CN112712495A - Anti-loose piece fracture identification method, system, terminal and storage medium - Google Patents
Anti-loose piece fracture identification method, system, terminal and storage medium Download PDFInfo
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- CN112712495A CN112712495A CN202011473076.8A CN202011473076A CN112712495A CN 112712495 A CN112712495 A CN 112712495A CN 202011473076 A CN202011473076 A CN 202011473076A CN 112712495 A CN112712495 A CN 112712495A
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0004—Industrial image inspection
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- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
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- G06T7/10—Segmentation; Edge detection
- G06T7/11—Region-based segmentation
Abstract
The embodiment of the application provides a method, a system, a terminal and a storage medium for identifying the breakage of a locking piece, and relates to the quality detection technology. The anti-loosening element breakage identification method comprises the following steps: determining point cloud data according to a pre-collected gray scale image and a pre-collected depth image; segmenting according to the point cloud data to obtain target point cloud of the anti-loosening piece; and when disconnected points exist in the obtained target point cloud, determining that the anti-loosening piece is broken. The embodiment of the application can automatically identify the broken anti-loosening piece, and compared with a manual detection mode, the anti-loosening piece has the advantages of higher detection efficiency, low danger degree, repeatable data check and higher reliability.
Description
Technical Field
The present disclosure relates to quality detection technologies, and in particular, to a method, a system, a terminal, and a storage medium for identifying a break of a locking member.
Background
In a rail vehicle such as a high-speed rail or a subway, a plurality of members are generally connected by a fastener such as a bolt. In order to prevent safety accidents caused by loosening of the fastening pieces, a loosening prevention piece wound with 8-shaped iron wires or the like is usually inserted between the two fastening pieces. The factors such as vibration generated in the running process of the railway vehicle lead to that the anti-loosening elements such as iron wires are easy to break.
In the related art, it is common to manually check to determine whether the check member is broken. Specifically, the inspector is required to judge by visual observation and manually pulling the release preventing member. However, as the number of high-speed rail vehicles is continuously increased, the detection number is continuously increased, the detection pressure is greatly increased, and the detection omission is easy to occur. And, when judging through manual pulling anti-loosening member, lead to anti-loosening member to warp very easily, lead to anti-loosening effect inefficacy of anti-loosening member.
Disclosure of Invention
In order to solve one of the technical defects, embodiments of the present application provide a method, a system, a terminal, and a storage medium for identifying a break of a locking piece.
The embodiment of the first aspect of the application provides a method for identifying the breakage of a locking piece, which comprises the following steps:
determining point cloud data according to a pre-collected gray scale image and a pre-collected depth image;
segmenting according to the point cloud data to obtain target point cloud of the anti-loosening piece;
and when disconnected points exist in the obtained target point cloud, determining that the anti-loosening piece is broken.
An embodiment of a second aspect of the present application provides a check member fracture identification system, including:
the first processing module is used for determining point cloud data according to a pre-collected gray scale image and a pre-collected depth image;
the second processing module is used for segmenting according to the point cloud data to obtain target point cloud of the anti-loosening piece;
and the third processing module is used for determining that the anti-loosening piece is broken when disconnected points exist in the obtained target point cloud.
An embodiment of a third aspect of the present application provides a terminal, including:
a memory;
a processor; and
a computer program;
wherein the computer program is stored in the memory and configured to be executed by the processor to implement a method as claimed in any preceding claim.
An embodiment of a fourth aspect of the present application provides a computer-readable storage medium having a computer program stored thereon; the computer program is executed by a processor to implement a method as claimed in any preceding claim.
The embodiment of the application provides a method, a system, a terminal and a storage medium for identifying breakage of a locking piece, which can acquire the locking piece through acquired image information of the locking piece, segment the area of the locking piece in point cloud data and finish breakage judgment, so that the broken locking piece is automatically identified. This embodiment is for artifical detection mode, and detection efficiency is higher, and the danger degree is low, but data retest, and the reliability is higher.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a schematic flow chart of a method provided in an exemplary embodiment;
FIG. 2 is a schematic flow chart of a method provided in an exemplary embodiment;
FIG. 3 is a schematic flow chart of an apparatus provided in an exemplary embodiment;
FIG. 4a is a schematic illustration of an unbroken wire provided in an exemplary embodiment;
FIG. 4b is a schematic representation of a broken iron wire in accordance with an exemplary embodiment;
fig. 4c is a schematic view of a broken iron wire according to another exemplary embodiment.
Detailed Description
In order to make the technical solutions and advantages of the embodiments of the present application more apparent, the following further detailed description of the exemplary embodiments of the present application with reference to the accompanying drawings makes it clear that the described embodiments are only a part of the embodiments of the present application, and are not exhaustive of all embodiments. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
In the related art, it is common to manually check to determine whether the check member is broken. Specifically, the inspector is required to judge by visual observation and manually pulling the release preventing member. However, as the number of high-speed rail vehicles is continuously increased, the detection number is continuously increased, the detection pressure is greatly increased, and the detection omission is easy to occur. And, when judging through manual pulling anti-loosening member, lead to anti-loosening member to warp very easily, lead to anti-loosening effect inefficacy of anti-loosening member.
In order to overcome at least one of the above problems, embodiments of the present application provide a method, a system, a terminal, and a storage medium for identifying a breakage of a locking member, which can acquire the locking member through acquired image information of the locking member, segment an area of the locking member in point cloud data, and complete a breakage judgment, so as to automatically identify the broken locking member. This embodiment is for artifical detection mode, and detection efficiency is higher, and the danger degree is low, but data retest, and the reliability is higher.
The following describes functions and implementation processes of a method, a system, a terminal and a storage medium for identifying a break of a locking piece according to an embodiment of the present application with reference to the accompanying drawings.
As shown in fig. 1, the method for identifying a break of a locking piece provided in this embodiment includes:
s101, point cloud data are determined according to a pre-collected gray scale image and a pre-collected depth image;
s102, segmenting according to the point cloud data to obtain target point cloud of the anti-loosening piece;
s103, when the disconnected points exist in the obtained target point cloud, the anti-loosening piece is determined to be broken.
In step S101, a gray scale image and a depth image of the anti-loosening element are acquired in advance. In particular, the grayscale image and the depth image can be acquired by the aligned grayscale camera and the depth camera, respectively. The gray scale camera and the depth camera are aligned, so that the gray scale camera and the depth camera are described by using one coordinate system, and subsequent data processing amount is reduced. The gray level camera depth cameras can be a plurality of groups, and the plurality of groups of gray level cameras and the depth cameras can work independently, so that image information of the anti-loosening pieces can be collected simultaneously.
After the image information acquired by the camera is acquired, the point cloud data of the anti-loosening element is determined according to the image information, so that the follow-up processing based on three-dimensional stereoscopic vision is facilitated. Specifically, step S101 includes:
acquiring position information of the anti-loosening piece according to a pre-acquired gray scale image; the position information of the anti-loosening piece comprises a two-dimensional coordinate of the anti-loosening piece, and the width and the height of the anti-loosening piece;
extracting the depth value of the corresponding area from the depth map according to the position information of the anti-loosening element;
and determining point cloud data of the anti-loose piece according to the position information of the anti-loose piece and the extracted depth value.
In this embodiment, the anti-loosening member is not exemplified as a wire. It can be understood that: in other examples, the check member may be a check member made of other materials. Wherein, the schematic diagram of the iron wire being intact, i.e. the iron wire not broken, is shown in fig. 4 a; the breaking of the iron wire is schematically shown in fig. 4b and 4 c.
And detecting and positioning the iron wire position based on the gray-scale image. Specifically, a convolutional neural network based on deep learning, such as a fast r-cnn target detection network, can be adopted to detect the positions of iron wires from a gray scale image; and outputting position information [ x1, y1, w, h ] of a plurality of iron wires in the drawing, wherein x1 and y1 are coordinates of the upper left corner of the iron wires, and w and h are the width and height of the iron wires respectively.
According to the detected position information of the iron wire, extracting the depth value of the corresponding area from the depth map, and obtaining corresponding three-dimensional point cloud data according to the camera internal parameters, wherein the data dimension is N x 3, N represents the point cloud number of the whole iron wire, and 3 is the x, y and z coordinate dimension.
In step S102, the point cloud is segmented based on the deep learning, and a target point cloud corresponding to the anti-loosening element is segmented from the point cloud to filter out point cloud data of interference items such as fasteners. Specifically, before step S102, the corresponding detection model may be trained in advance through deep learning. In step S102, the point cloud of the anti-loosening element and the fastening element is segmented according to the trained detection model and the obtained gray map, and the point cloud of the anti-loosening element is taken as a target point cloud for subsequent processing, so that the accuracy of fracture identification of the anti-loosening element is improved. The method for training the detection model may adopt conventional techniques in the art, and this embodiment is not limited in detail here.
In step S102, the N × 3 point cloud data obtained in step S101 is input, N × 3 iron wire point clouds are output, N represents the number of target point clouds, and N < N.
In step S103, whether the anti-loosening element is broken can be determined by determining whether the n × 3 point cloud is connected. Therefore, the areas of the iron wires can be divided in the three-dimensional point cloud, the fracture judgment is completed, and the automatic identification of the bolt anti-loosening iron wire fracture based on the three-dimensional space stereoscopic vision is realized.
And when the Euclidean distance of the nearest neighbor of each point and other n-1 points is less than or equal to a preset dis _ thresh threshold value, determining point cloud communication. The setting of dis _ thresh is related to the camera accuracy, and this embodiment is not limited in detail here.
Specifically, step S103 includes:
respectively obtaining nearest neighbor Euclidean distances between each point in the target point cloud and the rest points;
when the nearest neighbor Euclidean distance is larger than the threshold value, determining that the point of the nearest neighbor Euclidean distance larger than the threshold value is broken, and determining that the anti-loosening piece is broken;
and determining that the anti-loosening piece is not broken if the nearest Euclidean distances between each point in the target point cloud and the rest points are less than or equal to a threshold value.
The embodiment of the application provides a method for identifying breakage of a locking piece, which can acquire the locking piece through acquired image information of the locking piece, divide the area of the locking piece in point cloud data and finish breakage judgment, so that automatic identification of breakage of a bolt locking iron wire is realized based on three-dimensional space stereoscopic vision. This embodiment is for artifical detection mode, and detection efficiency is higher, and the danger degree is low, but data retest, and the reliability is higher.
The implementation of the method of this embodiment is illustrated below by taking the anti-loosening element as an iron wire. As shown in fig. 2, the method comprises the steps of:
and collecting a gray scale image and a depth image. The method has the advantages that the gray level and depth alignment cameras are adopted for shooting, the positioning of the iron wire positions can be completed based on the gray level images, and the depth cameras can acquire three-dimensional point cloud data.
And carrying out iron wire detection and positioning based on the convolution neural network of the gray level diagram. And carrying out iron wire detection and positioning based on the convolution neural network of the gray level diagram. The detection and positioning of the iron wire positions are completed based on the gray-scale map, a convolutional neural network based on deep learning, such as a fast r-cnn target detection network, can be adopted to output position information [ x1, y1, w, h ] of a plurality of iron wires in the map, wherein x1 and y1 are coordinates of the upper left corner of the iron wire, and w and h are the width and height of the iron wire. During specific implementation, the iron wire detection model at the position can be trained in advance through the convolutional neural network, and after the gray scale image of the iron wire is obtained, the detection and positioning of the position of the iron wire can be completed according to the iron wire detection model and the gray scale image.
And extracting point clouds of corresponding areas from the depth map based on the detection result. And extracting the depth value of the corresponding area on the depth map according to the detection result and obtaining corresponding three-dimensional point cloud data according to the internal parameters of the camera, wherein the data dimension is N x 3, N represents the number of the point clouds, and 3 is the x, y and z coordinate dimension.
And (3) a bolt and iron wire point cloud segmentation algorithm based on deep learning. And (3) completing the segmentation of the point cloud based on deep learning, inputting the point cloud data of N x 3 in the step 3, and outputting the point cloud of N x 3 iron wires, wherein N is less than N.
And judging whether the iron wire is broken or not based on the point cloud segmentation result. And judging whether the n x 3 iron wire point clouds are communicated, if not, breaking the iron wires, and if so, ensuring that the iron wires are normal. The definition of point cloud connectivity is: the Euclidean distance of the nearest neighbor of each point and other n-1 points is less than or equal to a dis _ thresh threshold, and the setting of the dis _ thresh is related to the precision of the camera.
The method for identifying the breakage of the anti-loosening piece can realize automatic detection of the broken anti-loosening piece through three-dimensional space stereoscopic vision, can divide the area of the iron wire in the three-dimensional point cloud and finish breakage judgment, and has the advantages of high speed, low danger degree, repeatable data check and the like compared with manual detection.
The present embodiment further provides a system for identifying a breakage of a locking member, which is a product embodiment corresponding to the foregoing method embodiment, and the functions and implementation processes of the system are the same as or similar to those of the foregoing embodiment, and are not described herein again.
As shown in fig. 3, a check member breakage recognition system includes:
the first processing module 11 is used for determining point cloud data according to a pre-collected gray scale image and a pre-collected depth image;
the second processing module 12 is used for segmenting according to the point cloud data to obtain target point cloud of the anti-loosening piece;
and the third processing module 13 is configured to determine that the anti-loosening member is broken when there is a point that is not connected in the obtained target point cloud.
In one possible implementation manner, the first processing module 11 is specifically configured to:
acquiring position information of the anti-loosening piece according to a pre-acquired gray scale image;
extracting the depth value of the corresponding area from the depth map according to the position information of the anti-loosening element;
and determining point cloud data of the anti-loose piece according to the position information of the anti-loose piece and the extracted depth value.
In one possible implementation, the position information of the anti-loose piece includes two-dimensional coordinates of the anti-loose piece, a width and a height of the anti-loose piece.
In one possible implementation manner, the third processing module 13 is specifically configured to:
respectively obtaining nearest neighbor Euclidean distances between each point in the target point cloud and the rest points;
and when the nearest neighbor Euclidean distance is greater than the threshold value, determining that the point of the nearest neighbor Euclidean distance greater than the threshold value is broken, and determining that the anti-loosening piece is broken.
In one possible implementation manner, the first processing module 11 is further configured to:
and acquiring a gray scale image and a depth image which are respectively acquired by the aligned gray scale camera and the aligned depth camera.
The embodiment of the application provides a locking piece fracture identification system can acquire the locking piece through the image information of the acquired locking piece, segments the region of the locking piece in point cloud data and finishes fracture judgment, thereby automatically identifying the fractured locking piece. This embodiment is for artifical detection mode, and detection efficiency is higher, and the danger degree is low, but data retest, and the reliability is higher.
The present embodiment provides a terminal, including:
a memory;
a processor; and
a computer program;
wherein the computer program is stored in the memory and configured to be executed by the processor to implement the respective method.
The memory is used for storing a computer program, and the processor executes the computer program after receiving the execution instruction, and the method executed by the apparatus defined by the flow process disclosed in the foregoing corresponding embodiments can be applied to or implemented by the processor.
The Memory may comprise a Random Access Memory (RAM) and may also include a non-volatile Memory, such as at least one disk Memory. The memory can implement communication connection between the system network element and at least one other network element through at least one communication interface (which may be wired or wireless), and the internet, a wide area network, a local network, a metropolitan area network, and the like can be used.
The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the method disclosed in the first embodiment may be implemented by hardware integrated logic circuits in a processor or instructions in the form of software. The Processor may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; but may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components. The corresponding methods, steps, and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software elements in the decoding processor. The software elements may be located in ram, flash, rom, prom, or eprom, registers, among other storage media that are well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor.
The present embodiment provides a computer-readable storage medium having stored thereon a computer program; the computer program is executed by a processor in a corresponding method. For specific implementation, reference may be made to the method embodiments, which are not described herein again.
It should be noted that: unless specifically stated otherwise, the relative steps, numerical expressions, and values of the components and steps set forth in these embodiments do not limit the scope of the present invention. In all examples shown and described herein, unless otherwise specified, any particular value should be construed as merely illustrative, and not restrictive, and thus other examples of example embodiments may have different values.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a unit, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While the preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.
Claims (12)
1. A check member breakage recognition method is characterized by comprising the following steps:
determining point cloud data according to a pre-collected gray scale image and a pre-collected depth image;
segmenting according to the point cloud data to obtain target point cloud of the anti-loosening piece;
and when disconnected points exist in the obtained target point cloud, determining that the anti-loosening piece is broken.
2. The method of claim 1, wherein determining point cloud data from a pre-collected gray scale map, a pre-collected depth map comprises:
acquiring position information of the anti-loosening piece according to a pre-acquired gray scale image;
extracting the depth value of the corresponding area from the depth map according to the position information of the anti-loosening element;
and determining corresponding point cloud data according to the position information of the anti-loosening element and the extracted depth value.
3. The method of claim 2, wherein the position information of the anti-release member includes two-dimensional coordinates of the anti-release member, a width and a height of the anti-release member.
4. The method of claim 1, wherein determining that the check member is broken when there is a disconnected point in the obtained target point cloud comprises:
respectively acquiring nearest neighbor Euclidean distances between each point in the target point cloud and the rest points;
and when the nearest neighbor Euclidean distance is greater than a threshold value, determining that the point of the nearest neighbor Euclidean distance greater than the threshold value is broken, and determining that the anti-loosening piece is broken.
5. The method of claim 1, further comprising, prior to determining point cloud data from the pre-collected grayscale map, the pre-collected depth map:
and acquiring a gray scale image and a depth image which are respectively acquired by the aligned gray scale camera and the aligned depth camera.
6. A check member breakage identification system, comprising:
the first processing module is used for determining point cloud data according to a pre-collected gray scale image and a pre-collected depth image;
the second processing module is used for segmenting according to the point cloud data to obtain target point cloud of the anti-loosening piece;
and the third processing module is used for determining that the anti-loosening piece is broken when disconnected points exist in the obtained target point cloud.
7. The system of claim 6, wherein the first processing module is specifically configured to:
acquiring position information of the anti-loosening piece according to a pre-acquired gray scale image;
extracting the depth value of the corresponding area from the depth map according to the position information of the anti-loosening element;
and determining point cloud data of the anti-loosening piece according to the position information of the anti-loosening piece and the extracted depth value.
8. The system of claim 7, wherein the position information of the anti-release member includes two-dimensional coordinates of the anti-release member, a width and a height of the anti-release member.
9. The system of claim 6, wherein the third processing module is specifically configured to:
respectively acquiring nearest neighbor Euclidean distances between each point in the target point cloud and the rest points;
and when the nearest neighbor Euclidean distance is greater than a threshold value, determining that the point of the nearest neighbor Euclidean distance greater than the threshold value is broken, and determining that the anti-loosening piece is broken.
10. The system of claim 6, wherein the first processing module is further configured to:
and acquiring a gray scale image and a depth image which are respectively acquired by the aligned gray scale camera and the aligned depth camera.
11. A terminal, comprising:
a memory;
a processor; and
a computer program;
wherein the computer program is stored in the memory and configured to be executed by the processor to implement the method of any one of claims 1-5.
12. A computer-readable storage medium, having stored thereon a computer program; the computer program is executed by a processor to implement the method of any one of claims 1-5.
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| CN202011473076.8A CN112712495A (en) | 2020-12-15 | 2020-12-15 | Anti-loose piece fracture identification method, system, terminal and storage medium |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114426187A (en) * | 2022-03-10 | 2022-05-03 | 郑州煤矿机械集团股份有限公司 | Scraper chain early warning system for data fusion and deep learning and detection method |
| CN115690098A (en) * | 2022-12-16 | 2023-02-03 | 中科海拓(无锡)科技有限公司 | Method for detecting breakage and loss of iron wire |
-
2020
- 2020-12-15 CN CN202011473076.8A patent/CN112712495A/en active Pending
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114426187A (en) * | 2022-03-10 | 2022-05-03 | 郑州煤矿机械集团股份有限公司 | Scraper chain early warning system for data fusion and deep learning and detection method |
| CN114426187B (en) * | 2022-03-10 | 2023-08-22 | 郑州恒达智控科技股份有限公司 | Data fusion and deep learning scraper chain early warning system |
| CN115690098A (en) * | 2022-12-16 | 2023-02-03 | 中科海拓(无锡)科技有限公司 | Method for detecting breakage and loss of iron wire |
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