CN112950765A - Cavity straightness detection method based on point cloud modeling - Google Patents
Cavity straightness detection method based on point cloud modeling Download PDFInfo
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Abstract
The invention discloses a cavity straightness detection method based on point cloud modeling, and belongs to the technical field of image processing and computer three-dimensional modeling. Based on an image processing technology and a computer three-dimensional modeling technology, distortion correction, image noise reduction, graying, threshold judgment, size measurement, three-dimensional modeling and geometric center and straightness model drawing are carried out on multi-section profile image data of a cavity of a measured inner bore, and straightness measurement and straightness change trajectory modeling of multiple sections of the cavity inner bore are achieved. The invention relates to a flexible, quick and efficient method for measuring the straightness of an inner cavity, which is suitable for measuring the straightness of inner cavities with different calibers such as a gun barrel, an electromagnetic emitter barrel, a long and narrow pipeline and the like, and solves the problems of complex operation, low precision and the like during the straightness measurement of the long and narrow inner cavity.
Description
Technical Field
The invention relates to a cavity straightness detection method based on point cloud modeling, and belongs to the technical field of image processing and computer three-dimensional modeling.
Background
The straightness of the cavity of the gun barrel inner bore is a main technical index of a military weapon represented by an artillery and an electromagnetic rail gun, and influences and restricts performance indexes of the military weapon, such as striking precision, reliability, service life and the like. The measurement of the straightness of the cavity of the bore of the gun barrel helps to analyze the deformation rule of the gun barrel, and provides a reference basis for improving the strength, the rigidity and the structural design of the gun barrel.
The traditional cavity linearity detection method can be divided into a linear reference measurement method and a non-linear reference measurement method according to the existence of a reference straight line. There are three kinds of straight line references adopted by the method of measuring the straight line reference, namely a real object reference, a gravity horizontal reference and a light reference. Modern measuring techniques often use straightness measuring methods based on light references, since such references allow contactless measurements to be carried out easily, for example laser collimator methods, dual-frequency laser interferometry and laser holography. The non-linear reference measuring method does not have a reference linear reference, but samples the surface of a measured object by a linear value measuring method to obtain deviation values of all sampling points on the measured surface, and then performs data processing and fits a linearity curve to judge and analyze the integral linearity error.
Based on the problems of complex operation and low measurement precision existing at present, a great deal of inconvenience and limitation exist on the straightness detection problem of a long and narrow special-shaped cavity represented by an artillery barrel and a rail artillery launching track bore. Therefore, a flexible, fast and efficient method for measuring the straightness of the bore cavity is needed.
Disclosure of Invention
Aiming at the problems in the traditional bore cavity straightness measurement, the invention provides a cavity straightness detection method based on point cloud modeling, which combines an image processing technology and a computer three-dimensional modeling technology, obtains a three-dimensional point cloud data set capable of representing the appearance characteristics of a measured bore cavity by carrying out a series of image processing on a series of acquired bore cavity contour images, obtains a three-dimensional model of the measured bore cavity by computer three-dimensional modeling, and realizes bore cavity straightness measurement by measuring contour central trajectory lines of a series of sections.
The purpose of the invention is realized by the following technical scheme:
the invention discloses a cavity straightness detection method based on point cloud modeling, which utilizes the outline image data of a detected bore cavity to carry out three-dimensional reconstruction of a bore cavity model and carries out straightness measurement, and comprises the following steps:
step 1, inputting a contour image of the cavity of the bore. A series of bore cavity contour images acquired by the lighting device matched with the image sensor are taken as processing objects of the method.
And 2, correcting distortion of the contour image of the cavity of the bore. And correcting the image distortion of the contour of the bore cavity caused by the lens so as to solve the distortion of the contour shape of the bore cavity caused by the image distortion.
Preferably, the distortion correction method of the contour image of the bore cavity adopts a Zhang-Yongyou calibration method.
And 3, filtering and denoising the contour image of the cavity of the inner bore. And filtering and denoising high photosensitive noise points existing at the junction of the contour target in the bore cavity contour image and the surrounding dark environment, so that the contour target in the bore cavity contour image is more sharply different from the surrounding environment.
Preferably, Gaussian filtering is adopted to perform filtering noise reduction on the bore cavity outline image.
And 4, graying the contour image of the cavity of the bore. And converting the contour image of the cavity of the inner bore from a color image into a gray image in a mode of converting a pixel format from an RGB (red, green and blue) channel format into a gray format.
And 5, judging the pixel threshold of the profile gray image of the cavity of the bore. And extracting a bore cavity contour area irradiated by light from the bore cavity contour gray level image according to a preset image binarization judgment standard.
And 6, measuring the size of the target. Based on the characteristics of fixed focal length of a lens and fixed axial distance between a contour target and the lens matched with the image sensor during shooting, the equivalent actual size of each pixel point of the contour image of the cavity of the inner chamber is measured by adopting a method of shooting a scale.
Further, two-dimensional position coordinates and dimensions of the target bore cavity profile within its cross-section are measured.
And 7, modeling the contour three-dimensional point cloud of the cavity of the inner bore. And (4) combining the single-section two-dimensional coordinate points obtained in the step (6) and the axial distance corresponding to the section into three-dimensional coordinate points, and performing three-dimensional modeling by adopting a computer to obtain a three-dimensional model of the measured bore cavity.
And 8, determining the geometric center of the contour of the cavity of the inner bore. And obtaining the geometric center points of the inner cavity profiles of all the sections after calibration by using a method of the contour pixel point coordinate average value, and obtaining the geometric center point three-dimensional coordinates of the inner cavity profiles of all the sections by combining the axial distance of each section.
And 9, drawing a trajectory line model of the contour central point of the cavity of the inner bore. And (4) measuring the horizontal position and vertical position variation of the geometric center of the profile section of each bore cavity on a plane vertical to the axial direction by taking the initial section of one end of the measured cavity as a reference according to the three-dimensional coordinates of the geometric center points of the profile sections of all the bore cavities obtained in the step (8), namely the variation of the straightness of the measured bore cavity.
Has the advantages that:
1. the invention discloses a cavity straightness detection method based on point cloud modeling, which is suitable for measuring the straightness of bore cavities with different calibers, such as artillery barrels, electromagnetic emitter barrels, long and narrow pipelines and the like.
2. The invention discloses a cavity straightness detection method based on point cloud modeling, wherein a processing object is bore cavity outline image data, the measurement precision and the measurement range completely depend on pixels of an image sensor and a matched lens, and the application range is wide.
3. The invention discloses a cavity straightness detection method based on point cloud modeling, which is based on computer image processing and modeling technology and can quickly, efficiently and accurately obtain a three-dimensional model and a central line trajectory line model of a bore cavity through bore cavity contour image data so as to obtain the straightness of the bore cavity.
Drawings
FIG. 1 is a schematic flow chart of a cavity straightness detection method based on point cloud modeling according to the present invention;
FIG. 2 is an exemplary diagram of a single-section bore cavity contour after pixel threshold determination processing in the cavity straightness detection method based on point cloud modeling of the present invention;
FIG. 3 is an exemplary diagram of a trajectory model of a center point of an irregularly-shaped cross section in a cavity straightness detection method based on point cloud modeling.
Detailed Description
To better illustrate the objects and advantages of the present invention, the following further description is made with reference to the accompanying drawings and examples.
Example 1:
the cavity straightness detection method based on point cloud modeling of the invention is applied to measure and analyze the straightness of a special-shaped cavity, as shown in figure 1, and comprises the following steps:
step 1, inputting a contour image of the cavity of the bore:
the contour image of the cavity in the inner bore of the embodiment is obtained by matching an annular laser generator with an image sensor, namely a plurality of images containing single-section contour information of the measured object and axial distance information corresponding to each image.
Step 2, distortion correction of the contour image of the cavity of the bore:
and correcting the contour image distortion of the inner cavity caused by the lens by adopting a Zhang Zhengyou calibration method so as to solve the contour shape distortion of the inner cavity caused by the image distortion.
Step 3, filtering and denoising the contour image of the cavity of the inner bore:
filtering and denoising the high-light-sensitive noise points existing at the junction of the contour target in the bore cavity contour image and the surrounding dark environment by adopting Gaussian filtering, so that the contour target in the bore cavity contour image is more sharply distinguished from the surrounding environment;
in this embodiment, the image resolution of the image sensor is 3840 × 2880, the gaussian kernel size for filtering is 101 × 101, and the standard deviation of the gaussian kernel function in the horizontal and vertical directions of the image is set to 0.
Step 4, graying the contour image of the bore cavity:
and converting the contour image of the cavity of the inner bore from a color image into a gray image in a mode of converting a pixel format from an RGB (red, green and blue) channel format into a gray format.
In this embodiment, the gray value conversion relational expression of each pixel point before and after conversion is as follows:
Gray＝0.299×R+0.587×G+0.114×B
wherein Gray represents a grayscale channel value, R represents a red channel value, G represents a green channel value, and B represents a blue channel value;
after the pixel threshold determination process, the profile of the single-section bore cavity is shown in fig. 2.
Step 5, judging the pixel threshold of the profile gray image of the cavity of the bore:
extracting a bore cavity contour area irradiated by light from the bore cavity contour gray level image according to a preset image binarization judgment standard;
in this embodiment, the threshold is set to 128 based on the characteristic that the gray scale value of the pixel is in the range of 0-255, so that the pixels with gray scale values greater than 128 in the image are classified as the pixels to which the target belongs.
And 6, measuring the size of the target. Based on the characteristics of fixed focal length of a lens and fixed axial distance between a contour target and the lens matched during shooting of an image sensor, measuring to obtain the equivalent actual size of each pixel point of a contour image of the cavity of the inner chamber by adopting a method of a shooting scale;
further, two-dimensional position coordinates and dimensions of the target bore cavity profile within its cross-section are measured.
And 7, modeling the contour three-dimensional point cloud of the cavity of the inner bore. And (4) combining the single-section two-dimensional coordinate points obtained in the step (6) and the axial distance corresponding to the section into three-dimensional coordinate points, and performing three-dimensional modeling by adopting a computer to obtain a three-dimensional model of the measured bore cavity.
And 8, determining the geometric center of the contour of the cavity of the inner bore. And obtaining the geometric center points of the inner cavity profiles of all the sections after calibration by using a method of the contour pixel point coordinate average value, and obtaining the geometric center point three-dimensional coordinates of the inner cavity profiles of all the sections by combining the axial distance of each section.
And 9, drawing a trajectory line model of the contour central point of the cavity of the inner bore. And (4) measuring the horizontal position and vertical position variation of the geometric center of the profile section of each bore cavity on a plane vertical to the axial direction by taking the initial section of one end of the measured cavity as a reference according to the three-dimensional coordinates of the geometric center points of the profile sections of all the bore cavities obtained in the step (8), namely the variation of the straightness of the measured bore cavity.
In this embodiment, a three-dimensional schematic diagram of a trajectory line model of a center point of a profile of a bore cavity is shown in fig. 3, which includes 30 profile profiles of the bore cavity, wherein planes of the profile profiles of the bore cavity are parallel to each other, and 30 isolated points in the center of the diagram are geometric center points of the 30 profile profiles of the bore cavity.
In this embodiment, 30 pieces of profile data of the bore cavity shown in fig. 3 are selected, the coordinates of the center points of the profile of the bore cavity are shown in the following table, and the coordinate deviations of the center points of the profile of the other bore cavities are obtained by using the coordinate of the center point of the profile of the 1 st bore cavity as a reference, which is the variation of the straightness of the bore cavity to be measured.
The cavity straightness detection method based on point cloud modeling is suitable for post-processing of cavity contour images containing the bore, which are obtained by matching any lighting mode with image acquisition equipment.
The cavity straightness detection method based on point cloud modeling provided by the invention has no specific limitation on morphological characteristics of the contour of the cavity of the bore.
The above detailed description is intended to illustrate the objects, aspects and advantages of the present invention, and it should be understood that the above detailed description is only exemplary of the present invention and is not intended to limit the scope of the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (2)
1. A cavity straightness detection method based on point cloud modeling is characterized by comprising the following steps: combining an image processing technology and a computer three-dimensional modeling technology, carrying out a series of image processing on a series of acquired contour images of the bore cavity to obtain a three-dimensional point cloud data set capable of representing the appearance characteristics of the measured bore cavity, obtaining a three-dimensional model of the measured bore cavity through computer three-dimensional modeling, and measuring the straightness of the bore cavity by measuring contour central trajectory lines of a series of sections.
2. The cavity straightness detection method based on point cloud modeling as claimed in claim 1, wherein: the three-dimensional reconstruction of the bore cavity model is carried out by utilizing the measured bore cavity outline image data, and the straightness measurement is carried out, the method comprises the following steps:
step 1, inputting a contour image of the cavity of the bore: a series of bore cavity contour images acquired by the lighting device matched with the image sensor are used as processing objects of the method;
step 2, distortion correction of the contour image of the cavity of the bore: correcting the image distortion of the contour of the bore cavity caused by the lens so as to solve the distortion of the contour shape of the bore cavity caused by the image distortion;
step 3, filtering and denoising the contour image of the cavity of the inner bore: filtering and denoising high-light-sensitive noise points existing at the junction of the contour target in the bore cavity contour image and the surrounding dark environment, so that the contour target in the bore cavity contour image is more sharply distinguished from the surrounding environment;
step 4, graying the contour image of the bore cavity: converting the contour image of the cavity of the inner bore from a color image into a gray image in a mode of converting a pixel format from an RGB channel format into a gray format;
step 5, judging the pixel threshold of the profile gray image of the cavity of the bore: extracting a bore cavity contour area irradiated by light from the bore cavity contour gray level image according to a preset image binarization judgment standard;
step 6, measuring the size of the target: based on the characteristics of fixed focal length of a lens and fixed axial distance between a contour target and the lens matched during shooting of an image sensor, measuring to obtain the equivalent actual size of each pixel point of a contour image of the cavity of the inner chamber by adopting a method of a shooting scale;
further, measuring two-dimensional position coordinates and sizes of the target bore cavity outline in the section plane of the target bore cavity outline;
step 7, three-dimensional point cloud modeling of the contour of the cavity of the inner bore: combining the single-section two-dimensional coordinate points obtained in the step (6) and the axial distance corresponding to the section into three-dimensional coordinate points, and performing three-dimensional modeling by adopting a computer to obtain a three-dimensional model of the measured bore cavity;
step 8, determining the geometric center of the contour of the cavity of the inner bore: obtaining the geometric center points of the bore cavity outlines of all the sections after calibration by using a contour pixel point coordinate average value method, and obtaining the geometric center point three-dimensional coordinates of the bore cavity outlines of all the sections by combining the axial distance of each section;
step 9, drawing a trajectory line model of the contour central point of the cavity of the inner bore: and (4) measuring the horizontal position and vertical position variation of the geometric center of the profile section of each bore cavity on a plane vertical to the axial direction by taking the initial section of one end of the measured cavity as a reference according to the three-dimensional coordinates of the geometric center points of the profile sections of all the bore cavities obtained in the step (8), namely the variation of the straightness of the measured bore cavity.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116993923A (en) * | 2023-09-22 | 2023-11-03 | 长沙能川信息科技有限公司 | Three-dimensional model making method, system, computer equipment and storage medium for converter station |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2010002070A1 (en) * | 2008-06-30 | 2010-01-07 | Korea Institute Of Oriental Medicine | Method for grouping 3d models to classify constitution |
| CN103236076A (en) * | 2013-04-11 | 2013-08-07 | 武汉大学 | Three-dimensional object model reconstruction system and method based on laser images |
| CN107063119A (en) * | 2017-04-02 | 2017-08-18 | 天津大学 | Inner-walls of duct pattern and central shaft linearity measurer and method |
| CN109583377A (en) * | 2018-11-30 | 2019-04-05 | 北京理工大学 | A kind of control method, device and host computer that pipeline model is rebuild |
| CN111145345A (en) * | 2019-12-31 | 2020-05-12 | 山东大学 | Tunnel construction area three-dimensional model construction method and system |
| CN111207695A (en) * | 2020-01-14 | 2020-05-29 | 北京科技大学 | Hot-rolled strip steel end three-dimensional contour measuring method based on double-line structured light |
| CN112013792A (en) * | 2020-10-19 | 2020-12-01 | 南京知谱光电科技有限公司 | Surface scanning three-dimensional reconstruction method for complex large-component robot |
| CN112102504A (en) * | 2020-09-16 | 2020-12-18 | 成都威爱新经济技术研究院有限公司 | Three-dimensional scene and two-dimensional image mixing method based on mixed reality |
-
2021
- 2021-03-08 CN CN202110252254.2A patent/CN112950765B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2010002070A1 (en) * | 2008-06-30 | 2010-01-07 | Korea Institute Of Oriental Medicine | Method for grouping 3d models to classify constitution |
| CN103236076A (en) * | 2013-04-11 | 2013-08-07 | 武汉大学 | Three-dimensional object model reconstruction system and method based on laser images |
| CN107063119A (en) * | 2017-04-02 | 2017-08-18 | 天津大学 | Inner-walls of duct pattern and central shaft linearity measurer and method |
| CN109583377A (en) * | 2018-11-30 | 2019-04-05 | 北京理工大学 | A kind of control method, device and host computer that pipeline model is rebuild |
| CN111145345A (en) * | 2019-12-31 | 2020-05-12 | 山东大学 | Tunnel construction area three-dimensional model construction method and system |
| CN111207695A (en) * | 2020-01-14 | 2020-05-29 | 北京科技大学 | Hot-rolled strip steel end three-dimensional contour measuring method based on double-line structured light |
| CN112102504A (en) * | 2020-09-16 | 2020-12-18 | 成都威爱新经济技术研究院有限公司 | Three-dimensional scene and two-dimensional image mixing method based on mixed reality |
| CN112013792A (en) * | 2020-10-19 | 2020-12-01 | 南京知谱光电科技有限公司 | Surface scanning three-dimensional reconstruction method for complex large-component robot |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116993923A (en) * | 2023-09-22 | 2023-11-03 | 长沙能川信息科技有限公司 | Three-dimensional model making method, system, computer equipment and storage medium for converter station |
| CN116993923B (en) * | 2023-09-22 | 2023-12-26 | 长沙能川信息科技有限公司 | Three-dimensional model making method, system, computer equipment and storage medium for converter station |
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