data of the flame arrester in its initial operating state and the scanned image data of the flame arrester in its current state into a dimension measurement model, and outputting a measurement result of the corrugation spacing of the flame arrester. The method uses deep learning technology to separate and calibrate image features, and is capable of recognizing ultrasonic scanning image data in an online state to accurately measure the corrugation spacing.

Description

Technical Field

[0001] The present invention relates to the field of image recognition technology, and more particularly to a high-precision dimension measurement method combining image recognition with parameter calibration. Background Art

[0002] Regarding image recognition technology for flame arrester measurement, conventional visible light image recognition technology only requires the flame arrester to be visible and is only applicable to flame arrester measurement when not installed. Although ultrasonic image recognition technology can be applied to flame arrester measurement when not installed, the flow of the medium in the pipeline under online conditions will cause significant interference to the ultrasonic wave, resulting in distortion and loss of the image feature representation of the flame arrester corrugation spacing. Therefore, it is generally only applicable to flame arrester measurement under offline conditions. Summary of the Invention

[0003] The present invention provides a high-precision dimension measurement method combining image recognition and parameter calibration, which solves the technical problems in related technologies.

[0004] The present invention provides a high-precision dimension measurement method combining image recognition and parameter calibration, comprising the following steps:

[0005] Step 1, collecting scanning image data of the flame arrester in the initial operating state and the initial non-operating state;

[0006] The initial operating state of the flame arrester refers to the state in which the flame arrester is in normal use and the flame arrester is in the state of conveying medium in the pipeline;

[0007] This state is generally when the flame arrester is first installed in the pipeline. At this time, the pipeline is offline. In the absence of medium, good three-dimensional reconstruction results can be obtained through ultrasonic detection scanning. Then, when the medium is passed through the pipeline, it is the initial operating state of the flame arrester.

[0008] The initial non-operating state means that the flame arrester can be ensured to be in normal use state and the flame arrester is not transporting medium in the pipeline;

[0009] Step 2, collecting the scanned image data of the flame arrester in its current state;

[0010] The current state of the flame arrester means that the state of the flame arrester is unknown and the pipeline is online.

[0011] Step 3: Input the scanned image data of the flame arrester in the initial operating state and the scanned image data of the flame arrester in the current state into a dimension measurement model. The dimension measurement model includes at least a first image recognition module for identifying the scanned image data of the flame arrester in the initial operating state to obtain a first image feature, a second image recognition module for identifying the scanned image data of the flame arrester in the initial non-operating state to obtain a second image feature, and a third image recognition module for identifying the scanned image data of the flame arrester in the current state to obtain a third image feature.

[0012] The dimensional measurement model further includes a medium feature separation module for identifying the medium and the image feature generated by the motion of the medium from the first image feature and the third image feature;

[0013] The size measurement model further includes a parameter calibration module for removing the image features generated by the medium separated by the medium feature separation module and the medium motion from the third image features;

[0014] The size measurement model also includes a measurement module, which is used to input the result of the parameter calibration module processing the third image feature and output the measurement result of the flame arrester corrugation spacing.

[0015] Furthermore, each time it is determined that the flame arrester is in a normal use state, the initial operating state of the flame arrester is updated.

[0016] Further, the measurement result of the corrugation spacing of the flame arrester includes the measurement results of all corrugation spacings of the flame arrester.

[0017] Furthermore, the first image recognition module, the second image recognition module, and the third image recognition module may all adopt convolutional neural networks.

[0018] Furthermore, the scan image data in the initial operating state, the initial non-operating state, and the current state are all collected within a set time period according to a set cycle, so the collected scan image data is a sequence data.

[0019] Furthermore, the first image features are encoded according to a time sequence of acquisition of the corresponding scanned images to obtain a first time sequence, wherein one sequence unit of the first time sequence corresponds to one first image feature;

[0020] Encoding the second image features according to the time sequence of acquisition of the corresponding scanned images to obtain a second time series, wherein one sequence unit of the second time series corresponds to one second image feature;

[0021] The third image features are encoded according to the time sequence of acquisition of the corresponding scanned images to obtain a third time series, where one sequence unit of the third time series corresponds to one third image feature.

[0022] Furthermore, the medium feature separation module includes two parallel sequence layers, which respectively input the first time series and the second time series. The two sequence layers respectively output update states to the separation layer, and the separation layer outputs the medium and image features generated by the medium movement.

[0023] Furthermore, the parameter calibration module includes a timing layer and a calibration layer, wherein the timing layer inputs the third time series and outputs deep features to the calibration layer, and the calibration layer outputs the image features generated by removing the medium separated by the medium feature separation module and the medium movement from the third image features.

[0024] Furthermore, the measurement module includes a fully connected layer for outputting per-unit values ​​of all corrugation spacings of the flame arrester.

[0025] The present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps in each of the above-mentioned high-precision dimension measurement method embodiments combining image recognition and parameter calibration are implemented.

[0026] The beneficial effects of the present invention are that the present invention separates and calibrates image features through deep learning technology, and can identify ultrasonic scanning image data in an online state to perform accurate corrugation spacing measurement. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is a flow chart of a high-precision dimension measurement method combining image recognition and parameter calibration according to the present invention;

[0028] Figure 2 is an example of the corrugation spacing of the flame arrester of the present invention;

[0029] Figure 3 It is a module schematic diagram of the computer device of the present invention. DETAILED DESCRIPTION

[0030] The subject matter described herein will now be discussed with reference to example embodiments. It should be understood that these embodiments are discussed solely to enable those skilled in the art to better understand and implement the subject matter described herein, and that the functions and arrangements of the elements discussed may be varied without departing from the scope of this specification. Various examples may omit, substitute, or add various processes or components as needed. Furthermore, features described in some examples may be combined in other examples.

[0031] At least one embodiment of the present invention discloses a high-precision dimension measurement method combining image recognition and parameter calibration, such as Figure 1 As shown, the following steps are included:

[0032] Step 1, collecting scanning image data of the flame arrester in the initial operating state and the initial non-operating state;

[0033] The initial operating state of the flame arrester refers to the state in which the flame arrester is in normal use and the flame arrester is in the state of conveying medium in the pipeline;

[0034] This state is generally when the flame arrester is first installed in the pipeline. At this time, the pipeline is offline. In the absence of medium, good three-dimensional reconstruction results can be obtained through ultrasonic detection scanning. Then, when the medium is passed through the pipeline, it is the initial operating state of the flame arrester.

[0035] The initial non-operating state means that the flame arrester can be ensured to be in a normal use state and the flame arrester is not transporting media in the pipeline;

[0036] Of course, it does not rule out the possibility of the flame arrester's initial operating state in other situations. For example, when the system where the pipeline is located is under maintenance or stops operating, the pipeline is also in a medium-free state. The flame arrester is tested to be intact, and then the medium is re-transmitted in the pipeline, which is the flame arrester's initial operating state.

[0037] The aforementioned medium and pipeline are superordinate terms. The pipeline includes all pipelines that can be used for flame arresters, and the medium is the medium that can be transported by the aforementioned pipeline. The medium is generally liquid hydrocarbons, LNG, etc.

[0038] Step 2, collecting the scanned image data of the flame arrester in its current state;

[0039] The current status of the flame arrester does not conflict with the initial operating status of the flame arrester. The current status of the flame arrester means that the flame arrester is in an unknown state and the pipeline is in an online state. Therefore, it may also include the initial operating status of the flame arrester.

[0040] Therefore, each time the flame arrester is judged to be in normal use, the initial operating state of the flame arrester can be updated so that the initial operating state of the flame arrester compared with the next detection of the current state of the flame arrester is closer;

[0041] Step 3: Input the scanned image data of the flame arrester in the initial operating state and the scanned image data of the flame arrester in the current state into a dimension measurement model. The dimension measurement model includes at least a first image recognition module for identifying the scanned image data of the flame arrester in the initial operating state to obtain a first image feature, a second image recognition module for identifying the scanned image data of the flame arrester in the initial non-operating state to obtain a second image feature, and a third image recognition module for identifying the scanned image data of the flame arrester in the current state to obtain a third image feature.

[0042] The dimensional measurement model further includes a medium feature separation module for identifying the medium and the image feature generated by the motion of the medium from the first image feature and the third image feature;

[0043] The size measurement model further includes a parameter calibration module for removing the image features generated by the medium separated by the medium feature separation module and the medium motion from the third image features;

[0044] The size measurement model also includes a measurement module, which is used to input the result of the parameter calibration module processing the third image feature and output the measurement result of the flame arrester corrugation spacing.

[0045] The measurement results of the flame arrester corrugation spacing include the measurement results of all corrugation spacings of the flame arrester, such as Figure 2 The oval mark shown is one corrugation distance of the flame arrester.

[0046] In some embodiments of the present invention, the first image recognition module, the second image recognition module, and the third image recognition module may all adopt a convolutional neural network (CNN).

[0047] In some embodiments of the present invention, the scan image data in the initial operating state, the initial non-operating state, and the current state are all collected within a set time period according to a set cycle, so the collected scan image data is a sequence data;

[0048] Encoding the first image features according to the time sequence of acquisition of the corresponding scanned images to obtain a first time sequence, where one sequence unit of the first time sequence corresponds to one first image feature;

[0049] Encoding the second image features according to the time sequence of acquisition of the corresponding scanned images to obtain a second time series, wherein one sequence unit of the second time series corresponds to one second image feature;

[0050] Encoding the third image features according to the time sequence of acquisition of the corresponding scanned images to obtain a third time series, where one sequence unit of the third time series corresponds to one third image feature;

[0051] In one embodiment of the present invention, the medium feature separation module includes two parallel sequence layers, which respectively input a first time series and a second time series. The two sequence layers respectively output updated states to the separation layer, and the separation layer outputs the medium and image features generated by the medium movement.

[0052] The expression of the sequence layer is as follows:

[0053] Forget Gate:

[0054]

[0055] Input Gate:

[0056]

[0057] Candidate unit status:

[0058]

[0059] Unit status update:

[0060]

[0061] Output Gate:

[0062]

[0063] Status Update:

[0064]

[0065] : Activation vectors of the forget, input, and output gates.

[0066] : The state of the t-th unit.

[0067] : The state of the t-1th unit.

[0068] : The state of the t-th candidate unit;

[0069] : The tth updated state;

[0070] : The t-1th updated state;

[0071] and , where n represents the total number of sequence units contained in the first time series or the second time series, Represents a set of positive integers, when t=1 ; Represents the t-th sequence unit of the first time series or the second time series;

[0072] : A, B, C, D, E, F, G, H weight matrices.

[0073] : First, second, third, fourth bias vector.

[0074] : Sigmoid activation function.

[0075] : Optional non-linear activation function (e.g. tanh).

[0076] : dot product.

[0077] The expression of the separation layer is as follows:

[0078]

[0079] in Represents the image characteristics of the medium and the movement of the medium, and Respectively represent and Weight matrix; and Represents the nth updated state of the output of the two sequence layers respectively;

[0080] In one embodiment of the present invention, the parameter calibration module includes a timing layer and a calibration layer, wherein the timing layer inputs the third time series and outputs deep features to the calibration layer, and the calibration layer outputs the image features generated by the medium and the medium motion separated by the medium feature separation module from the third image features;

[0081] The expression of the timing layer is as follows:

[0082]

[0083] in represents the t-th deep feature, represents the t-1th deep feature, represents the t-th sequence unit of the third time series, and are the first and second sequence weight parameters, is the sequence bias parameter, tanh is the hyperbolic tangent function;

[0084] The expression of the calibration layer is as follows:

[0085]

[0086] in Indicates the image features generated by the medium and the medium movement after the medium feature separation module is removed from the third image feature. and Respectively represent and Weight matrix; Represents the nth deep feature output by the temporal layer;

[0087] The measurement module includes a fully connected layer, and the expression of the fully connected layer is as follows:

[0088]

[0089] in represents the weight parameter of the fully connected layer, represents the bias parameter of the fully connected layer, Indicates the per-unit value of the measured value of the i-th corrugation spacing.

[0090] In one embodiment of the present invention, the fully connected layer includes multiple layers, which are output in parallel, and each layer outputs a per-unit value of the measurement value of the ripple spacing.

[0091] In one embodiment of the present invention, the fully connected layer outputs serially, and the i-th time step outputs the per-unit value of the measurement value of the i-th ripple spacing.

[0092] The above describes an embodiment of the present invention, but this embodiment is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Ordinary technicians in this field can also make more forms of equivalent embodiments based on the inspiration of this embodiment, all of which are protected by this embodiment.

[0093] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 3 As shown. The computer device includes a processor, memory, an input / output (I / O) interface, and a communication interface. The processor, memory, and I / O interface are connected via a system bus, and the communication interface is connected to the system bus via the I / O interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store data related to celestial body information. The I / O interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals via a network connection. When executed by the processor, the computer program implements a high-precision dimensional measurement method that combines image recognition and parameter calibration. The display unit of the computer device is used to form a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device casing, or an external keyboard, touchpad or mouse.

[0094] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0095] In one embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, which, when executed by the processor, implements the steps in each of the above-mentioned high-precision dimension measurement method embodiments combining image recognition and parameter calibration.

[0096] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in each of the above-mentioned high-precision dimension measurement method embodiments combining image recognition and parameter calibration are implemented.

[0097] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processors (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), data processing logic devices based on quantum computing, and the like.

Claims

1. A high-precision dimension measurement method combining image recognition and parameter calibration, characterized in that: The following steps are involved: Collecting scanning image data of the flame arrester in an initial operating state and an initial non-operating state; Collecting scanned image data of the flame arrester in its current state; Inputting scanned image data of the flame arrester in an initial operating state and scanned image data of the flame arrester in a current state into a dimension measurement model, the dimension measurement model comprising at least a first image recognition module for identifying the scanned image data of the flame arrester in the initial operating state to obtain a first image feature, a second image recognition module for identifying the scanned image data of the flame arrester in an initial non-operating state to obtain a second image feature, and a third image recognition module for identifying the scanned image data of the flame arrester in the current state to obtain a third image feature; The dimensional measurement model further includes a medium feature separation module for identifying the medium and the image feature generated by the motion of the medium from the first image feature and the third image feature; The size measurement model further includes a parameter calibration module for removing the image features generated by the medium separated by the medium feature separation module and the medium motion from the third image features; The size measurement model also includes a measurement module, which is used to input the result of the parameter calibration module processing the third image feature and output the measurement result of the flame arrester corrugation spacing.

2. The high-precision dimension measurement method combining image recognition and parameter calibration according to claim 1, characterized in that: After each determination that the flame arrester is in a normal use state, the initial operating state of the flame arrester is updated.

3. The high-precision dimension measurement method combining image recognition and parameter calibration according to claim 1, characterized in that: The measurement results of the flame arrester corrugation spacing include the measurement results of all corrugation spacings of the flame arrester.

4. The high-precision dimension measurement method combining image recognition and parameter calibration according to claim 1, characterized in that: The first image recognition module, the second image recognition module, and the third image recognition module all use convolutional neural networks.

5. The high-precision dimension measurement method combining image recognition and parameter calibration according to claim 1, characterized in that: The scan image data in the initial operating state, the initial non-operating state, and the current state are all collected in a set time period according to a set cycle, so the collected scan image data is a sequence data.

6. The high-precision dimension measurement method combining image recognition and parameter calibration according to claim 5, characterized in that: Encoding the first image features according to the time sequence of acquisition of the corresponding scanned images to obtain a first time sequence, where one sequence unit of the first time sequence corresponds to one first image feature; Encoding the second image features according to the time sequence of acquisition of the corresponding scanned images to obtain a second time series, wherein one sequence unit of the second time series corresponds to one second image feature; The third image features are encoded according to the time sequence of acquisition of the corresponding scanned images to obtain a third time series, where one sequence unit of the third time series corresponds to one third image feature.

7. The high-precision dimension measurement method combining image recognition and parameter calibration according to claim 6, characterized in that: The medium feature separation module includes two parallel sequence layers, which input the first time series and the second time series respectively. The two sequence layers output the updated status to the separation layer respectively, and the separation layer outputs the medium and the image features generated by the medium movement.

8. The high-precision dimension measurement method combining image recognition and parameter calibration according to claim 6, characterized in that: The parameter calibration module includes a timing layer and a calibration layer, wherein the timing layer inputs the third time series and outputs deep features to the calibration layer, and the calibration layer outputs the image features generated by removing the medium separated by the medium feature separation module and the medium movement from the third image features.

9. The high-precision dimension measurement method combining image recognition and parameter calibration according to claim 8, characterized in that: The measurement module includes a fully connected layer to output the per-unit values ​​of all corrugation spacings of the flame arrester.

10. A computer-readable storage medium, characterized in that A computer program is stored thereon, and when the computer program is executed by a processor, the steps of a high-precision dimension measurement method combining image recognition and parameter calibration as described in any one of claims 1 to 9 are implemented.

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