Rebar weld inspection method and device

Abstract

A rebar weld inspection method, comprising: acquiring first point cloud data from an inspected area, wherein the first point cloud data comprises a plurality of pieces of discrete point data, and the discrete point data comprises color values and plane coordinates (S101); according to the first point cloud data, determining a plurality of contour points of a weld to be subjected to inspection (S102); obtaining size information of said weld on the basis of plane coordinates respectively corresponding to the plurality of contour points, wherein the size information comprises the length and width of said weld (S103); and if the size information meets a preset condition, determining that said weld is a qualified weld (S104). Thus, the quantitative inspection for a weld can be realized, thereby improving the inspection efficiency of the weld. Further disclosed are a rebar weld inspection device, an electronic device and a computer-readable storage medium.

Description

A steel bar weld detection method and device Technical Field

[0001] The present application relates to the technical field of weld detection, and in particular to a method and equipment for detecting steel bar welds. Background Art

[0002] Rebar, a type of steel used to make reinforced concrete and prestressed reinforced concrete, is particularly common on construction sites and is widely used in various building structures.

[0003] When using rebar on-site, it is necessary to construct it into a cage or other specific shapes. During this construction process, the rebar undergoes additional welding to ensure the final product is reliable and secure, and to avoid safety incidents. Therefore, it is essential to inspect the welds after welding. Currently, the most common method for inspecting welds is manual inspection, where workers visually inspect and measure with a ruler to determine the weld's reliability.

[0004] However, different people may have different judgments on weld quality, making it difficult to form a quality standard that can be quantified for testing, resulting in low testing efficiency.

[0005] Therefore, there is an urgent need for an efficient and accurate steel bar weld detection method and equipment. Summary of the Invention

[0006] The present application provides a steel bar weld detection method and device, which can realize quantitative detection of welds, thereby improving the detection efficiency of welds.

[0007] In the first aspect, the present application provides a steel bar weld detection method, which is applied to weld detection equipment, and the method includes: obtaining first point cloud data of the detection area, the first point cloud data including multiple discrete point data, and the discrete point data including color values ​​and plane coordinates; determining multiple contour points of the weld to be detected based on the first point cloud data; obtaining size information of the weld to be detected based on the plane coordinates corresponding to each of the multiple contour points, the size information including the length and width of the weld to be detected; if the size information meets preset conditions, determining that the weld to be detected is a qualified weld.

[0008] By adopting this technical solution, the weld inspection equipment determines the outer contour of the weld to be inspected based on the point cloud data of the inspection area, thereby determining the position and outer contour of the weld to be inspected. The dimensional information of the weld to be inspected is then obtained based on the point set on the outer contour. This saves labor costs in the large-scale weld inspection process and reduces misjudgments caused by manual inspection. Finally, the dimensional information is compared with preset conditions to determine whether the weld to be inspected is qualified, thus standardizing quality standards and making weld inspection simple and efficient.

[0009] In a second aspect, the present application provides a steel bar weld detection device, which includes a detection module and a data processing module, wherein:

[0010] The detection module is configured to obtain first point cloud data of a detection area, wherein the first point cloud data includes a plurality of discrete point data, and the discrete point data includes color values ​​and plane coordinates;

[0011] The data processing module is used to determine multiple contour points of the weld to be inspected based on the first point cloud data; obtain size information of the weld to be inspected based on the plane coordinates corresponding to each of the multiple contour points, the size information including the length and width of the weld to be inspected; if the size information meets a preset condition, determine that the weld to be inspected is a qualified weld.

[0012] By adopting this technical solution, the detection module obtains the outer contour of the weld to be inspected based on the point cloud data of the inspection area, thereby determining the position and outer contour of the weld to be inspected. The data processing module then obtains the dimensional information of the weld to be inspected based on the point set on the outer contour. This saves labor costs in the large-scale weld inspection process and reduces misjudgments caused by manual inspection. Finally, the dimensional information is compared with preset conditions to determine whether the weld to be inspected is qualified, thus unifying quality standards and making weld inspection simple and efficient.

[0013] Optionally, before obtaining the point cloud data of the detection area, it specifically includes: a detection module is used to obtain second point cloud data of the initial area, the second point cloud data includes multiple initial discrete point data; a data processing module is used to determine the center position information corresponding to each of a plurality of preset marks based on the second point cloud data, the preset mark is a mark point of a preset shape composed of multiple identification discrete points, and the plurality of preset marks is at least 3; the detection area is obtained according to the center position information corresponding to each of the plurality of preset marks.

[0014] By adopting the above technical solution, since the initial area is large, when the detection module identifies the detection area where the weld to be detected is located from the initial area, the position of the detection area is determined by identifying the marking points of a preset shape pre-set in the initial area; subsequently, only the point cloud data within the detection area needs to be processed, thereby simplifying the complexity of data processing and shortening the data processing time.

[0015] Optionally, the detection module is used to compare the color values ​​corresponding to each of the multiple initial discrete point data with the preset color values ​​to obtain the multiple identification discrete points that constitute the preset shape; the data processing module is used to fit the plane coordinates of the multiple identification discrete points to obtain the contour lines of the multiple preset shapes; based on the contour lines of the multiple preset shapes, determine the center position information corresponding to each of the multiple preset marks.

[0016] By adopting the above technical solution, since the initial area contains not only the preset marking points and welds, but also other production tools, the marking points are set to preset shapes. When identifying the marking points, the preset shapes are recognized to determine the exact position of each marking point, thereby obtaining the accurate position of the detection area, reducing the impact of external factors on the detection.

[0017] Optionally, the detection module is used to obtain multiple sets of point cloud data within the detection area, and the multiple sets of point cloud data are point cloud data collected in the same environment at different time points; the data processing module is used to perform point cloud registration on the plane coordinates of the third point cloud data according to the reference point cloud data to obtain registered point cloud data, and the third point cloud data is any one of the multiple sets of point cloud data, and the reference point cloud data is any one of the multiple sets of point cloud data except the third point cloud data; the average value of the reference point cloud data and the registered point cloud data is calculated to obtain the first point cloud data.

[0018] By adopting the above technical solution, when acquiring point cloud data of the inspection area, multiple acquisitions are required to reduce outliers in the collected data. At this time, the detection module performs point cloud registration on the multiple acquired data with the reference point cloud data, thereby obtaining accurate point cloud data with fewer outliers, thereby improving the accuracy of weld detection.

[0019] Optionally, the data processing module is used to obtain the color value of the sample discrete point data, where the sample discrete point data is any one of the multiple discrete point data; if the color value of the sample discrete point data is within a preset weld color value range, the sample discrete point is determined to be the contour point of the weld to be detected.

[0020] By adopting the above technical solution, the data processing module compares the color value of the sample discrete point with the preset weld color value range, thereby determining whether the discrete point is the contour point of the weld to be detected; at this time, the accuracy of weld size detection is improved.

[0021] Optionally, the data processing module is used to perform function fitting on the plane coordinates corresponding to the multiple contour points to obtain a fitting curve; based on the fitting curve, the outer contour line of the weld to be detected is determined; and based on the outer contour line, the size information of the weld to be detected is determined.

[0022] By adopting the above technical solution, multiple contour points of the weld to be inspected are fitted into a curve, and then the size information of the weld to be inspected is obtained based on the point set on the fitting curve, thereby reducing the impact of the error caused by the contour points on the judgment result.

[0023] Optionally, when the length of the weld to be detected is within a preset length range and the width of the weld to be detected is within a preset width range, the weld to be detected is determined to be a qualified weld.

[0024] By adopting the above technical solution, the quality standards of weld inspection are unified, thereby improving the efficiency of weld inspection.

[0025] In a third aspect, the present application provides an electronic device comprising a processor, a memory, a user interface, and a network interface, wherein the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes a method as described in any one of the first aspects.

[0026] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores instructions, and when the instructions are executed, the method as described in any one of the first aspects is executed.

[0027] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0028] 1. The weld inspection equipment obtains the outer contour of the weld to be inspected based on the point cloud data of the inspection area, thereby determining the position and outer contour of the weld to be inspected. The weld's dimensional information is then obtained based on the point set on the outer contour. This saves labor costs and reduces misjudgments caused by manual inspection during large-scale weld inspections. Finally, the dimensional information is compared with preset conditions to determine whether the weld to be inspected is qualified, thus unifying quality standards and making weld inspection simple and efficient.

[0029] 2. Since the initial area contains not only preset marking points and welds, but also other production equipment, at this time, by setting the marking points to preset shapes, the preset shapes are identified when identifying the marking points, thereby determining the exact position of each marking point and obtaining the exact position of the detection area, thereby reducing the detection impact caused by external factors. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] FIG1 is a schematic structural diagram of a weld detection device provided in an embodiment of the present application.

[0031] FIG2 is a flow chart of a weld detection method provided in an embodiment of the present application.

[0032] FIG3 is a schematic diagram of a flow chart for dividing weld inspection areas provided in an embodiment of the present application.

[0033] FIG4 is a schematic structural diagram of a weld detection device provided in an embodiment of the present application.

[0034] FIG5 is a schematic structural diagram of an electronic device disclosed in an embodiment of the present application.

[0035] Explanation of the accompanying drawings: 1. Detection module; 2. Data processing module; 3. Electric pan-tilt head; 500. Electronic device; 501. Processor; 502. Communication bus; 503. User interface; 504. Network interface; 505. Memory. DETAILED DESCRIPTION

[0036] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0037] In the description of the embodiments of this application, words such as "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "for example" or "for instance" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "for example" or "for instance" is intended to present the relevant concepts in a concrete manner.

[0038] In the description of the embodiments of the present application, the term "multiple" means two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0039] Rebar refers to the steel used in reinforced concrete and prestressed concrete. It's particularly common on construction sites and is widely used in various building structures, particularly large, heavy, lightweight, thin-walled, and high-rise structures. When used on-site, the rebar must be constructed into cages or other specialized shapes, requiring additional welding. To ensure the finished product is secure and reliable and to avoid potential safety incidents, it's essential to inspect the welds after welding.

[0040] Currently, the most common weld inspection method is manual inspection, where workers determine weld reliability through visual inspection and ruler measurement. However, manual inspection is prone to misjudgments, omissions, and low efficiency. Furthermore, different people may have different judgments, making it difficult to establish quantifiable quality standards, resulting in low inspection efficiency.

[0041] Before describing the embodiments of this application, this application provides a schematic diagram of the structure of a weld inspection device. As shown in Figure 1, the weld inspection device includes a detection module 1, a data processing module 2, and a motorized pan / tilt head 3. The detection module 1 uses a 3D laser depth camera to collect digital data about objects within the detection range; the data processing module 2 is used to process the collected data, operate the motorized pan / tilt head 3, extract weld information, determine the information, and display the results; the motorized pan / tilt head 3 is used to fix the detection device and can rotate at multiple angles to optimize the detection angle, allowing it to be installed on the manipulator arm of different welding robots.

[0042] In order to solve the above problems, the present application provides a steel bar weld detection method, which uses weld detection equipment. As shown in FIG2 , the method includes steps S101 to S104 .

[0043] S101. Acquire first point cloud data of a detection area, where the first point cloud data includes a plurality of discrete point data, and the discrete point data includes color values ​​and plane coordinates.

[0044] In the above steps, after welding is completed, the worker secures the steel bar to be inspected on the inspection table. At this point, data processing module 2 adjusts motorized pan / tilt platform 3 based on the point cloud data collected by detection module 1, ensuring that detection module 1's detection area covers the weld to be inspected and is centered in the middle of the detection area, thereby achieving the optimal detection angle. At this point, detection module 1 acquires point cloud data for the initial area.

[0045] After acquiring the point cloud data for the initial region, to simplify data processing and shorten processing time, the detection module 1 acquires the point cloud data for the initial region. Point cloud data can be understood as a collection of point data on the product's surface obtained using a measuring instrument in reverse engineering. Therefore, the point cloud data for the initial region includes multiple initial discrete points, each of which includes its color value and plane coordinates. Next, based on the point cloud data for the initial region, the data processing module 2 compares the color values ​​corresponding to each of the initial discrete points with preset color values ​​to obtain multiple identifying discrete points that form a preset shape. The data processing module 2 then performs fitting based on the plane coordinates of the multiple identifying discrete points to obtain multiple contour lines of the preset shape. Finally, based on the contour lines of the preset shape, the center position information corresponding to each of the multiple preset markers in the initial region is calculated. In this case, the detection region is the area formed by connecting the center positions of the multiple preset markers. This process removes redundant blank areas from the initial region, thereby reducing the amount of data to be processed and accelerating data processing. The multiple preset markers can be understood as marking points of a preset shape formed by multiple discrete identification points, where the discrete identification points are part of the multiple initial discrete points. The center position information can be the center of gravity of the preset shape. The number of preset markers is at least three.

[0046] For example, as shown in Figure 3, Figure a is the initial area, which includes steel bars, welds, base plates, and production equipment. At this time, the base plates and production equipment in Figure a can cause misjudgment of weld identification during data processing. Therefore, in Figure b, the weld inspection equipment processes the point cloud data of the initial area to obtain four preset markers: A, B, C, and D. Then, as shown in Figure c, the four preset markers are curve-fitted to obtain their respective outer contours. Finally, as shown in Figure d, the center positions of the four preset markers are connected to obtain Area 2, which is the inspection area.

[0047] In the process of identifying the preset mark, the preset shape can be a circle, a square or a polygon, which is not limited in this application. Since the initial area includes not only the weld to be detected, but also other production tools such as detection equipment and base plates, and the colors, positions and shapes of these production tools are different, the production tools will cause errors in the recognition of the detection module 1. Therefore, by setting a preset shape, in the recognition process of the detection module 1, a plurality of initial discrete points constituting the preset shape are used as marks to determine the detection area containing the weld to be detected and with fewer production tools. In this process, points are no longer used as marks, but preset shapes are used as marks, which not only increases the recognition of the mark, but also greatly reduces the possibility of misjudgment of the mark point.

[0048] S102: Determine a plurality of contour points of the weld to be inspected based on the first point cloud data.

[0049] In the above steps, after obtaining the point cloud data of the detection area, the color value of the sample discrete point data is compared with the preset weld color value range. If the color value of the sample discrete point data is within the preset weld color value range, the sample discrete point is determined to be the contour point of the weld to be detected. Among them, the sample discrete point is any one of the multiple discrete point data in the detection area, and the color value of the sample discrete point can be understood as the RGB value of the discrete point. In this way, the approximate shape of the weld to be detected is obtained. Of course, since the discrete point data not only includes color values, but also includes point information such as reflectivity and intensity information. Therefore, when the accuracy requirements of the weld are high, in order to obtain accurate contour points, more dimensional comparisons can be performed. The specific judgment criteria depend on the actual situation and will not be elaborated here.

[0050] S103 , based on the plane coordinates corresponding to the plurality of contour points, obtaining size information of the weld to be detected, where the size information includes the length and width of the weld to be detected.

[0051] In the above steps, since the contour points of the weld to be inspected only provide a rough outline of its shape, to obtain a more accurate weld dimension, a curve fitting is performed on the plane coordinates corresponding to each of the multiple contour points, thereby generating a fitted curve. During this process, if the plane coordinates of a contour point do not lie on the fitted curve, the contour point is projected onto the fitted curve. The fitted curve then represents the outer contour of the weld to be inspected. Curve fitting can employ spline curve fitting or polynomial fitting. Since curve fitting is a conventional technique for those skilled in the art, it will not be further described in this embodiment.

[0052] In order to determine the size information of the weld to be inspected, the weld inspection equipment calculates the coordinate position of each contour point on the fitting curve according to the equation of the fitting curve, thereby calculating the width and length of the weld. The width is the distance between the two points of the weld to be inspected that are farthest apart in the width direction, and the length is the distance between the two points of the weld to be inspected that are farthest apart in the length direction.

[0053] S104: If the size information meets the preset conditions, determine that the weld to be inspected is a qualified weld.

[0054] In the above steps, the pre-set conditions can be understood as the engineering quality acceptance criteria for welds. Different rebars have different welding requirements. For example, for truss rebar, the engineering quality acceptance criteria for welds are: a weld length of 5d for double-sided welds and 10d for single-sided welds, with an error range of -5mm to 5mm. For example, if the rebar diameter d is 30mm and single-sided welding is used, the weld length requirement is 300mm. If the weld length is 299mm, the weld is considered acceptable.

[0055] In a possible embodiment, obtaining the first point cloud data of the detection area specifically also includes: obtaining multiple groups of point cloud data in the area to be detected, the multiple groups of point cloud data are point cloud data collected in the same environment at different time points; performing point cloud registration on the plane coordinates of the third point cloud data according to the reference point cloud data to obtain registered point cloud data, the third point cloud data is any one of the multiple groups of point cloud data, and the reference point cloud data is any one of the multiple groups of point cloud data except the third point cloud data; calculating the average value of the reference point cloud data and the registered point cloud data to obtain the first point cloud data.

[0056] Specifically, when acquiring point cloud data for the inspection area, multiple acquisitions are necessary to reduce outliers in the collected data. Even under the same environment, point cloud data collected at different times can still differ. For example, point cloud data collected at a first moment includes a first discrete point, while point cloud data collected at a second moment includes a second discrete point. The first and second discrete points are both located at the same position, and the second moment occurs after the first moment. However, due to measurement errors in the detection module 1, there is a discrepancy between the positions of the first and second discrete points. In this case, the position of the second discrete point is replaced by another discrete point, resulting in measurement errors. Therefore, the detection module 1 performs point cloud registration on the plane coordinates of the third point cloud data based on the reference point cloud data to obtain registered point cloud data. The reference point cloud data can be any of the multiple sets of collected point cloud data. The third point cloud data is the point cloud data that requires point cloud registration. After all point cloud data are registered, the average of the reference point cloud data and the registered point cloud data is calculated to obtain the point cloud data for the inspection area. In this process, there are fewer outliers in the point cloud data and the data is accurate, which improves the accuracy of weld detection.

[0057] The present application provides a steel bar weld detection device, as shown in FIG4 , which includes a detection module 1 and a data processing module 2 , wherein:

[0058] Detection module 1, used to obtain first point cloud data of the detection area, the first point cloud data including a plurality of discrete point data, the discrete point data including color values ​​and plane coordinates;

[0059] Data processing module 2 is used to determine multiple contour points of the weld to be inspected based on the first point cloud data; obtain size information of the weld to be inspected based on the plane coordinates corresponding to each of the multiple contour points, the size information including the length and width of the weld to be inspected; if the size information meets the preset conditions, the weld to be inspected is determined to be a qualified weld.

[0060] In a possible embodiment, before obtaining the point cloud data of the detection area, it specifically includes: the detection module 1 is used to obtain the second point cloud data of the initial area, and the second point cloud data includes a plurality of initial discrete point data; the data processing module 2 is used to determine the center position information corresponding to each of the plurality of preset marks based on the second point cloud data, the preset mark is a mark point of a preset shape composed of a plurality of identification discrete points, and the plurality of preset marks is at least 3; the detection area is obtained according to the center position information corresponding to each of the plurality of preset marks.

[0061] In one possible embodiment, the detection module 1 is used to compare the color values ​​corresponding to each of the multiple initial discrete point data with the preset color values ​​to obtain multiple identification discrete points that constitute a preset shape; the data processing module 2 is used to fit the plane coordinates of the multiple identification discrete points to obtain the contour lines of multiple preset shapes; based on the contour lines of the multiple preset shapes, determine the center position information corresponding to each of the multiple preset marks.

[0062] In one possible embodiment, the detection module 1 is used to obtain multiple sets of point cloud data in the area to be detected, and the multiple sets of point cloud data are point cloud data collected in the same environment at different time points; the data processing module 2 is used to perform point cloud registration on the plane coordinates of the third point cloud data according to the reference point cloud data to obtain registered point cloud data, the third point cloud data is any one of the multiple sets of point cloud data, and the reference point cloud data is any one of the multiple sets of point cloud data except the third point cloud data; the average value of the reference point cloud data and the registered point cloud data is calculated to obtain the first point cloud data.

[0063] In one possible embodiment, the data processing module 2 is used to obtain the color value of the sample discrete point data, where the sample discrete point data is any one of multiple discrete point data; if the color value of the sample discrete point data is within a preset weld color value range, the sample discrete point is determined to be the contour point of the weld to be detected.

[0064] In one possible implementation, the data processing module 2 is used to perform function fitting on the plane coordinates corresponding to the multiple contour points to obtain a fitting curve; based on the fitting curve, the outer contour line of the weld to be detected is determined, and the size information of the weld to be detected is determined based on the outer contour line.

[0065] In a possible implementation, when the length of the weld to be detected is within a preset length range and the width of the weld to be detected is within a preset width range, the weld to be detected is determined to be a qualified weld.

[0066] It should be noted that the above embodiments provide devices that implement their functions using only the division of the above functional modules as examples. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiments are based on the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0067] The present application also provides an electronic device. Referring to FIG5 , FIG5 is a schematic diagram of the structure of an electronic device disclosed in an embodiment of the present application. The electronic device 500 may include: at least one processor 501 , at least one network interface 504 , a user interface 503 , a memory 505 , and at least one communication bus 502 .

[0068] The communication bus 502 is used to implement the connection and communication between these components.

[0069] The user interface 503 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 503 may also include a standard wired interface and a wireless interface.

[0070] The network interface 504 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0071] The processor 501 may include one or more processing cores. The processor 501 utilizes various interfaces and circuits to connect various components within the server. It executes instructions, programs, code sets, or instruction sets stored in the memory 505, as well as accesses data stored in the memory 505, to perform various server functions and process data. Optionally, the processor 501 may be implemented using at least one of the following hardware forms: a digital signal processing (DSP), a field-programmable gate array (FPGA), or a programmable logic array (PLA). The processor 501 may integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU primarily processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing content displayed on the display screen; and the modem handles wireless communications. It is understood that the modem may also be implemented as a separate chip, rather than integrated into the processor 501.

[0072] Memory 505 may include random access memory (RAM) or read-only memory (ROM). Optionally, memory 505 may include non-transitory computer-readable storage medium. Memory 505 may be used to store instructions, programs, code, code sets, or instruction sets. Memory 505 may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, sound playback function, image playback function, etc.), instructions for implementing each of the aforementioned method embodiments, and the data storage area may store data related to each of the aforementioned method embodiments. Memory 505 may also optionally be at least one storage device located remotely from the processor 501. Referring to Figure 5 , memory 505, as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for a rebar weld detection method.

[0073] In the electronic device 500 shown in FIG5 , the user interface 503 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 501 can be used to call an application program storing a steel bar weld detection method in the memory 505. When executed by one or more processors 501, the electronic device 500 executes one or more of the methods described in the above embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should know that this application is not limited by the described order of actions, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for this application.

[0074] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0075] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely schematic, such as the division of units, which is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interface, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0076] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0077] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0078] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of this application, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the method of this application. The aforementioned memory includes various media that can store program code, such as USB flash drives, mobile hard drives, magnetic disks, or optical disks.

[0079] The foregoing is merely an exemplary embodiment of the present disclosure and is not intended to limit the scope of the present disclosure. In other words, any equivalent variations and modifications made in accordance with the teachings of the present disclosure are still within the scope of the present disclosure. Those skilled in the art will readily conceive of other embodiments of the present disclosure after considering the disclosure and the practical implications thereof.

[0080] This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art not described herein. The description and examples are to be considered as exemplary only, and the scope and spirit of the present disclosure are to be defined by the claims.

Claims

1. A steel bar weld detection method, characterized in that: Applied to weld detection equipment, the method comprises: Acquire first point cloud data of the detection area, wherein the first point cloud data includes a plurality of discrete point data, and the discrete point data includes color values ​​and plane coordinates; Determine a plurality of contour points of the weld to be inspected according to the first point cloud data; Based on the plane coordinates corresponding to each of the plurality of contour points, the size information of the weld to be detected is obtained, wherein the size information includes the length and width of the weld to be detected; If the size information meets the preset conditions, the weld to be inspected is determined to be a qualified weld.

2. The method according to claim 1, characterized in that Before acquiring the point cloud data of the detection area, the method includes: Acquire second point cloud data of the initial area, wherein the second point cloud data includes a plurality of initial discrete point data; Based on the second point cloud data, determine the center position information corresponding to each of a plurality of preset marks, wherein the preset marks are mark points of a preset shape formed by a plurality of identification discrete points, and the plurality of the preset marks is at least 3; The detection area is obtained according to the central position information corresponding to each of the plurality of preset marks.

3. The method according to claim 2, characterized in that The determining, based on the second point cloud data, the center position information of the plurality of preset marks comprises: Compare the color values ​​corresponding to the plurality of initial discrete point data with the preset color values ​​to obtain the plurality of identification discrete points constituting the preset shape; Fitting the plane coordinates of the plurality of identified discrete points to obtain the contour lines of the plurality of preset shapes; Based on the contour lines of the plurality of preset shapes, the center position information corresponding to each of the plurality of preset marks is determined.

4. The method according to claim 1, characterized in that The obtaining of first point cloud data of the detection area comprises: Acquire multiple sets of point cloud data in the detection area, wherein the multiple sets of point cloud data are point cloud data collected at different time points in the same environment; Performing point cloud registration on the plane coordinates of the third point cloud data according to the reference point cloud data to obtain registered point cloud data, wherein the third point cloud data is any one of the multiple groups of point cloud data, and the reference point cloud data is any one of the multiple groups of point cloud data except the third point cloud data; An average value of the reference point cloud data and the registration point cloud data is calculated to obtain the first point cloud data.

5. The method according to claim 1, characterized in that Determining a plurality of contour points of the weld to be inspected according to the first point cloud data comprises: Obtaining a color value of a sample discrete point data, wherein the sample discrete point data is any one of the plurality of discrete point data; If the color value of the sample discrete point data is within a preset weld color value range, the sample discrete point is determined to be a contour point of the weld to be detected.

6. The method according to claim 1, characterized in that The obtaining of the size information of the weld to be detected based on the plane coordinates corresponding to each of the plurality of contour points comprises: Performing function fitting on the plane coordinates corresponding to each of the plurality of contour points to obtain a fitting curve; Determining the outer contour line of the weld to be inspected according to the fitting curve; The size information of the weld to be inspected is determined based on the outer contour line.

7. The method according to claim 1, characterized in that If the size information satisfies a preset condition, determining that the weld to be inspected is a qualified weld includes: When the length of the weld to be detected is within a preset length range, and the width of the weld to be detected is within a preset width range, the weld to be detected is determined to be a qualified weld.

8. A steel bar weld detection device, characterized in that: It comprises a detection module (1) and a data processing module (2), wherein: The detection module (1) is used to obtain first point cloud data of the detection area, the first point cloud data includes a plurality of discrete point data, and the discrete point data includes color values ​​and plane coordinates; The data processing module (2) is used to determine a plurality of contour points of the weld to be detected based on the point cloud data; obtain size information of the weld to be detected based on the plane coordinates corresponding to each of the plurality of contour points, the size information including the length and width of the weld to be detected; and determine that the weld to be detected is a qualified weld if the size information meets a preset condition.

9. An electronic device, characterized in that: The electronic device (500) comprises a processor (501), a memory (505), a user interface (503) and a network interface (504), wherein the memory (505) is used to store instructions, the user interface (503) and the network interface (504) are used to communicate with other devices, and the processor (501) is used to execute the instructions stored in the memory (505) so that the electronic device (500) executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 7 is performed.

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