CN111179232A - Steel bar size detection system and method based on image processing - Google Patents
Steel bar size detection system and method based on image processing Download PDFInfo
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Abstract
The invention provides a steel bar size detection system and method based on image processing. Wherein, reinforcing bar size detecting system based on image processing includes: the image acquisition module is carried on the unmanned aerial vehicle and is configured to acquire construction site pictures; an image processing module configured to: detecting the edges of the steel bars in the construction site picture, and extracting an edge binary image; carrying out Hough line detection on the edge binary image, and fitting the outline edge of the steel bar; extracting the image containing the fitted steel bar outline edge again to remove shading impurities, then carrying out filtering and segmentation pretreatment, and finally detecting the steel bar outline size and detecting the number of pixel points in each outline; and (4) obtaining the real area of the steel bar according to the real-time position height conversion of the unmanned aerial vehicle, comparing the real area of the steel bar with the acceptance standard of the steel bar, and marking whether the steel bar is qualified.
Description
Technical Field
The invention belongs to the field of image processing, and particularly relates to a steel bar size detection system and method based on image processing.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
Traditional building site supervision acceptance method is mainly based on artificial vision detection, and supervision engineer patrols and examines the scope limited every day, and patrols and examines on the scene, measures and need climb building body building, and not only inefficiency, security and detection effect also are difficult to guarantee moreover, and the most serious problem is that there is the condition of privately lowering the acceptance criterion in enterprise and individual a bit, leads to the manual acceptance procedure to have the leak.
The inventor discovers that the machine vision detection technology related to the reinforcing steel bars in the existing building field is mainly applied to the quantity statistics and the weld appearance quality detection of bundled reinforcing steel bars, the size detection research of the reinforcing steel bars combined with an unmanned aerial vehicle is less, the main reason is that the unmanned aerial vehicle patrols and examines the high requirement on equipment, the construction site environment is complex, the color of single-layer reinforcing steel bars is very close to the background color of shading, and harmful interference and other noises exist, and the requirements are high for the accurate identification of the edges of the reinforcing steel bars. Even with methods similar to those used in research, many of the methods are too complex and redundant to be of widespread applicability.
Disclosure of Invention
In order to solve the problems, the invention provides a steel bar size detection system and method based on image processing, which combine automatic inspection of an unmanned aerial vehicle with computer vision and image processing and intelligently realize effective detection and identification of on-site steel bars.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention provides a steel bar size detection system based on image processing, which comprises:
the image acquisition module is carried on the unmanned aerial vehicle and is configured to acquire construction site pictures;
an image processing module configured to:
detecting the edges of the steel bars in the construction site picture, and extracting an edge binary image;
carrying out Hough line detection on the edge binary image, and fitting the outline edge of the steel bar;
extracting the image containing the fitted steel bar outline edge again to remove shading impurities, then carrying out filtering and segmentation pretreatment, and finally detecting the steel bar outline size and detecting the number of pixel points in each outline;
and (4) obtaining the real area of the steel bar according to the real-time position height conversion of the unmanned aerial vehicle, comparing the real area of the steel bar with the acceptance standard of the steel bar, and marking whether the steel bar is qualified.
In the image processing module, as an implementation manner, a Sobel edge detection algorithm of direct convolution is adopted to detect the edge of the steel bar in the construction site picture, and an edge binary image is extracted.
The technical scheme has the advantages that the color of the steel bar is very close to the background of the shading, in addition, the adverse interference and other noises exist, the effective extraction can be carried out on the edge only by combining a specific edge detection algorithm and a Hough straight line, the window traversal can be directly carried out on the source image just by directly carrying out the convolution Sobel edge detection, the neighborhood gradient amplitude value in the window is calculated, and the accuracy of the steel bar edge detection is improved.
In the image processing module, the fitted steel bar outline edge is extracted again by using a Grabcut target detection algorithm to remove shading impurities.
The technical scheme has the advantages that due to the influence of the background of the shading, a plurality of impurities exist around the outline, Grabcut target detection is just suitable for the scene, and the clear extracted outline foreground and background impurities can be separated.
In one embodiment, the image processing module pre-processes the image using mean filtering and partition allowances.
The technical scheme has the advantages that the accuracy of the detected pixel values of the steel bar outline can be improved through the conventional preprocessing of the mean filtering and the partition Otsu method.
As an implementation manner, the image processing module is further connected to a display module, and the display module is configured to display whether the steel bar is qualified.
In one embodiment, the image processing module is further connected with a remote monitoring terminal.
The technical scheme has the advantage that whether the size of the steel bar output by the image processing module is qualified or not is remotely monitored through the remote monitoring terminal.
The second aspect of the present invention provides a steel bar size detection method based on image processing, which includes:
receiving a construction site picture acquired by an image acquisition module carried by an unmanned aerial vehicle;
detecting the edges of the steel bars in the construction site picture, and extracting an edge binary image;
carrying out Hough line detection on the edge binary image, and fitting the outline edge of the steel bar;
extracting the image containing the fitted steel bar outline edge again to remove shading impurities, then carrying out filtering and segmentation pretreatment, and finally detecting the steel bar outline size and detecting the number of pixel points in each outline;
and (4) obtaining the real area of the steel bar according to the real-time position height conversion of the unmanned aerial vehicle, comparing the real area of the steel bar with the acceptance standard of the steel bar, and marking whether the steel bar is qualified.
As an implementation mode, a direct convolution Sobel edge detection algorithm is adopted to detect the edge of the steel bar in the construction site picture, and an edge binary image is extracted.
The technical scheme has the advantages that the color of the steel bar is very close to the background of the shading, in addition, the adverse interference and other noises exist, the effective extraction can be carried out on the edge only by combining a specific edge detection algorithm and a Hough straight line, the window traversal can be directly carried out on the source image just by directly carrying out the convolution Sobel edge detection, the neighborhood gradient amplitude value in the window is calculated, and the accuracy of the steel bar edge detection is improved.
And as an implementation mode, extracting the fitted steel bar outline edge again by using a Grabcut target detection algorithm to remove shading impurities.
The technical scheme has the advantages that due to the influence of the background of the shading, a plurality of impurities exist around the outline, Grabcut target detection is just suitable for the scene, and the clear extracted outline foreground and background impurities can be separated.
As one embodiment, the image is pre-processed using mean filtering and partition ontology.
The technical scheme has the advantages that the accuracy of the detected pixel values of the steel bar outline can be improved through the conventional preprocessing of the mean filtering and the partition Otsu method.
The invention has the beneficial effects that:
the invention realizes the contour extraction and size detection of the target reinforcing steel bar on the site of the construction site, the extracted edge and contour are very clear, and the size identification and positioning are accurate;
the invention can simply and reliably provide an implementation method and a theoretical basis for an engineering supervision project of intelligent steel bar size detection, compared with other methods, the invention can carry out contour extraction and size detection on single-layer steel bar pictures on cloudy scenes in most cases, and can also be well applied to the field of steel bar identification of construction sites or contour identification of similar scenes.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
Fig. 1 is a schematic structural diagram of a steel bar size detection system based on image processing according to an embodiment of the present invention;
FIG. 2(a) is a perspective view of a worksite provided by an embodiment of the present invention;
fig. 2(b) is an image of extracted single-layer steel bars to be detected according to the embodiment of the present invention;
FIG. 3 is a Sobel edge detection for direct convolution according to an embodiment of the present invention;
fig. 4 is hough line detection provided by an embodiment of the present invention;
fig. 5 is a graph of the detection effect of the Grabcut target provided by the embodiment of the present invention;
FIG. 6 shows the filtering effect of the mean filtering 3 × 3 kernel provided by the embodiment of the present invention;
FIG. 7 is a graph of the effects of Otsu threshold segmentation provided by embodiments of the present invention;
FIG. 8 illustrates a reinforcement contour detection provided by embodiments of the present invention;
FIG. 9 is a profile dimension result output provided by an embodiment of the present invention.
Detailed Description
The invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
Example one
Fig. 1 shows a schematic structural diagram of a steel bar size detection system based on image processing according to this embodiment.
The specific structure of the image processing-based steel bar size detection system according to the present embodiment will be described in detail below with reference to the accompanying drawings.
As shown in fig. 1, the system for detecting the size of a steel bar based on image processing of the present embodiment includes:
(1) the image acquisition module is mounted on the unmanned aerial vehicle and is configured to acquire construction site pictures.
In a specific implementation, the image acquisition module can be implemented by a camera. Fig. 2(a) is a panoramic view of the collected worksite. As shown in fig. 2(b), the extracted image of the single-layer steel bar to be detected is shown.
(2) An image processing module configured to:
a. detecting the edges of the steel bars in the construction site picture, and extracting an edge binary image as shown in fig. 3;
in specific implementation, a direct convolution Sobel edge detection algorithm is adopted to detect the edge of the steel bar in the construction site picture, and an edge binary image is extracted.
The technical scheme has the advantages that the color of the steel bar is very close to the background of the shading, in addition, the adverse interference and other noises exist, the effective extraction can be carried out on the edge only by combining a specific edge detection algorithm and a Hough straight line, the window traversal can be directly carried out on the source image just by directly carrying out the convolution Sobel edge detection, the neighborhood gradient amplitude value in the window is calculated, and the accuracy of the steel bar edge detection is improved.
The Sobel operator in the Sobel edge detection algorithm detects the edge according to the gray weighting difference of upper, lower, left and right adjacent points of the pixel point, and the phenomenon that the edge reaches an extreme value. The method has a smoothing effect on noise and provides more accurate edge direction information.
b. Carrying out Hough line detection on the edge binary image, and fitting the outline edge of the steel rib as shown in FIG. 4;
the basic principle of hough line detection is that, in the line detection task, the straight lines in the image space correspond to the points in the parameter space one by one, and the straight lines in the parameter space correspond to the points in the image space one by one, by using the duality of the points and the lines.
1) Each line in the image space is represented in the parameter space corresponding to a single point;
2) any part of line segments on the straight line in the image space correspond to the same point in the parameter space.
Therefore, the Hough line detection algorithm is used for converting the line detection problem in the image space into the detection problem of the point in the parameter space, and the line detection task is completed by searching the peak value in the parameter space.
The Hough line detection is to detect an object with a specific shape through a voting algorithm, a set which is in accordance with the specific shape is obtained in a parameter space by calculating the local maximum value of an accumulated result in the process and is used as a Hough transformation result, and the method can detect the shapes of circles, straight lines, ellipses and the like.
c. Extracting the image containing the fitted steel bar outline edge again to remove shading impurities, then carrying out filtering and segmentation pretreatment, and finally detecting the steel bar outline size and detecting the number of pixel points in each outline;
in specific implementation, the fitted steel bar outline edge is extracted again by using a Grabcut target detection algorithm to remove shading impurities, as shown in FIG. 5.
The Grabcut target detection algorithm utilizes texture (color) information and boundary (contrast) information in an image, and a good segmentation result can be obtained only through a small amount of user interaction operation.
The technical scheme has the advantages that due to the influence of the background of the shading, a plurality of impurities exist around the outline, Grabcut target detection is just suitable for the scene, and the clear extracted outline foreground and background impurities can be separated.
In a specific implementation, the image is pre-processed using mean filtering and partition Otsu.
The zonal population (Otsu) is an algorithm for determining an image segmentation threshold, which was proposed by the japanese scholars in 1979 and is also called the maximum inter-class variance method in principle. After binarization and segmentation are carried out according to a threshold value obtained by the Otsu method: the variance of the foreground and background images is maximum; the identified position is the optimal algorithm for selecting the threshold value in the image segmentation; the method is not influenced by the brightness and the contrast of the image, and is simple in calculation; and is widely applied to digital images. Mean filtering the filtering effect of the 3 × 3 kernel, as shown in fig. 6. Partition Otsu segmentation effect, as shown in FIG. 7.
The technical scheme has the advantages that the accuracy of the detected pixel values of the steel bar outline can be improved through the conventional preprocessing of the mean filtering and the partition Otsu method.
d. And (4) obtaining the real area of the steel bar according to the real-time position height conversion of the unmanned aerial vehicle, comparing the real area of the steel bar with the acceptance standard of the steel bar, and marking whether the steel bar is qualified. As shown in fig. 8, the steel bar outline detection result; figure 9 is the profile size output.
In another embodiment, the image processing module is further connected to a display module, and the display module is used for displaying whether the steel bar is qualified.
In particular, the display module may be a display screen.
In one embodiment, the image processing module is further connected with a remote monitoring terminal.
The technical scheme has the advantage that whether the size of the steel bar output by the image processing module is qualified or not is remotely monitored through the remote monitoring terminal.
The method realizes the contour extraction and size detection of the target reinforcing steel bar on the site of the construction site, the extracted edge and contour are very clear, and the size identification and positioning are accurate;
the method can simply and reliably provide an implementation method and a theoretical basis for an engineering supervision project of intelligent steel bar size detection, compared with other methods, the method can be used for carrying out contour extraction and size detection on single-layer steel bar pictures on cloudy sites in most cases, and can also be well applied to the field of steel bar identification on construction sites or contour identification of similar scenes.
Example two
The embodiment provides a steel bar size detection method based on image processing, which comprises the following steps:
step 1: receiving a construction site picture acquired by an image acquisition module carried by an unmanned aerial vehicle;
in a specific implementation, the image acquisition module can be implemented by a camera.
Step 2: detecting the edges of the steel bars in the construction site picture, and extracting an edge binary image;
in specific implementation, a direct convolution Sobel edge detection algorithm is adopted to detect the edge of the steel bar in the construction site picture, and an edge binary image is extracted.
The technical scheme has the advantages that the color of the steel bar is very close to the background of the shading, in addition, the adverse interference and other noises exist, the effective extraction can be carried out on the edge only by combining a specific edge detection algorithm and a Hough straight line, the window traversal can be directly carried out on the source image just by directly carrying out the convolution Sobel edge detection, the neighborhood gradient amplitude value in the window is calculated, and the accuracy of the steel bar edge detection is improved.
The Sobel operator in the Sobel edge detection algorithm detects the edge according to the gray weighting difference of upper, lower, left and right adjacent points of the pixel point, and the phenomenon that the edge reaches an extreme value. The method has a smoothing effect on noise and provides more accurate edge direction information.
And step 3: carrying out Hough line detection on the edge binary image, and fitting the outline edge of the steel bar;
the basic principle of hough line detection is that, in the line detection task, the straight lines in the image space correspond to the points in the parameter space one by one, and the straight lines in the parameter space correspond to the points in the image space one by one, by using the duality of the points and the lines.
1) Each line in the image space is represented in the parameter space corresponding to a single point;
2) any part of line segments on the straight line in the image space correspond to the same point in the parameter space.
Therefore, the Hough line detection algorithm is used for converting the line detection problem in the image space into the detection problem of the point in the parameter space, and the line detection task is completed by searching the peak value in the parameter space.
The Hough line detection is to detect an object with a specific shape through a voting algorithm, a set which is in accordance with the specific shape is obtained in a parameter space by calculating the local maximum value of an accumulated result in the process and is used as a Hough transformation result, and the method can detect the shapes of circles, straight lines, ellipses and the like.
And 4, step 4: extracting the image containing the fitted steel bar outline edge again to remove shading impurities, then carrying out filtering and segmentation pretreatment, and finally detecting the steel bar outline size and detecting the number of pixel points in each outline;
in specific implementation, the fitted steel bar outline edge is extracted again by using a Grabcut target detection algorithm to remove shading impurities.
The Grabcut target detection algorithm utilizes texture (color) information and boundary (contrast) information in an image, and a good segmentation result can be obtained only through a small amount of user interaction operation.
The technical scheme has the advantages that due to the influence of the background of the shading, a plurality of impurities exist around the outline, Grabcut target detection is just suitable for the scene, and the clear extracted outline foreground and background impurities can be separated.
In a specific implementation, the image is pre-processed using mean filtering and partition Otsu.
The zonal population (Otsu) is an algorithm for determining an image segmentation threshold, which was proposed by the japanese scholars in 1979 and is also called the maximum inter-class variance method in principle. After binarization and segmentation are carried out according to a threshold value obtained by the Otsu method: the variance of the foreground and background images is maximum; the identified position is the optimal algorithm for selecting the threshold value in the image segmentation; the method is not influenced by the brightness and the contrast of the image, and is simple in calculation; and is widely applied to digital images.
The technical scheme has the advantages that the accuracy of the detected pixel values of the steel bar outline can be improved through the conventional preprocessing of the mean filtering and the partition Otsu method.
And 5: and (4) obtaining the real area of the steel bar according to the real-time position height conversion of the unmanned aerial vehicle, comparing the real area of the steel bar with the acceptance standard of the steel bar, and marking whether the steel bar is qualified.
The method realizes the contour extraction and size detection of the target reinforcing steel bar on the site of the construction site, the extracted edge and contour are very clear, and the size identification and positioning are accurate;
the method can simply and reliably provide an implementation method and a theoretical basis for an engineering supervision project of intelligent steel bar size detection, compared with other methods, the method can be used for carrying out contour extraction and size detection on single-layer steel bar pictures on cloudy sites in most cases, and can also be well applied to the field of steel bar identification on construction sites or contour identification of similar scenes.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A reinforcing bar size detecting system based on image processing is characterized by comprising:
the image acquisition module is carried on the unmanned aerial vehicle and is configured to acquire construction site pictures;
an image processing module configured to:
detecting the edges of the steel bars in the construction site picture, and extracting an edge binary image;
carrying out Hough line detection on the edge binary image, and fitting the outline edge of the steel bar;
extracting the image containing the fitted steel bar outline edge again to remove shading impurities, then carrying out filtering and segmentation pretreatment, and finally detecting the steel bar outline size and detecting the number of pixel points in each outline;
and (4) obtaining the real area of the steel bar according to the real-time position height conversion of the unmanned aerial vehicle, comparing the real area of the steel bar with the acceptance standard of the steel bar, and marking whether the steel bar is qualified.
2. The image processing-based steel bar size detection system according to claim 1, wherein in the image processing module, a Sobel edge detection algorithm of direct convolution is adopted to detect the steel bar edge in the construction site picture, and an edge binary image is extracted.
3. The image processing-based steel bar size detection system according to claim 1, wherein in the image processing module, the fitted steel bar outline edge is extracted again by using Grabcut target detection algorithm to remove shading impurities.
4. The image-processing-based rebar size detection system of claim 1, wherein in the image processing module, the image is pre-processed using mean filtering and partition allowances.
5. The image processing-based rebar size detection system of claim 1, wherein the image processing module is further connected with a display module, and the display module is used for displaying whether the rebar is qualified or not.
6. The image processing-based rebar size detection system of claim 1, wherein the image processing module is further connected with a remote monitoring terminal.
7. A steel bar size detection method based on image processing is characterized by comprising the following steps:
receiving a construction site picture acquired by an image acquisition module carried by an unmanned aerial vehicle;
detecting the edges of the steel bars in the construction site picture, and extracting an edge binary image;
carrying out Hough line detection on the edge binary image, and fitting the outline edge of the steel bar;
extracting the image containing the fitted steel bar outline edge again to remove shading impurities, then carrying out filtering and segmentation pretreatment, and finally detecting the steel bar outline size and detecting the number of pixel points in each outline;
and (4) obtaining the real area of the steel bar according to the real-time position height conversion of the unmanned aerial vehicle, comparing the real area of the steel bar with the acceptance standard of the steel bar, and marking whether the steel bar is qualified.
8. The image processing-based steel bar size detection method according to claim 7, wherein a Sobel edge detection algorithm of direct convolution is adopted to detect the steel bar edge in the construction site picture, and an edge binary image is extracted.
9. The steel bar size detection method based on image processing as claimed in claim 7, wherein the fitted steel bar outline edge is extracted again by using Grabcut target detection algorithm to remove shading impurities.
10. The method of claim 7, wherein the image is pre-processed using mean filtering and partition ontology.
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| CN201911328093.XA CN111179232A (en) | 2019-12-20 | 2019-12-20 | Steel bar size detection system and method based on image processing |
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| CN111932508A (en) * | 2020-07-31 | 2020-11-13 | 山东大学 | Steel bar size measuring method and system based on image processing |
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| CN112598723A (en) * | 2020-12-17 | 2021-04-02 | 中南大学 | Method and device for identifying thickness of stainless steel coated steel bar and storage medium |
| CN113160112A (en) * | 2020-12-30 | 2021-07-23 | 苏州特文思达科技有限公司 | Intelligent steel grating product identification and detection method based on machine learning |
| CN113160112B (en) * | 2020-12-30 | 2024-02-02 | 苏州特文思达科技有限公司 | Intelligent recognition and detection method for steel grating products based on machine learning |
| JP2022164949A (en) * | 2021-04-19 | 2022-10-31 | 三菱電機エンジニアリング株式会社 | Bar arrangement inspection device, bar arrangement inspection method and program |
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| CN113592808B (en) * | 2021-07-28 | 2024-04-02 | 福建威而特旋压科技有限公司 | Visual detection method for belt pulley |
| CN113592808A (en) * | 2021-07-28 | 2021-11-02 | 福建威而特旋压科技有限公司 | Belt pulley visual detection method |
| CN113781422A (en) * | 2021-09-01 | 2021-12-10 | 廊坊中油朗威工程项目管理有限公司 | Pipeline construction violation identification method based on single image geometric measurement algorithm |
| CN114707206A (en) * | 2022-03-10 | 2022-07-05 | 北京梦诚科技有限公司 | Method, system, equipment and medium for identifying highway square column pier stirrup information |
| CN115638742B (en) * | 2022-10-12 | 2023-08-15 | 北京迈思发展科技有限责任公司 | Reinforcing bar shape quality inspection system based on image recognition |
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