CN113129271A - Method, system and device for detecting steel bar spacing in building and storage medium - Google Patents
Method, system and device for detecting steel bar spacing in building and storage medium Download PDFInfo
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Abstract
The application discloses a method, a system and a device for detecting the distance between reinforcing steel bars in a building and a storage medium. The method comprises the steps of collecting point cloud picture data and parameter data of a batch of reinforcing steel bars; inputting the point cloud picture data into a machine learning model, and extracting a plurality of first characteristic data of the point cloud picture data; inputting the parameter data into a machine learning model, and extracting various second characteristic data of the parameter data; according to the first feature data and the second feature data, feature selection is carried out on the machine learning model; predicting a first diameter of the first reinforcing steel bar and a second diameter of the second reinforcing steel bar through the machine learning model after feature selection; the spacing between the first rebar and the second rebar is determined based on the first diameter and the second diameter. The method can effectively improve the accuracy of detecting the space between the steel bars in the building, is beneficial to high-quality construction of the building, and improves the safety and reliability of the building. The method can be widely applied to the technical field of buildings.
Description
Technical Field
The application relates to the technical field of buildings, in particular to a method, a system and a device for detecting the spacing between reinforcing steel bars in a building and a storage medium.
Background
In the current building field, steel bars are widely used for various building structures, especially large, heavy, light thin-walled and high-rise building structures, have excellent mechanical properties, are convenient to process, have strong bearing capacity, and belong to one of very important projects in construction.
At present, the construction of the steel bars in the building mainly involves the inspection of the space, and as the bearing capacity of the reinforced concrete structure is greatly influenced by the size and the position of the steel bars, the correct size and position of the installation of the steel bars are ensured so as to ensure the structural integrity of the structure. Is an important task for manufacturers and field engineers during the manufacturing and construction phases. In the related art, a worker often grasps the distance between the two reinforcing steel bars through experience, and a large error may occur, so that the construction quality is affected. In view of the above, there is a need to solve the technical problems in the related art.
Disclosure of Invention
The present application aims to solve at least one of the technical problems in the related art to some extent.
Therefore, an object of the embodiments of the present application is to provide a method for detecting a distance between reinforcing bars in a building, which can effectively improve accuracy of detecting the distance between reinforcing bars in the building, facilitate high-quality construction of the building, and improve safety and reliability of the building.
Another object of the embodiments of the present application is to provide a system for detecting a distance between reinforcing bars in a building.
In order to achieve the technical purpose, the technical scheme adopted by the embodiment of the application comprises the following steps:
in a first aspect, an embodiment of the present application provides a method for detecting a distance between reinforcing bars in a building, including the following steps:
collecting point cloud picture data and parameter data of a batch of reinforcing steel bars; the parameter data comprises material strength, surface color and surface roughness of the steel bar;
inputting the point cloud picture data into a machine learning model, and extracting various first characteristic data of the point cloud picture data;
inputting the parameter data into a machine learning model, and extracting multiple kinds of second characteristic data of the parameter data;
according to the first feature data and the second feature data, feature selection is carried out on the machine learning model;
predicting a first diameter of the first reinforcing steel bar and a second diameter of the second reinforcing steel bar through the machine learning model after feature selection; the first and second rebars are adjacent;
determining a spacing between the first rebar and the second rebar based on the first diameter and the second diameter.
In addition, according to the method for detecting the distance between the reinforcing steel bars in the building of the above embodiment of the present application, the following additional technical features may also be provided:
further, in an embodiment of the present application, the performing feature selection on the machine learning model according to the first feature data and the second feature data includes:
determining a first correlation coefficient of the first characteristic data and the diameter of the steel bar and a second correlation coefficient of the second characteristic data and the diameter of the steel bar;
and selecting the characteristics of the machine learning model according to the sizes of the first correlation coefficient and the second correlation coefficient.
Further, in an embodiment of the present application, the determining the first correlation coefficient between the first characteristic data and the diameter of the steel bar includes:
determining a first correlation coefficient of the first characteristic data and the diameter of the steel bar by a Pearson correlation coefficient method.
Further, in an embodiment of the present application, the acquiring data of the cloud point map of the batch of the steel bars includes:
scanning the deformation image data of the steel bar through a ground three-dimensional laser scanner to obtain the point cloud picture data.
Further, in an embodiment of the present application, the scanning the deformation image data of the steel bar by the ground three-dimensional laser scanner includes:
and scanning deformation image data of the steel bar through a ground three-dimensional laser scanner under different incidence angles, different distances and different angular resolutions respectively.
Further, in one embodiment of the present application, the determining a spacing between the first rebar and the second rebar based on the first diameter and the second diameter comprises:
determining a first main shaft position of the first steel bar and a second main shaft position of the second steel bar according to the first diameter and the second diameter;
and determining the distance between the first steel bar and the second steel bar according to the first main shaft position and the second main shaft position.
In a second aspect, an embodiment of the present application provides a system for detecting a distance between reinforcing bars in a building, including:
the acquisition module is used for acquiring point cloud picture data and parameter data of the batches of reinforcing steel bars; the parameter data comprises material strength, surface color and surface roughness of the steel bar;
the first input module is used for inputting the point cloud picture data into a machine learning model and extracting various first feature data of the point cloud picture data;
the second input module is used for inputting the parameter data into a machine learning model and extracting various second feature data of the parameter data;
the feature selection module is used for selecting features of the machine learning model according to the first feature data and the second feature data;
the prediction module is used for predicting a first diameter of the first steel bar and a second diameter of the second steel bar through the machine learning model after the characteristic selection; the first and second rebars are adjacent;
and the detection module is used for determining the distance between the first steel bar and the second steel bar according to the first diameter and the second diameter.
In a third aspect, an embodiment of the present application provides a device for detecting a reinforcement distance in a building, including:
at least one processor;
at least one memory for storing at least one program;
the at least one program, when executed by the at least one processor, causes the at least one processor to implement the method of detecting a rebar spacing in a building of the first aspect.
In a fourth aspect, embodiments of the present application further provide a computer-readable storage medium, in which a program executable by a processor is stored, and when the program is executed by the processor, the program is used to implement the method for detecting a distance between reinforcing bars in a building according to the first aspect.
Advantages and benefits of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application:
according to the detection method for the steel bar spacing in the building, provided by the embodiment of the application, the point cloud picture data and the parameter data of the steel bars in batches are collected; the parameter data comprises material strength, surface color and surface roughness of the steel bar; inputting the point cloud picture data into a machine learning model, and extracting various first characteristic data of the point cloud picture data; inputting the parameter data into a machine learning model, and extracting multiple kinds of second characteristic data of the parameter data; according to the first feature data and the second feature data, feature selection is carried out on the machine learning model; predicting a first diameter of the first reinforcing steel bar and a second diameter of the second reinforcing steel bar through the machine learning model after feature selection; the first and second rebars are adjacent; determining a spacing between the first rebar and the second rebar based on the first diameter and the second diameter. The method can effectively improve the accuracy of detecting the space between the steel bars in the building, is beneficial to high-quality construction of the building, and improves the safety and reliability of the building.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following description is made on the drawings of the embodiments of the present application or the related technical solutions in the prior art, and it should be understood that the drawings in the following description are only for convenience and clarity of describing some embodiments in the technical solutions of the present application, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic flow chart illustrating an embodiment of a method for detecting a distance between reinforcing bars in a building according to the present invention;
FIG. 2 is a schematic structural diagram of an embodiment of a system for detecting a distance between reinforcing bars in a building according to the present invention;
fig. 3 is a schematic structural diagram of a specific embodiment of a device for detecting a distance between reinforcing bars in a building according to the present application.
Detailed Description
Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary only for the purpose of explaining the present application and are not to be construed as limiting the present application. The step numbers in the following embodiments are provided only for convenience of illustration, the order between the steps is not limited at all, and the execution order of each step in the embodiments can be adapted according to the understanding of those skilled in the art.
The embodiment of the application provides a method for detecting the distance between reinforcing steel bars in a building, and the control method in the embodiment of the application can be applied to a terminal, a server, software running in the terminal or the server and the like. The terminal may be, but is not limited to, a tablet computer, a notebook computer, a desktop computer, and the like. The server may be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server providing basic cloud computing services such as cloud service, a cloud database, cloud computing, a cloud function, cloud storage, network service, cloud communication, middleware service, domain name service, security service, CDN, and a big data and artificial intelligence platform. Referring to fig. 1, the method mainly comprises the following steps:
in the embodiment of the application, in the first step of the training process, the point cloud image data of the reinforcing steel bars can be collected by scanning deformation image data of the reinforcing steel bars with different diameters by using a TLS (Terrestrial Laser Scanner ground three-dimensional Laser Scanner). Specifically, in the embodiment of the present application, seven steel bar diameters, 10 mm, 12 mm, 16 mm, 20 mm, 25 mm, 32 mm and 40 mm, which are sequentially denoted as D10, D12, D16, D20, D25, D32 and D40, may be selected, which is a type of steel bar widely used in the field of building industry. In the data acquisition process, the TLS can be controlled to acquire image data for multiple times under three variables of different incidence angles, different distances and different angular resolutions so as to acquire point cloud picture data of the steel bars under different conditions as much as possible, and a high-precision machine learning model can be generated conveniently, so that the finally obtained result is more accurate.
In the embodiment of the application, for the task of predicting the diameter of the steel bar, the consideration of steel bar parameter data is also added, and the parameter data can comprise the material strength, the surface color, the surface roughness and the like of the steel bar.
Before feature extraction is carried out on the point cloud picture data and the parameter data of each steel bar, unnecessary scanning points, mainly background noise and mixed pixels, near the point cloud picture data of the steel bars can be eliminated. In the embodiment of the application, the background noise and the mixed pixels in the original scanning points can be removed by data preprocessing, and a single steel bar is cut into a plurality of small examples for feature extraction in the next step. Specifically, first, the background noise can be automatically separated from the scanned points of the rebar according to the Z-axis histogram using a plurality of Otsu thresholds. Once the background noise is removed, the blended pixel noise can be detected and removed in the next step. Each rebar is assumed to be a line and in order to identify multiple rebars, an iterative run of line RANSAC identification can be performed to find the best fit line for the existing scan points. Assuming that the axis direction of the steel bar is an x axis and the plane of the cross section is a y-z plane, the steel bar is rotated to ensure that the steel bar part is parallel to the x axis, so as to facilitate further data processing. And after the steel bar is sliced, projecting scanning points of each slice are arranged on a y-z plane for realizing feature extraction.
in the embodiment of the application, the key features of the steel bars are extracted for machine learning. Specifically, multiple features in the point cloud image data can be extracted and recorded as first feature data, and multiple features in the parameter data can be extracted and recorded as second feature data. The most relevant or critical characteristics to the steel bar diameter prediction are determined through characteristic selection, so that the accuracy of machine learning prediction of the steel bar diameter is improved, and the calculation time is reduced. In the embodiment of the application, a correlation selection method can be adopted for feature selection, and under the condition that the feature data and the predicted value are in a linear relationship, the feature data is selected to be a robust method, because the extracted feature data is often in a linear relationship with the predicted diameter of the steel bar. In the embodiment of the application, the optimal feature data type can be selected from the correlation coefficients of the feature data and the diameter, and the features extracted during the subsequent prediction of the model are determined. In the embodiment of the present application, a pearson correlation coefficient method may be used to calculate the correlation coefficient. Specifically, a correlation coefficient between the first characteristic data and the diameter of the steel bar is recorded as a first correlation coefficient, and a correlation coefficient between the second characteristic data and the diameter of the steel bar is recorded as a second correlation coefficient. And selecting the characteristics of the machine learning model according to the sizes of the first correlation coefficient and the second correlation coefficient. The machine learning model in the embodiment of the application can be any one of original Bayes, discriminant analysis, classification trees, nearest neighbor and support vector machines.
in the embodiment of the application, after feature selection is completed, the diameters of two adjacent steel bars can be predicted by using a machine learning model. Specifically, the two steel bars are respectively marked as a first steel bar and a second steel bar, the point cloud picture data and the parameter data of the first steel bar are input into a machine learning model, and a diameter prediction result of the first steel bar can be obtained and marked as a first diameter; and inputting the point cloud picture data and the parameter data of the second steel bar into the machine learning model to obtain a diameter prediction result of the second steel bar, and recording the diameter prediction result as a second diameter.
In the embodiment of the application, according to the first diameter and the second diameter, a first main shaft position of the first steel bar and a second main shaft position of the second steel bar can be determined; and the distance between the first and second rebars may be further determined based on the first and second spindle positions. Based on the interval between the adjacent reinforcing bars, whether the building meets the construction requirements can be judged, and the safety and the reliability of the building are improved.
The following describes in detail a system for detecting a distance between reinforcement bars in a building according to an embodiment of the present application with reference to the accompanying drawings.
Referring to fig. 2, the system for detecting a distance between reinforcing bars in a building provided in the embodiment of the present application includes:
the acquisition module 101 is used for acquiring point cloud picture data and parameter data of a batch of reinforcing steel bars; the parameter data comprises material strength, surface color and surface roughness of the steel bar;
the first input module 102 is used for inputting the point cloud picture data into a machine learning model and extracting various first feature data of the point cloud picture data;
the second input module 103 is used for inputting the parameter data into a machine learning model and extracting various second feature data of the parameter data;
a feature selection module 104, configured to perform feature selection on the machine learning model according to the first feature data and the second feature data;
a prediction module 105, configured to predict a first diameter of the first steel bar and a second diameter of the second steel bar through the machine learning model after feature selection; the first and second rebars are adjacent;
a detecting module 106, configured to determine a distance between the first steel bar and the second steel bar according to the first diameter and the second diameter.
It is to be understood that the contents in the foregoing method embodiments are all applicable to this system embodiment, the functions specifically implemented by this system embodiment are the same as those in the foregoing method embodiment, and the advantageous effects achieved by this system embodiment are also the same as those achieved by the foregoing method embodiment.
Referring to fig. 3, the present application provides a device for detecting a reinforcement distance in a building, including:
at least one processor 201;
at least one memory 202 for storing at least one program;
the at least one program, when executed by the at least one processor 201, causes the at least one processor 201 to implement a method of detecting a reinforcement pitch in a building.
Similarly, the contents of the method embodiments are all applicable to the apparatus embodiments, the functions specifically implemented by the apparatus embodiments are the same as the method embodiments, and the beneficial effects achieved by the apparatus embodiments are also the same as the beneficial effects achieved by the method embodiments.
The embodiment of the present application also provides a computer-readable storage medium, in which a program executable by the processor 201 is stored, and the program executable by the processor 201 is used for executing the above-mentioned method for detecting the distance between the reinforcing bars in the building when the program is executed by the processor 201.
Similarly, the contents in the above method embodiments are all applicable to the computer-readable storage medium embodiments, the functions specifically implemented by the computer-readable storage medium embodiments are the same as those in the above method embodiments, and the beneficial effects achieved by the computer-readable storage medium embodiments are also the same as those achieved by the above method embodiments.
In alternative embodiments, the functions/acts noted in the block diagrams may occur out of the order noted in the operational illustrations. 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/acts involved. Furthermore, the embodiments presented and described in the flowcharts of the present application are provided by way of example in order to provide a more thorough understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and in which sub-operations described as part of larger operations are performed independently.
Furthermore, although the present application is described in the context of functional modules, it should be understood that, unless otherwise stated to the contrary, one or more of the functions and/or features may be integrated in a single physical device and/or software module, or one or more functions and/or features may be implemented in separate physical devices or software modules. It will also be appreciated that a detailed discussion regarding the actual implementation of each module is not necessary for an understanding of the present application. Rather, the actual implementation of the various functional modules in the apparatus disclosed herein will be understood within the ordinary skill of an engineer, given the nature, function, and internal relationship of the modules. Accordingly, those skilled in the art can, using ordinary skill, practice the present application as set forth in the claims without undue experimentation. It is also to be understood that the specific concepts disclosed are merely illustrative of and not intended to limit the scope of the application, which is defined by the appended claims and their full scope of equivalents.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
In the foregoing description of the specification, reference to the description of "one embodiment/example," "another embodiment/example," or "certain embodiments/examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present application have been shown and described, it will be understood by those of ordinary skill in the art that: numerous changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the application, the scope of which is defined by the claims and their equivalents.
While the present application has been described with reference to the preferred embodiments, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (9)
1. A method for detecting the space between reinforcing steel bars in a building is characterized by comprising the following steps:
collecting point cloud picture data and parameter data of a batch of reinforcing steel bars; the parameter data comprises material strength, surface color and surface roughness of the steel bar;
inputting the point cloud picture data into a machine learning model, and extracting various first characteristic data of the point cloud picture data;
inputting the parameter data into a machine learning model, and extracting multiple kinds of second characteristic data of the parameter data;
according to the first feature data and the second feature data, feature selection is carried out on the machine learning model;
predicting a first diameter of the first reinforcing steel bar and a second diameter of the second reinforcing steel bar through the machine learning model after feature selection; the first and second rebars are adjacent;
determining a spacing between the first rebar and the second rebar based on the first diameter and the second diameter.
2. The method for detecting the distance between reinforcing steel bars in a building according to claim 1, wherein the characteristic selection of the machine learning model according to the first characteristic data and the second characteristic data comprises:
determining a first correlation coefficient of the first characteristic data and the diameter of the steel bar and a second correlation coefficient of the second characteristic data and the diameter of the steel bar;
and selecting the characteristics of the machine learning model according to the sizes of the first correlation coefficient and the second correlation coefficient.
3. The method for detecting the distance between the steel bars in the building as claimed in claim 2, wherein said determining the first characteristic data and the first correlation coefficient of the diameter of the steel bars comprises:
determining a first correlation coefficient of the first characteristic data and the diameter of the steel bar by a Pearson correlation coefficient method.
4. The method for detecting the distance between the steel bars in the building as claimed in claim 1, wherein the collecting the cloud point data of the batch of steel bars comprises:
scanning the deformation image data of the steel bar through a ground three-dimensional laser scanner to obtain the point cloud picture data.
5. The method for detecting the distance between the reinforcing bars in the building as claimed in claim 4, wherein the scanning the deformation image data of the reinforcing bars by the ground three-dimensional laser scanner comprises:
and scanning deformation image data of the steel bar through a ground three-dimensional laser scanner under different incidence angles, different distances and different angular resolutions respectively.
6. The method of claim 1, wherein determining the spacing between the first and second rebars based on the first and second diameters comprises:
determining a first main shaft position of the first steel bar and a second main shaft position of the second steel bar according to the first diameter and the second diameter;
and determining the distance between the first steel bar and the second steel bar according to the first main shaft position and the second main shaft position.
7. A system for detecting the distance between reinforcing bars in a building is characterized by comprising:
the acquisition module is used for acquiring point cloud picture data and parameter data of the batches of reinforcing steel bars; the parameter data comprises material strength, surface color and surface roughness of the steel bar;
the first input module is used for inputting the point cloud picture data into a machine learning model and extracting various first feature data of the point cloud picture data;
the second input module is used for inputting the parameter data into a machine learning model and extracting various second feature data of the parameter data;
the feature selection module is used for selecting features of the machine learning model according to the first feature data and the second feature data;
the prediction module is used for predicting a first diameter of the first steel bar and a second diameter of the second steel bar through the machine learning model after the characteristic selection; the first and second rebars are adjacent;
and the detection module is used for determining the distance between the first steel bar and the second steel bar according to the first diameter and the second diameter.
8. The utility model provides a detection apparatus for reinforcing bar interval in building which characterized in that includes:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to implement the method of detecting a rebar spacing in a structure of any one of claims 1-6.
9. A computer-readable storage medium in which a program executable by a processor is stored, characterized in that: the processor executable program when executed by a processor is for implementing a method of detecting a reinforcement pitch in a structure according to any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110325983.6A CN113129271A (en) | 2021-03-26 | 2021-03-26 | Method, system and device for detecting steel bar spacing in building and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110325983.6A CN113129271A (en) | 2021-03-26 | 2021-03-26 | Method, system and device for detecting steel bar spacing in building and storage medium |
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106254754A (en) * | 2015-06-08 | 2016-12-21 | 奥林巴斯株式会社 | Filming apparatus, image processing apparatus, the control method of filming apparatus |
| CN111932508A (en) * | 2020-07-31 | 2020-11-13 | 山东大学 | Steel bar size measuring method and system based on image processing |
| CN112016638A (en) * | 2020-10-26 | 2020-12-01 | 广东博智林机器人有限公司 | Method, device and equipment for identifying steel bar cluster and storage medium |
| CN112287992A (en) * | 2020-10-26 | 2021-01-29 | 广东博智林机器人有限公司 | Reinforcing steel bar cluster classification method and device, electronic equipment and storage medium |
-
2021
- 2021-03-26 CN CN202110325983.6A patent/CN113129271A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106254754A (en) * | 2015-06-08 | 2016-12-21 | 奥林巴斯株式会社 | Filming apparatus, image processing apparatus, the control method of filming apparatus |
| CN111932508A (en) * | 2020-07-31 | 2020-11-13 | 山东大学 | Steel bar size measuring method and system based on image processing |
| CN112016638A (en) * | 2020-10-26 | 2020-12-01 | 广东博智林机器人有限公司 | Method, device and equipment for identifying steel bar cluster and storage medium |
| CN112287992A (en) * | 2020-10-26 | 2021-01-29 | 广东博智林机器人有限公司 | Reinforcing steel bar cluster classification method and device, electronic equipment and storage medium |
Non-Patent Citations (3)
| Title |
|---|
| 曲金辉: "混凝土中钢筋保护层厚度检测准确性分析", 《上海建设科技》 * |
| 王瑞瑞 等: "基于多源遥感数据的输电线走廊树种分类", 《农业机械学报》 * |
| 颜培岩 等: "探地雷达技术在暗挖隧道钢筋间距检测中的正演模拟与应用", 《ＳＰＥＣＩＡＬ ＳＴＲＵＣＴＵＲＥＳ》 * |
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