Recognition method, device and equipment for steel bar binding point position and storage medium

Abstract

The invention provides a reinforcing steel bar binding point position identification method, device and equipment and a storage medium, and the method comprises the steps: obtaining the image data, collected by an industrial camera, of a reinforcing steel bar binding region, and the point cloud data, collected by a laser radar, of the reinforcing steel bar binding region, fusing the image data and the point cloud data, and obtaining a reinforcing steel bar binding point position identification result. Obtaining a first-layer steel bar binding point and a point cloud cluster around the first-layer steel bar binding point according to the features of the fused data; thirdly, fitting the three-dimensional coordinates and the normal vectors of the first-layer steel bar binding points, verifying the authenticity of the steel bar binding points based on the average depth value of the central cross point, the average depth value of the transverse steel bars and the average depth value of the longitudinal steel bars, and storing the position information of the first-layer steel bar binding points meeting the conditions; space shielding and shadow interference are overcome, and accurate recognition of the first-layer steel bar binding point is achieved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent construction technology, and in particular to a method, device, equipment and storage medium for identifying steel bar binding points. Background Art

[0002] In modern construction, the automation of steel bar tying is a key link to improve construction efficiency and quality. As building structures become increasingly complex, the density of steel bar arrangement continues to increase. In terms of steel bar tying point identification, especially in multi-layer steel bar grid structures, the three-dimensional spatial position and normal vector of the tying point cannot be accurately located due to the occlusion and shadow effect between the steel bars. This spatial complexity leads to a difficult technical problem: how to accurately identify and precisely locate the first-floor steel bar tying points in a complex environment with multi-level and high-density steel bars.

[0003] In view of this, this application is filed. Summary of the invention

[0004] The invention discloses a method, device, equipment and storage medium for identifying steel bar binding points, aiming to overcome space occlusion and shadow interference to achieve accurate identification of first-floor steel bar binding points.

[0005] The first embodiment of the present invention provides a method for identifying a steel bar binding point, comprising:

[0006] Acquire image data of the steel bar binding area collected by the industrial camera and point cloud data of the steel bar binding area collected by the laser radar, fuse the image data and point cloud data, and acquire the first-layer steel bar binding point and its surrounding point cloud clusters according to the characteristics of the fused data;

[0007] The three-dimensional coordinates and normal vectors of the first-layer steel bar binding points are fitted, and the authenticity of the steel bar binding points is verified based on the average depth value of the central intersection, the average depth value of the transverse steel bars, and the average depth value of the longitudinal steel bars, and the position information of the first-layer steel bar binding points that meet the conditions is saved.

[0008] Preferably, the step of obtaining the first-floor steel bar binding points and their surrounding point cloud clusters according to the characteristics of the fused data is specifically as follows:

[0009] Divide the image data into a plurality of grids, select a first-layer steel bar point in each of the grids, calculate the depth value of each first-layer steel bar point, remove the two depth values ​​with the largest and smallest depth values, and calculate the average value of the remaining depth values;

[0010] A depth value range is generated according to the average value and the error range, and the reinforcement binding points and their surrounding point cloud clusters within the depth value range are retained.

[0011] Preferably, the authenticity of the steel bar binding points is verified based on the average depth value at the center intersection, the average depth value of the transverse steel bars, and the average depth value of the longitudinal steel bars, and the position information of the first-layer steel bar binding points that meet the conditions is saved, specifically:

[0012] Calculate a first difference between the average depth value of the central intersection and the average depth value of the transverse reinforcement, and a second difference between the average depth value of the central intersection and the average depth value of the longitudinal reinforcement, and further subtract the first difference from the second difference to generate a third difference;

[0013] The authenticity range of the binding point is generated according to the steel bar diameter and the error range, and when it is determined that the third difference is within the authenticity range, the position information of the steel bar binding point is saved.

[0014] Preferably, before fusing the image data and the point cloud data and acquiring the first-layer steel bar binding points and the surrounding point cloud clusters according to the characteristics of the fused data, the method further includes:

[0015] The image data of the steel bar binding area is detected to generate the bounding box information and confidence of the steel bar binding point. The confidence is used to determine whether the acquired image information meets the requirements. If the requirements are met, the pixel coordinates of the steel bar binding point are retained.

[0016] Preferably, before acquiring the image data of the steel bar binding area collected by the industrial camera and the point cloud data of the steel bar binding area collected by the laser radar, the method further includes:

[0017] The relative positions of the industrial camera and the laser radar are jointly calibrated.

[0018] A second embodiment of the present invention provides a device for identifying steel bar binding points, comprising:

[0019] A data receiving unit is used to obtain image data of the steel bar binding area collected by the industrial camera and point cloud data of the steel bar binding area collected by the laser radar, fuse the image data and point cloud data, and obtain the first-layer steel bar binding point and its surrounding point cloud clusters according to the characteristics of the fused data;

[0020] A position information saving unit is used to fit the three-dimensional coordinates and normal vectors of the first-layer steel bar binding points, and verify the authenticity of the steel bar binding points based on the average depth value of the central intersection, the average depth value of the transverse steel bars, and the average depth value of the longitudinal steel bars, and save the position information of the first-layer steel bar binding points that meet the conditions.

[0021] The third embodiment of the present invention provides a device for identifying steel bar binding points, including a memory and a processor, wherein the memory stores a computer program, and the computer program can be executed by the processor to implement a method for identifying steel bar binding points as described in any one of the above.

[0022] The fourth embodiment of the present invention provides a computer-readable storage medium, characterized in that a computer program is stored therein, and the computer program can be executed by a processor of a device where the computer-readable storage medium is located to implement a method for identifying a steel bar binding point as described in any one of the above items.

[0023] Based on the method, device, equipment and storage medium for identifying steel bar binding points provided by the present invention, by acquiring image data of the steel bar binding area collected by an industrial camera and point cloud data of the steel bar binding area collected by a laser radar, the image data and the point cloud data are fused, and the first-layer steel bar binding points and the surrounding point cloud clusters are obtained according to the characteristics of the fused data; then, the three-dimensional coordinates and normal vectors of the first-layer steel bar binding points are fitted, and the authenticity of the steel bar binding points is verified based on the average depth value of the central intersection, the average depth value of the transverse steel bars, and the average depth value of the longitudinal steel bars, and the position information of the first-layer steel bar binding points that meet the conditions is saved, and the first-layer steel bar binding points can be accurately identified by overcoming spatial occlusion and shadow interference. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a schematic flow chart of a method for identifying steel bar binding points provided by the first embodiment of the present invention;

[0025] Figure 2 It is a schematic diagram of the overall identification process of the steel bar binding points provided by the present invention;

[0026] Figure 3 It is a module schematic diagram of a device for identifying steel bar binding points provided in the second embodiment of the present invention. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] In order to better understand the technical solution of the present invention, the embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0029] It should be clear that the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0030] The terms used in the embodiments of the present invention are only for the purpose of describing specific embodiments, and are not intended to limit the present invention. The singular forms "a", "said" and "the" used in the embodiments of the present invention and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings.

[0031] It should be understood that the term "and / or" used in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0032] The word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0033] The "first\second" mentioned in the embodiments is only to distinguish similar objects, and does not represent a specific order for the objects. It is understandable that the "first\second" can be interchanged with the specific order or sequence where permitted. It should be understood that the objects distinguished by "first\second" can be interchanged where appropriate, so that the embodiments described herein can be implemented in an order other than those illustrated or described herein.

[0034] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0035] The invention discloses a method, device, equipment and storage medium for identifying steel bar binding points, aiming to overcome space occlusion and shadow interference to achieve accurate identification of first-floor steel bar binding points.

[0036] See also Figure 1 and Figure 2 The first embodiment of the present invention provides a method for identifying a steel bar binding point, which can be performed by a steel bar binding point identification device (hereinafter referred to as the identification device), and in particular, is performed by one or more processors in the identification device to implement at least the following steps:

[0037] S101, acquiring image data of the steel bar binding area collected by the industrial camera and point cloud data of the steel bar binding area collected by the laser radar, fusing the image data and the point cloud data, and acquiring the first-layer steel bar binding point and its surrounding point cloud clusters according to the characteristics of the fused data;

[0038] In this embodiment, the identification device can be a terminal with data processing capabilities such as a server, workstation, notebook, desktop computer, single-chip microcomputer (such as STM32), which can establish wired or wireless communication with industrial cameras and lidars. The corresponding operating system and application software can be installed in the identification device, and the functions required by this embodiment can be realized through the combination of the operating system and application software.

[0039] It should be noted that before implementing this embodiment, the industrial camera and the laser radar can be fixedly installed at a specified position, and the two can be calibrated together. Specifically, the industrial camera and the laser radar can simultaneously capture the same calibration plate or calibration object. The calibration plate can contain regularly arranged dots, a checkerboard or a specific pattern, which is used to calculate the spatial coordinates during the calibration process.

[0040] The process of time synchronization: the recognition device can be used to send trigger signals to the industrial camera and the LiDAR at the same time to keep the sampling time of the two consistent. The synchronization frequency is 10Hz to ensure that the image taken by the industrial camera and the point cloud data collected by the LiDAR are strictly consistent in time.

[0041] In space calibration (camera internal and external calibration): first use the calibration plate to perform the internal calibration of the industrial camera to obtain the internal parameter data such as the focal length, principal point position, and distortion parameters of the camera. By shooting the calibration plate images at multiple angles and using the camera calibration algorithm (such as Zhang Zhengyou calibration method in the OpenCV library), the internal parameter matrix is ​​calculated. Next, the camera and lidar external parameter calibration: the external parameter calibration of the lidar and industrial camera is performed simultaneously using the calibration object. By matching the point cloud data collected by the lidar with the calibration plate image taken by the industrial camera, the transformation relationship between the camera coordinate system and the lidar coordinate system is determined. The process includes the calculation of the translation matrix and the plane rotation matrix, so as to achieve the alignment of the two coordinate systems. Through the calibration results, a conversion model between the industrial camera and the lidar coordinate system is established. This model can use the external parameter calibration results, including the rotation matrix and the translation vector, to map the lidar point cloud data to the camera image coordinate system, or vice versa. Through multiple calibration shooting and data collection, the accuracy of the calibration results of both are verified. The calibration parameters are further optimized and corrected according to the calibration error (such as reprojection error) to ensure that the error meets the requirements when the image and point cloud data are fused (usually the error needs to be controlled within the sub-pixel level).

[0042] Furthermore, in this embodiment, the steel bar binding area is photographed by an industrial camera to obtain high-resolution image data. The recognition device inputs these image data into a pre-trained target detection model, which can identify the binding points of the steel bars in the image. The detection model generates candidate steel bar binding point areas based on the feature points (i.e., steel bar intersections) in the image, and these areas are marked as bounding box information. Further, a confidence assessment is performed on each candidate steel bar binding point. The confidence is an evaluation value of the accuracy of the detection result, based on the degree of feature matching of the model to the area and the corresponding depth information. By comparing the confidence value of each steel bar binding point area with the set threshold, the recognition device can filter out high-confidence candidate points. When the confidence of a steel bar binding point is higher than the threshold, the system will retain and store the location information (including pixel coordinates) of the point as valid binding point data. If the confidence fails to meet the requirements, the system will discard the recognition result of the point.

[0043] Furthermore, in this embodiment, the collected image data is segmented, and the steel bar binding area is divided into multiple grids, and each grid corresponds to an independent image area. The recognition device can analyze and locate the steel bar binding points in different areas in a complex multi-layer steel bar structure separately. In each grid, the recognition device will first select a preliminary first-layer steel bar point, usually based on the visual features or position of the steel bars in the image. The preliminary selected steel bar points will be used for depth analysis. The recognition device calculates the depth value of each steel bar point by fusing the image and the point cloud data of the lidar. Taking the image divided into 16 grids as an example, each depth value is h i (i=1, 2, 3, ... 16);

[0044] The recognition device removes the maximum and minimum depth values ​​calculated in each grid to eliminate possible outliers or noise interference and ensure that the remaining depth data can reflect the actual depth of the steel bar points. Then, the recognition device averages the remaining depth values ​​to obtain the average depth value h of the steel bar points in the grid. T According to the average value h T and error range σ to set a depth value range (h T -σ,h T +σ). All the steel bar binding points within this depth range will be retained. At the same time, the recognition device will retain the point cloud cluster information around the binding point based on its characteristics.

[0045] S102, fitting the three-dimensional coordinates and normal vectors of the first-layer steel bar binding points, and verifying the authenticity of the steel bar binding points based on the average depth value of the central intersection, the average depth value of the transverse steel bars, and the average depth value of the longitudinal steel bars, and saving the position information of the first-layer steel bar binding points that meet the conditions.

[0046] It should be noted that in this embodiment, during the fitting process, the recognition device not only calculates the three-dimensional coordinates of the binding point, but also fits the normal vector of the binding point by analyzing the normal distribution of the point cloud. The normal vector reflects the spatial direction of the steel bar binding point, which can be calculated by analyzing the gradient changes of the surrounding point cloud. Furthermore, the normal vector of the steel bar binding point can be obtained by local plane fitting of the point cloud or other normal estimation algorithms, which can be used to determine the accurate position and posture of the steel bar.

[0047] Furthermore, in this embodiment, the average depth value of the center intersection is first calculated. By measuring the depth of the intersection position multiple times, the average depth value d1 is calculated. Then, the system will calculate the average depth value d2 of the transverse reinforcement except the center, and the average depth value d3 of the longitudinal reinforcement, and obtain the first difference Z1=d1-d2 by calculating the difference between the average depth value of the center intersection and the depth of the transverse reinforcement; similarly, by calculating the difference between the average depth value of the center intersection and the longitudinal reinforcement, the second difference Z2=d1-d3 is obtained.

[0048] In order to further verify the accuracy of the results, the first difference and the second difference are subtracted to obtain a third difference Z3 = Z1-Z2. The third difference reflects the depth difference between the central intersection and the horizontal and vertical steel bars, and identifies the spatial position characteristics of the steel bar binding point.

[0049] Based on the diameter of the steel bar d s and the preset error range σ, the recognition device will generate a range interval (d s -σ,d s +σ). When the third difference falls within the authenticity range, it indicates that the depth and position of the tying point meet the expected standards. The recognition device regards this tying point as a real tying point and saves its position information. If the third difference exceeds the range, the recognition device will determine that there may be errors or noise interference in the tying point and will not save it. In this way, the recognition device can ensure that the tying points identified in complex multi-layer steel bar structures have high accuracy.

[0050] See also Figure 3 The second embodiment of the present invention provides a device for identifying a steel bar binding point, comprising:

[0051] The data receiving unit 201 is used to obtain the image data of the steel bar binding area collected by the industrial camera and the point cloud data of the steel bar binding area collected by the laser radar, fuse the image data and the point cloud data, and obtain the first-layer steel bar binding point and its surrounding point cloud clusters according to the characteristics of the fused data;

[0052] The position information saving unit 202 is used to fit the three-dimensional coordinates and normal vectors of the first-layer steel bar binding points, and verify the authenticity of the steel bar binding points based on the average depth value of the central intersection, the average depth value of the transverse steel bars, and the average depth value of the longitudinal steel bars, and save the position information of the first-layer steel bar binding points that meet the conditions.

[0053] The third embodiment of the present invention provides a device for identifying steel bar binding points, including a memory and a processor, wherein the memory stores a computer program, and the computer program can be executed by the processor to implement a method for identifying steel bar binding points as described in any one of the above.

[0054] The fourth embodiment of the present invention provides a computer-readable storage medium, characterized in that a computer program is stored therein, and the computer program can be executed by a processor of a device where the computer-readable storage medium is located to implement a method for identifying a steel bar binding point as described in any one of the above items.

[0055] Based on the method, device, equipment and storage medium for identifying steel bar binding points provided by the present invention, by acquiring image data of the steel bar binding area collected by an industrial camera and point cloud data of the steel bar binding area collected by a laser radar, the image data and the point cloud data are fused, and the first-layer steel bar binding points and the surrounding point cloud clusters are obtained according to the characteristics of the fused data; then, the three-dimensional coordinates and normal vectors of the first-layer steel bar binding points are fitted, and the authenticity of the steel bar binding points is verified based on the average depth value of the central intersection, the average depth value of the transverse steel bars, and the average depth value of the longitudinal steel bars, and the position information of the first-layer steel bar binding points that meet the conditions is saved, and the first-layer steel bar binding points can be accurately identified by overcoming spatial occlusion and shadow interference.

[0056] Exemplarily, the computer program described in the third and fourth embodiments of the present invention may be divided into one or more modules, and the one or more modules are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program in the device for realizing a steel bar binding point identification. For example, the device described in the second embodiment of the present invention.

[0057] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the method for identifying the steel bar binding points, and uses various interfaces and lines to connect the various parts of the method for identifying the steel bar binding points.

[0058] The memory can be used to store the computer program and / or module, and the processor realizes various functions of a method for identifying a steel bar binding point by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, a text conversion function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, text message data, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Med ia Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0059] Wherein, if the implemented module is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0060] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0061] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for identifying steel bar binding points, characterized in that: include: Acquire image data of the steel bar binding area collected by the industrial camera and point cloud data of the steel bar binding area collected by the laser radar, fuse the image data and point cloud data, and obtain the first-layer steel bar binding point and its surrounding point cloud clusters according to the characteristics of the fused data; The three-dimensional coordinates and normal vectors of the first-layer steel bar binding points are fitted, and the authenticity of the steel bar binding points is verified based on the average depth value of the central intersection, the average depth value of the transverse steel bars, and the average depth value of the longitudinal steel bars, and the position information of the first-layer steel bar binding points that meet the conditions is saved.

2. A method for identifying steel bar binding points according to claim 1, characterized in that: The first-floor steel bar binding points and their surrounding point cloud clusters are obtained according to the characteristics of the fused data, specifically: Divide the image data into a plurality of grids, select a first-layer steel bar point in each of the grids, calculate the depth value of each first-layer steel bar point, remove the two depth values ​​with the largest and smallest depth values, and calculate the average value of the remaining depth values; A depth value range is generated according to the average value and the error range, and the reinforcement binding points and their surrounding point cloud clusters within the depth value range are retained.

3. A method for identifying steel bar binding points according to claim 1, characterized in that: The authenticity of the reinforcement binding points is verified based on the average depth value of the center intersection, the average depth value of the transverse reinforcement, and the average depth value of the longitudinal reinforcement, and the location information of the first-layer reinforcement binding points that meet the conditions is saved, specifically: Calculate a first difference between the average depth value of the central intersection and the average depth value of the transverse reinforcement, and a second difference between the average depth value of the central intersection and the average depth value of the longitudinal reinforcement, and further subtract the first difference from the second difference to generate a third difference; The authenticity range of the binding point is generated according to the steel bar diameter and the error range, and when it is determined that the third difference is within the authenticity range, the position information of the steel bar binding point is saved.

4. A method for identifying steel bar binding points according to claim 1, characterized in that: Before fusing the image data and the point cloud data, and obtaining the first-floor steel bar binding points and the surrounding point cloud clusters according to the characteristics of the fused data, it also includes: The image data of the steel bar binding area is detected to generate the bounding box information and confidence of the steel bar binding point. The confidence is used to determine whether the acquired image information meets the requirements. If the requirements are met, the pixel coordinates of the steel bar binding point are retained.

5. The method for identifying steel bar binding points according to claim 1, characterized in that: Before acquiring the image data of the steel bar binding area collected by the industrial camera and the point cloud data of the steel bar binding area collected by the laser radar, the method further includes: The relative positions of the industrial camera and the laser radar are jointly calibrated.

6. A device for identifying steel bar binding points, characterized in that: include: A data receiving unit is used to obtain image data of the steel bar binding area collected by the industrial camera and point cloud data of the steel bar binding area collected by the laser radar, fuse the image data and point cloud data, and obtain the first-layer steel bar binding point and its surrounding point cloud clusters according to the characteristics of the fused data; A position information saving unit is used to fit the three-dimensional coordinates and normal vectors of the first-layer steel bar binding points, and verify the authenticity of the steel bar binding points based on the average depth value of the central intersection, the average depth value of the transverse steel bars, and the average depth value of the longitudinal steel bars, and save the position information of the first-layer steel bar binding points that meet the conditions.

7. A device for identifying steel bar binding points, characterized in that: It comprises a memory and a processor, wherein the memory stores a computer program, and the computer program can be executed by the processor to implement a method for identifying a steel bar binding point as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: A computer program is stored, and the computer program can be executed by a processor of the device where the computer-readable storage medium is located to implement a method for identifying a steel bar binding point as described in any one of claims 1 to 5.

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