CN115791830A - Steel plate detection system, steel plate detection method and electronic equipment - Google Patents
Steel plate detection system, steel plate detection method and electronic equipment Download PDFInfo
- Publication number
- CN115791830A CN115791830A CN202211564477.3A CN202211564477A CN115791830A CN 115791830 A CN115791830 A CN 115791830A CN 202211564477 A CN202211564477 A CN 202211564477A CN 115791830 A CN115791830 A CN 115791830A
- Authority
- CN
- China
- Prior art keywords
- steel plate
- measured
- thickness
- camera
- data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Landscapes
- Length Measuring Devices By Optical Means (AREA)
Abstract
The invention provides a steel plate detection system, a steel plate detection method and electronic equipment, and belongs to the technical field of vision measurement. The system comprises an image acquisition device, camera calibration software and data analysis software. The device comprises an image acquisition device used for acquiring image data of the upper surface and the lower surface of the measured steel plate, camera calibration software connected with the image acquisition device and used for carrying out data preprocessing on the acquired image data, and data analysis software connected with the camera calibration software and used for calculating the thickness standard deviation of the measured steel plate according to the preprocessed image data and evaluating the thickness uniformity of the measured steel plate according to the thickness standard deviation. The problems that manual visual steel plate detection in the related technology is high in labor intensity, prone to causing omission, incapable of adapting to the production environment of a high-speed unit, low in detection precision and the like are solved at least, and the method is suitable for scenes of visual measurement and steel plate detection.
Description
Technical Field
The invention relates to the technical field of visual measurement, in particular to a steel plate detection system, a steel plate detection method and electronic equipment.
Background
The steel plate surface detection is mainly used for detecting discontinuity defects (such as pits, scratches and the like) on the steel plate surface, and the detection method comprises an artificial experience detection method and a nondestructive detection technology based on electromagnetic induction and ultrasound.
At present, a manual visual method is mainly used for detecting the large steel plate and is limited by the requirement of production takt, and the bottom surface of the steel plate cannot be detected by the manual visual method, so that the upper surface and the lower surface of the detected steel plate cannot be completely covered; and the manual visual detection method can not carry out numerical measurement on the discontinuity of the surface of the steel plate, and needs to add working procedures and carry out steel plate grading operation after measuring the numerical value by using a special instrument, so that the detection of the current large-sized steel plate has the problems of high manual labor intensity, easy detection omission, incapability of adapting to the production environment of a high-speed unit, low detection precision and the like.
Disclosure of Invention
The invention aims to solve the technical problems of the prior art, and provides a steel plate detection system, a steel plate detection method and electronic equipment, so as to at least solve the problems of high labor intensity, easy detection omission, incapability of adapting to the production environment of a high-speed unit, low detection precision and the like in the manual visual steel plate detection in the related art.
In a first aspect, the present invention provides a steel plate detection system, which includes an image acquisition device, camera calibration software, and data analysis software.
And the image acquisition device is used for acquiring the image data of the upper surface and the lower surface of the measured steel plate. And the camera calibration software is connected with the image acquisition device and is used for carrying out data preprocessing on the acquired image data. And the data analysis software is connected with the camera calibration software and used for calculating the thickness standard deviation of the measured steel plate according to the preprocessed image data and evaluating the thickness uniformity of the measured steel plate according to the thickness standard deviation.
Preferably, the image capturing apparatus includes an array camera and a capturing signal controller. And the array camera is connected with the acquisition signal controller and used for acquiring image data of the upper surface and the lower surface of the measured steel plate according to the acquisition signal sent by the acquisition signal controller and outputting a first coordinate of a sampling point, wherein the first coordinate is in a coordinate system taking the position of the array camera as an origin. The acquisition signal controller is provided with an acquisition signal period and is used for sending acquisition signals to the array camera according to the acquisition signal period, wherein the acquisition signal period is the ratio of the steel plate stepping length to the steel plate transmission rate.
Preferably, the array camera comprises two sets of linear scanning cameras. And the two groups of linear scanning cameras are used for being fixedly arranged at the upper part and the lower part of the detection gate respectively and keeping a preset shooting distance between the two groups of linear scanning cameras and the corresponding surfaces of the detected steel plate respectively, wherein the plurality of linear scanning cameras in the group cover the wide edge of the detected steel plate together in a device cascading mode.
Preferably, the image acquisition device further comprises a speed controller and a rolling machine. And the speed controller is connected with the rolling machine and is provided with a steel plate transmission speed for controlling the rotating speed of the rolling machine according to the steel plate transmission speed. And the rolling machine is used for placing the steel plate to be measured and conveying the steel plate to be measured through rotation.
Preferably, the camera calibration software comprises a coordinate conversion module. The coordinate conversion module is used for converting first coordinates of sampling points output by the cameras into a world coordinate system according to the relative position relation of the cameras in the array cameras to obtain converted acquisition data, wherein the origin O of the world coordinate system is one corner of a measured steel plate, the X axis of the world coordinate system is parallel to the array cameras formed by the cameras, the Y axis of the world coordinate system is parallel to the transmission direction of the measured steel plate, the Z axis of the world coordinate system is vertical to an XOY plane, and the converted acquisition data are as follows:
wherein p = u represents the upper surface of the measured steel plate, p = d represents the lower surface of the measured steel plate, t represents the sampling time, i = j · N + k, j is the camera index, and i takes a value of 0,1,2 M Represents the total number of sampling points, N, of any surface of the steel plate to be tested at each sampling moment M M represents the number of sampling points of the last camera for shooting any surface of the measured steel plate.
Preferably, the data analysis software comprises a first calculation module and a second calculation module. The first calculation module is used for calculating the thickness of the steel plate at each sampling point of the measured steel plate according to the following formula:
wherein the content of the first and second substances,
for collecting data of the upper surface of the measured steel plate,
and collecting data of the lower surface of the measured steel plate. The second calculation module is connected with the first calculation module and used for calculating the standard deviation of the thickness of the measured steel plate according to the thickness of the steel plate and the following formula:
wherein the content of the first and second substances,
the average thickness of the steel sheet to be measured is shown.
Preferably, the data analysis software further comprises an evaluation module. And the evaluation module is connected with the second calculation module and used for evaluating the thickness uniformity of the measured steel plate in response to the thickness standard deviation being smaller than the preset threshold value and evaluating the thickness uniformity of the measured steel plate in response to the thickness standard deviation being larger than or equal to the preset threshold value.
In a second aspect, the present invention further provides a steel plate detection method, including: acquiring image data of the upper surface and the lower surface of a measured steel plate; carrying out data preprocessing on the acquired image data; and calculating the thickness standard deviation of the steel plate to be measured according to the preprocessed image data, and evaluating the thickness uniformity of the steel plate to be measured according to the thickness standard deviation.
Preferably, the acquiring image data of the upper surface and the lower surface of the measured steel plate specifically includes: and acquiring image data of the upper surface and the lower surface of the measured steel plate by adopting an array camera according to an acquisition signal period, and outputting a first coordinate of a sampling point, wherein the first coordinate is in a coordinate system taking the position of the array camera as an origin. The array camera comprises two groups of linear scanning cameras, the two groups of linear scanning cameras respectively collect image data of the upper surface and the lower surface of the measured steel plate, and a plurality of linear scanning cameras in the group cover the wide edge of the measured steel plate together in an equipment cascading mode.
Preferably, the data preprocessing is performed on the acquired image data, and specifically includes: converting the first coordinate of the sampling point output by each camera into a world coordinate system according to the relative position relation of each camera in the array cameras to obtain converted acquisition data, wherein the origin O of the world coordinate system is one corner of the measured steel plate, the X axis of the world coordinate system is parallel to the array cameras formed by each camera, the Y axis of the world coordinate system is parallel to the transmission direction of the measured steel plate, the Z axis of the world coordinate system is vertical to the XOY plane, and the converted acquisition data are as follows:
wherein p = u represents the upper surface of the measured steel plate, p = d represents the lower surface of the measured steel plate, t represents the sampling time, i = j · N + k, j is the camera index, and i takes a value of 0,1,2 M Representing the total number of sampling points, N, of any surface of the steel plate to be tested at each sampling moment M M represents the number of sampling points of the last camera for shooting any surface of the steel plate to be measured.
Preferably, calculating the standard deviation of the thickness of the measured steel plate according to the preprocessed image data specifically comprises: calculating the thickness of the steel plate at each sampling point of the measured steel plate according to the following formula:
wherein, the first and the second end of the pipe are connected with each other,
data is collected for the upper surface of the steel plate to be measured,
calculating the thickness standard deviation of the measured steel plate according to the thickness of the steel plate and the following formula for the collected data of the lower surface of the measured steel plate:
wherein the content of the first and second substances,
the average thickness of the steel sheets measured is indicated.
Preferably, the method for evaluating the thickness uniformity of the measured steel plate according to the thickness standard deviation specifically comprises the following steps: evaluating the thickness uniformity of the measured steel plate in response to the fact that the thickness standard deviation is smaller than a preset threshold value; and evaluating the thickness unevenness of the measured steel plate in response to the thickness standard deviation being greater than or equal to a preset threshold value.
In a third aspect, the present invention further provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to implement the steel plate detection method according to the second aspect.
The invention provides a steel plate detection system, a steel plate detection method and electronic equipment. Compared with a manual visual method, the detection system for detecting the flatness and flaws of the steel plate in industrial production based on machine vision has the characteristics of higher automation degree, comprehensive detection, suitability for the production environment of a high-speed unit and high detection precision.
Drawings
Fig. 1 is a schematic structural diagram of a steel plate detection system according to embodiment 1 of the present invention;
fig. 2 is a schematic view of a camera coverage area in embodiment 1 of the present invention;
FIG. 3 is a schematic view of a world coordinate system according to embodiment 1 of the present invention;
FIG. 4 is a schematic flow chart of a steel plate inspection method according to embodiment 2 of the present invention;
fig. 5 is a schematic structural diagram of an electronic device according to embodiment 3 of the present invention.
Detailed Description
In order to make those skilled in the art better understand the technical solutions of the present invention, the following detailed description will be made with reference to the accompanying drawings.
It is to be understood that the specific embodiments and figures described herein are merely illustrative of the invention and are not limiting of the invention.
It is to be understood that the embodiments and features of the embodiments can be combined with each other without conflict.
It is to be understood that, for the convenience of description, only parts related to the present invention are shown in the drawings of the present invention, and parts not related to the present invention are not shown in the drawings.
It should be understood that each unit and module related in the embodiments of the present invention may correspond to only one physical structure, may also be composed of multiple physical structures, or multiple units and modules may also be integrated into one physical structure.
It will be understood that, without conflict, the functions, steps, etc. noted in the flowchart and block diagrams of the present invention may occur in an order different from that noted in the figures.
It is to be understood that the flowchart and block diagrams of the present invention illustrate the architecture, functionality, and operation of possible implementations of systems, apparatus, devices and methods according to various embodiments of the present invention. Each block in the flowchart or block diagrams may represent a unit, module, segment, code, which comprises executable instructions for implementing the specified function(s). Furthermore, each block or combination of blocks in the block diagrams and flowchart illustrations can be implemented by hardware-based systems that perform the specified functions or by a combination of hardware and computer instructions.
It is to be understood that the units and modules involved in the embodiments of the present invention may be implemented by software, and may also be implemented by hardware, for example, the units and modules may be located in a processor.
Example 1:
as shown in fig. 1, the embodiment provides a steel plate detection system, which can be applied to a steel plate production scenario of a high-speed unit or other steel plate detection scenarios. The steel plate detection system comprises an image acquisition device 11, camera calibration software 12 and data analysis software 13.
And the image acquisition device 11 is used for acquiring the image data of the upper surface and the lower surface of the measured steel plate.
And the camera calibration software 12 is connected with the image acquisition device 11 and is used for performing data preprocessing on the acquired image data.
And the data analysis software 13 is connected with the camera calibration software 12 and used for calculating the thickness standard deviation of the measured steel plate according to the preprocessed image data and evaluating the thickness uniformity of the measured steel plate according to the thickness standard deviation.
Optionally, the image acquisition device comprises an array camera and an acquisition signal controller. And the array camera is connected with the acquisition signal controller and is used for acquiring image data of the upper surface and the lower surface of the measured steel plate according to the acquisition signal sent by the acquisition signal controller and outputting a first coordinate of the sampling point, wherein the first coordinate is in a coordinate system taking the position of the array camera as an origin. The acquisition signal controller is internally provided with an acquisition signal period, and is used for sending acquisition signals to the array camera according to the acquisition signal period, wherein the acquisition signal period is the ratio of the steel plate stepping length L (unit: meter) to the steel plate transmission rate S (unit: meter/second), namely the time interval of the acquisition signal controller for sending the acquisition signals is as follows: τ = LS (unit: second), that is, the array camera captures and acquires data once every interval τ.
In particular, the array camera includes two sets of linear scanning cameras. And the two groups of linear scanning cameras are used for fixedly arranging on the upper part and the lower part of the detection gate respectively and keeping a preset shooting distance between the two parts and the corresponding surfaces of the detected steel plate respectively, wherein the plurality of linear scanning cameras in the group cover the broadsides of the detected steel plate together in a device cascading mode.
In this embodiment, as shown in fig. 1, a set of three-dimensional (3D) linear scanning cameras is respectively installed on the upper and lower portions of the detection gate (i.e., above and below the steel plate to be detected) to collect image data, and the upper (or lower) cameras are cascaded through a device to cover the entire width of the steel plate to be detected. As shown in fig. 2, each camera covers a straight line range with a certain length, and 6 cameras jointly cover the broadside of the steel plate to be measured in a device cascade mode. The number of the cameras is determined by the width of the steel plate (or other object to be measured) to be measured and the scanning range of the cameras (for example, when the acquisition coverage width of the image data of each 3D camera is 0.45 meter, and the width of the steel plate to be measured is 4.2 meters, a total of 18 to 20 3D cameras are required to cover the width of the upper and lower surfaces of the whole steel plate to be measured in a device cascade manner). When the resolutions of the cascaded cameras are different, according to the different resolutions of the cameras, each camera obtains a group of coordinates of pixel points of the measured steel plate in the coverage range, and outputs a first coordinate of the measured steel plate as a coordinate system based on the position of each camera as an origin, and the first coordinate mark is as follows:
p = u or p = d, u represents a camera above the upper surface of the steel plate to be measured, d represents a camera below the lower surface of the steel plate to be measured, T =0,1,2,. T represents a sampling time, j =0,1,2,. M represents a camera index ID, k =0,1,2,. N represents an index ID of each camera acquiring N sampling points at a time, all the cameras are uniformly controlled by the acquisition signal controller, and when the acquisition signal controller sends out an acquisition signal, all the cameras simultaneously start to shoot and acquire image data of the steel plate to be measured.
Optionally, the image capturing device further comprises a rate controller and a roller bed. And the speed controller is connected with the rolling machine, and the steel plate transmission speed is arranged in the speed controller and is used for controlling the rotating speed of the rolling machine according to the steel plate transmission speed. And the rolling machine is used for placing the steel plate to be tested and conveying the steel plate to be tested through rotation. In this embodiment, the rate controller and the acquisition signal controller are controlled and cooperated by rate matching software.
The data preprocessing comprises data cleaning, data integration, data transformation and data specification. In this embodiment, a detailed process is described by data conversion.
Specifically, the camera calibration software includes a coordinate conversion module.
And the coordinate conversion module is used for converting the first coordinates of the sampling points output by each camera into a world coordinate system according to the relative position relationship of each camera in the array camera to obtain the converted acquisition data. Preferably, as shown in fig. 3, the origin O of the world coordinate system is a corner of the measured steel plate (a corner of the detected object is a uniform coordinate origin O as shown in fig. 1), the X axis of the world coordinate system is parallel to the array cameras composed of the cameras, the Y axis of the world coordinate system is parallel to the transmission direction of the measured steel plate, the Z axis of the world coordinate system is perpendicular to the XOY plane, and the converted collected data is:
wherein p = u represents the upper surface of the measured steel plate, p = d represents the lower surface of the measured steel plate, t represents the sampling time, i = j · N + k, j is the camera index, i takes a value of 0,1,2 M Representing the total number of sampling points, N, of any surface of the steel plate to be tested at each sampling moment M M represents the number of sampling points of the last camera for shooting any surface of the steel plate to be measured. It should be noted that the origin of the world coordinate system is not limited to a corner of the measured steel plate in the present embodiment. The data collected by each camera is converted into the collected data in the same coordinate system, so that the subsequent calculation of the thickness of the measured steel plate is facilitated, and the accuracy of the calculation result is ensured.
Optionally, the data analysis software comprises a first calculation module and a second calculation module.
The first calculation module is used for calculating the steel plate thickness of each sampling point of the measured steel plate according to the following formula:
wherein, the first and the second end of the pipe are connected with each other,
for collecting data of the upper surface of the measured steel plate,
the data is collected for the lower surface of the steel plate to be measured.
The second calculation module is connected with the first calculation module and used for calculating the thickness standard deviation of the measured steel plate according to the thickness of the steel plate and the following formula:
wherein, the first and the second end of the pipe are connected with each other,
the average thickness of the steel sheet to be measured is shown.
Optionally, the data analysis software further comprises an evaluation module. And the evaluation module is connected with the second calculation module and used for evaluating the thickness uniformity of the measured steel plate in response to the thickness standard deviation being smaller than the preset threshold value and evaluating the thickness uniformity of the measured steel plate in response to the thickness standard deviation being larger than or equal to the preset threshold value. The standard deviation sigma of the steel plate thickness characterizes the uniformity of the detected steel plate thickness, and the smaller the value is, the more uniform the steel plate thickness is. The uniformity of the thickness is measured through the thickness standard deviation, and the evaluation is reasonable and effective.
In the steel plate detection system of the embodiment, the image acquisition devices (such as the array cameras) are used for respectively acquiring the image data of the upper surface and the lower surface of the detected steel plate, the camera calibration software is used for preprocessing the acquired data, and the data analysis software is used for calculating the thickness standard deviation of the steel plate according to the preprocessed data so as to evaluate the thickness uniformity of the steel plate. The steel plate detection system based on the machine vision detection of the flatness and flaws of the steel plate in industrial production has the advantages of being high in automation degree, comprehensive in detection, suitable for production environment of a high-speed unit and high in detection precision compared with a manual visual method. Furthermore, the camera calibration software is used for converting data acquired by each camera into acquired data in the same coordinate system, so that the thickness of the measured steel plate can be conveniently calculated subsequently, and the accuracy of the calculation result can be ensured. In addition, the data analysis software is used for measuring the uniformity of the thickness of the measured steel plate by adopting the thickness standard deviation, and the evaluation is reasonable and effective.
Example 2:
as shown in fig. 4, the present embodiment provides a steel plate detection method, including:
And 403, calculating the thickness standard deviation of the measured steel plate according to the preprocessed image data, and evaluating the thickness uniformity of the measured steel plate according to the thickness standard deviation.
Optionally, the acquiring image data of the upper surface and the lower surface of the measured steel plate specifically includes: the method comprises the steps of adopting an array camera to collect image data of the upper surface and the lower surface of a measured steel plate according to a collected signal period and outputting a first coordinate of a sampling point, wherein the first coordinate is located in a coordinate system using the position of the array camera as an original point, the array camera comprises two groups of linear scanning cameras, the two groups of linear scanning cameras collect the image data of the upper surface and the lower surface of the measured steel plate respectively, and a plurality of linear scanning cameras in each group cover the wide edge of the measured steel plate together in a device cascading mode. The signal acquisition period is the ratio of the steel plate stepping length to the steel plate transmission rate. The speed controller is internally provided with a steel plate transmission speed and is used for controlling the rotating speed of the rolling machine according to the steel plate transmission speed; and the rolling machine is used for placing the steel plate to be measured and transmitting the steel plate to be measured through rotation to obtain the steel plate stepping length.
Preferably, the acquired image data is subjected to data processingThe pretreatment specifically comprises the following steps: converting first coordinates of sampling points output by each camera into a world coordinate system according to the relative position relationship of each camera in the array camera to obtain converted acquisition data, wherein the origin O of the world coordinate system is one corner of a measured steel plate, the X axis of the world coordinate system is parallel to the array camera formed by each camera, the Y axis of the world coordinate system is parallel to the transmission direction of the measured steel plate, the Z axis of the world coordinate system is vertical to an XOY plane, and the converted acquisition data are as follows:
wherein p = u represents the upper surface of the measured steel plate, p = d represents the lower surface of the measured steel plate, t represents the sampling time, i = j · N + k, j is the camera index, and i takes a value of 0,1,2 M Representing the total number of sampling points, N, of any surface of the steel plate to be tested at each sampling moment M M represents the number of sampling points of the last camera for shooting any surface of the measured steel plate.
Optionally, calculating a standard deviation of the thickness of the measured steel plate according to the preprocessed image data, specifically including: calculating the thickness of the steel plate at each sampling point of the measured steel plate according to the following formula:
wherein, the first and the second end of the pipe are connected with each other,
data is collected for the upper surface of the steel plate to be measured,
collecting data of the lower surface of the measured steel plate;
and calculating the thickness standard deviation of the measured steel plate according to the thickness of the steel plate and the following formula:
wherein, the first and the second end of the pipe are connected with each other,
the average thickness of the steel sheet to be measured is shown.
Optionally, the evaluating the thickness uniformity of the measured steel plate according to the thickness standard deviation specifically includes: evaluating the thickness uniformity of the measured steel plate in response to the fact that the thickness standard deviation is smaller than a preset threshold value; and evaluating the thickness unevenness of the measured steel plate in response to the thickness standard deviation being greater than or equal to a preset threshold value. The steel plate thickness standard deviation sigma represents the uniformity of the detected steel plate thickness, and the smaller the value, the more uniform the steel plate thickness is.
Example 3:
as shown in fig. 5, the present embodiment provides an electronic device, which includes a memory 51 and a processor 52, where the memory 51 stores therein a computer program, and the processor 52 is configured to run the computer program to implement the steel plate detection method according to embodiment 2.
The steel plate detection method of example 2 and the electronic device of example 3 evaluate the thickness uniformity of the steel plate by respectively acquiring image data of the upper surface and the lower surface of the steel plate to be measured, preprocessing the acquired data, and calculating the thickness standard deviation of the steel plate according to the preprocessed data. The steel plate detection system based on the machine vision detection of the flatness and flaws of the steel plate in industrial production has the advantages of being high in automation degree, comprehensive in detection, suitable for the production environment of a high-speed unit and high in detection precision compared with a manual visual method. Furthermore, the data collected by each camera is converted into the collected data in the same coordinate system, so that the thickness of the measured steel plate can be conveniently calculated subsequently, and the accuracy of the calculation result is ensured. In addition, the uniformity of the thickness of the measured steel plate is measured by using the thickness standard deviation, and the method is reasonable and effective in evaluation.
It will be understood that the above embodiments are merely exemplary embodiments taken to illustrate the principles of the present invention, which is not limited thereto. It will be apparent to those skilled in the art that various modifications and improvements can be made without departing from the spirit and scope of the invention, and such modifications and improvements are also considered to be within the scope of the invention.
Claims (13)
1. A steel plate detection system is characterized by comprising an image acquisition device, camera calibration software and data analysis software,
an image acquisition device for acquiring the image data of the upper surface and the lower surface of the measured steel plate,
camera calibration software connected with the image acquisition device for data preprocessing of the acquired image data,
and the data analysis software is connected with the camera calibration software and used for calculating the thickness standard deviation of the measured steel plate according to the preprocessed image data and evaluating the thickness uniformity of the measured steel plate according to the thickness standard deviation.
2. The steel sheet inspection system of claim 1, wherein the image acquisition device comprises an array camera and an acquisition signal controller,
the array camera is connected with the acquisition signal controller and used for acquiring image data of the upper surface and the lower surface of the measured steel plate according to the acquisition signal sent by the acquisition signal controller and outputting a first coordinate of a sampling point, wherein the first coordinate is in a coordinate system taking the position of the array camera as an origin,
the acquisition signal controller is provided with an acquisition signal period and is used for sending acquisition signals to the array camera according to the acquisition signal period, wherein the acquisition signal period is the ratio of the steel plate stepping length to the steel plate transmission rate.
3. The steel sheet inspection system of claim 2, wherein the array cameras comprise two sets of linear scanning cameras,
and the two groups of linear scanning cameras are used for being fixedly arranged at the upper part and the lower part of the detection gate respectively and keeping a preset shooting distance between the two groups of linear scanning cameras and the corresponding surfaces of the detected steel plate respectively, wherein the plurality of linear scanning cameras in the group cover the wide edge of the detected steel plate together in a device cascading mode.
4. The steel sheet inspection system of claim 1, wherein the image capture device further comprises a rate controller and a rolling machine,
a speed controller connected with the rolling machine, the speed controller is provided with a steel plate transmission speed and is used for controlling the rotating speed of the rolling machine according to the steel plate transmission speed,
and the rolling machine is used for placing the steel plate to be tested and conveying the steel plate to be tested through rotation.
5. The steel sheet inspection system of claim 2, wherein the camera calibration software includes a coordinate conversion module,
a coordinate conversion module for converting the first coordinate of the sampling point output by each camera into a world coordinate system according to the relative position relationship of each camera in the array camera to obtain the converted acquisition data,
wherein, the original point O of the world coordinate system is an angle of the measured steel plate, the X axis of the world coordinate system is parallel to the array camera formed by each camera, the Y axis of the world coordinate system is parallel to the transmission direction of the measured steel plate, the Z axis of the world coordinate system is vertical to the XOY plane, and the converted collected data is as follows:
wherein p = u represents the upper surface of the measured steel plate, p = d represents the lower surface of the measured steel plate, t represents the sampling time, i = j · N + k, j is the camera index, and i takes a value of 0,1,2 M Representing the total number of sampling points, N, of any surface of the steel plate to be tested at each sampling moment M M represents the number of sampling points of the last camera for shooting any surface of the steel plate to be measured.
6. The steel plate inspection system of claim 1, wherein the data analysis software includes a first calculation module and a second calculation module,
the first calculation module is used for calculating the steel plate thickness of each sampling point of the measured steel plate according to the following formula:
wherein, the first and the second end of the pipe are connected with each other,
data is collected for the upper surface of the steel plate to be measured,
for collecting data of the lower surface of the measured steel plate,
the second calculation module is connected with the first calculation module and used for calculating the thickness standard deviation of the measured steel plate according to the thickness of the steel plate and the following formula:
wherein the content of the first and second substances,
the average thickness of the steel sheets measured is indicated.
7. The steel sheet inspection system of claim 6, wherein the data analysis software further comprises an evaluation module,
and the evaluation module is connected with the second calculation module and used for evaluating that the thickness of the measured steel plate is uniform in response to the thickness standard deviation being smaller than the preset threshold value, and evaluating that the thickness of the measured steel plate is non-uniform in response to the thickness standard deviation being larger than or equal to the preset threshold value.
8. A steel plate detection method is characterized by comprising the following steps:
collecting image data of the upper surface and the lower surface of a measured steel plate;
carrying out data preprocessing on the acquired image data;
and calculating the thickness standard deviation of the steel plate to be measured according to the preprocessed image data, and evaluating the thickness uniformity of the steel plate to be measured according to the thickness standard deviation.
9. The steel plate detection method according to claim 8, wherein the acquiring image data of the upper surface and the lower surface of the detected steel plate specifically comprises:
acquiring image data of the upper surface and the lower surface of the measured steel plate by adopting an array camera according to an acquisition signal period, and outputting a first coordinate of a sampling point, wherein the first coordinate is in a coordinate system taking the position of the array camera as an origin;
the array camera comprises two groups of linear scanning cameras, the two groups of linear scanning cameras respectively collect image data of the upper surface and the lower surface of the measured steel plate, and the plurality of linear scanning cameras in the group cover the broadsides of the measured steel plate together in a device cascading mode.
10. The steel plate detection method according to claim 8, wherein the data preprocessing is performed on the acquired image data, and specifically comprises:
converting the first coordinate of the sampling point output by each camera into a world coordinate system according to the relative position relationship of each camera in the array camera to obtain converted acquisition data,
wherein, the original point O of the world coordinate system is an angle of the measured steel plate, the X axis of the world coordinate system is parallel to the array camera formed by each camera, the Y axis of the world coordinate system is parallel to the transmission direction of the measured steel plate, the Z axis of the world coordinate system is vertical to the XOY plane, and the converted collected data is as follows:
wherein p = u represents the upper surface of the measured steel plate, p = d represents the lower surface of the measured steel plate, t represents the sampling time, i = j · N + k, j is the camera index, and i takes a value of 0,1,2 M Indicates each miningThe total number of sampling points, N, on any surface of the steel plate to be tested at the sampling time M M represents the number of sampling points of the last camera for shooting any surface of the steel plate to be measured.
11. The steel plate detection method according to claim 8, wherein calculating the standard deviation of the thickness of the steel plate to be detected according to the preprocessed image data specifically comprises:
calculating the thickness of the steel plate at each sampling point of the measured steel plate according to the following formula:
wherein the content of the first and second substances,
for collecting data of the upper surface of the measured steel plate,
collecting data of the lower surface of the measured steel plate;
and calculating the thickness standard deviation of the measured steel plate according to the thickness of the steel plate and the following formula:
wherein, the first and the second end of the pipe are connected with each other,
the average thickness of the steel sheets measured is indicated.
12. The method for detecting the steel plate according to claim 8, wherein the step of evaluating the thickness uniformity of the detected steel plate according to the thickness standard deviation specifically comprises the steps of:
evaluating the thickness uniformity of the measured steel plate in response to the fact that the thickness standard deviation is smaller than a preset threshold value;
and evaluating the thickness unevenness of the measured steel plate in response to the thickness standard deviation being greater than or equal to a preset threshold value.
13. An electronic device, comprising a memory having a computer program stored therein and a processor configured to run the computer program to implement the steel plate detection method according to any one of claims 8-12.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211564477.3A CN115791830A (en) | 2022-12-07 | 2022-12-07 | Steel plate detection system, steel plate detection method and electronic equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211564477.3A CN115791830A (en) | 2022-12-07 | 2022-12-07 | Steel plate detection system, steel plate detection method and electronic equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115791830A true CN115791830A (en) | 2023-03-14 |
Family
ID=85418865
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211564477.3A Pending CN115791830A (en) | 2022-12-07 | 2022-12-07 | Steel plate detection system, steel plate detection method and electronic equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115791830A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117190887A (en) * | 2023-11-06 | 2023-12-08 | 深圳市磐锋精密技术有限公司 | Aerogel thickness automatic detection system for mobile phone production |
-
2022
- 2022-12-07 CN CN202211564477.3A patent/CN115791830A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117190887A (en) * | 2023-11-06 | 2023-12-08 | 深圳市磐锋精密技术有限公司 | Aerogel thickness automatic detection system for mobile phone production |
| CN117190887B (en) * | 2023-11-06 | 2024-01-30 | 深圳市磐锋精密技术有限公司 | Aerogel thickness automatic detection system for mobile phone production |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109696356B (en) | Geosynthetic material tensile sample global strain field measuring device and method | |
| CN102393181B (en) | Automatic online detection method and device of angle steel geometric parameters | |
| CN107817044B (en) | Device and method for measuring plate vibration based on machine vision | |
| CN104048744A (en) | Non-contact real-time online vibration measurement method based on images | |
| CN112540089B (en) | Application method of digital imaging system in concrete bridge crack detection and analysis | |
| CN104457627A (en) | Method for utilizing Photoshop for precisely measuring area of plane of irregular object | |
| CN102226687A (en) | Method for measuring thickness of insulation and jacket material of electric cable and optical cable | |
| CN115791830A (en) | Steel plate detection system, steel plate detection method and electronic equipment | |
| CN113884011A (en) | Non-contact concrete surface crack measuring equipment and method | |
| CN102359761B (en) | Thickness measuring method for insulating and sheathing materials of cable and optical cable | |
| Zeinali et al. | Accurate structural dynamic response monitoring of multiple structures using one CCD camera and a novel targets configuration | |
| CN1282508C (en) | Automatic flatness measuring device and method | |
| He et al. | Size detection of firebricks based on machine vision technology | |
| CN201166625Y (en) | Apparatus for scanning and measuring solid material deflection | |
| CN115876782A (en) | Method and device for detecting flaw point of steel plate and storage medium | |
| CN115791802A (en) | Detection method and detection device for flaw surface of steel plate and storage medium | |
| CN112504137B (en) | Multi-target digital image detection method based on cloud computing | |
| CN115728240A (en) | Steel plate scratch detection method, detection device and storage medium | |
| TWI419762B (en) | Online measuring method and apparatus for work pieces of machine tools | |
| CN112924456A (en) | Curved screen detection device and detection method thereof | |
| CN103543157A (en) | Simulation and dynamic acquisition method and device for surface images of offline strip | |
| Szeto et al. | Nondestructive Test Using a 3D Computer Vision System for Damage Detection of Structures | |
| KR101213169B1 (en) | Visual encoder system using area camera | |
| CN204462038U (en) | A kind of electric connector housing machine vision on-line detecting system | |
| CN208654052U (en) | Computer cabinet plate surface detector |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |