CN114399631A - Three-dimensional reconstruction and oil sludge identification method for interior of crude oil large tank - Google Patents
Three-dimensional reconstruction and oil sludge identification method for interior of crude oil large tank Download PDFInfo
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- 239000010779 crude oil Substances 0.000 title claims abstract description 45
- 239000003921 oil Substances 0.000 title claims abstract description 39
- 239000010802 sludge Substances 0.000 title claims abstract description 27
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- 238000001514 detection method Methods 0.000 description 4
- 238000004140 cleaning Methods 0.000 description 2
- 238000003708 edge detection Methods 0.000 description 2
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- 238000004519 manufacturing process Methods 0.000 description 1
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- 239000003208 petroleum Substances 0.000 description 1
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Abstract
The invention discloses a three-dimensional reconstruction and oil sludge identification method for the interior of a crude oil large tank, which comprises the following steps: acquiring images of the interior of a plurality of crude oil large tanks after pretreatment; extracting characteristic points of the image in the crude oil tank; searching the position of a pixel point corresponding to the characteristic point in the image in the other frame of crude oil tank obtained by shooting after the camera moves by adopting an epipolar line search and block matching technology; calculating the pose of the camera according to the positions of the feature point pairs matched by the two images; estimating the depth of a pixel point according to the pose of the camera, and recovering the three-dimensional space coordinate of the pixel point; and converting and splicing the three-dimensional space coordinates to obtain a point cloud map, carrying out triangular meshing dense reconstruction on the point cloud map, and pasting textures to obtain a three-dimensional reconstruction model in the oil storage tank. The invention can stably and efficiently carry out three-dimensional visual display on the visible surface in the oil storage tank in real time, and accurately identify the position of the oil sludge, so as to accurately and efficiently clean the oil sludge on the tank body.
Description
Technical Field
The invention relates to the field of petrochemical industry, in particular to a method for three-dimensional reconstruction and oil sludge identification of the interior of a crude oil large tank.
Background
At present, three-dimensional reconstruction is widely applied to various aspects such as automatic driving, robots, AR, industrial manufacturing and the like; object detection, i.e., finding a specific location of an object in a given image, video or scene, is also widely used. Petroleum is an important energy source next to coal in China, a large amount of crude oil needs to be stored in China at present, a large amount of crude oil storage tanks with huge volumes are provided, but crude oil stored in the tanks for a long time can be deposited to form oil sludge, certain corrosion is caused to the oil storage tanks, regular cleaning is needed, and in order to clearly know the conditions inside the oil storage tanks and accurately clean oil stains, three-dimensional reconstruction and oil sludge identification are carried out on the inside of the oil storage tanks.
At present, no one can do the work of three-dimensional reconstruction after acquiring images by utilizing a monocular infrared camera aiming at the specific scene inside the large crude oil tank. Meanwhile, currently, for oil sludge identification in a tank, the mainstream algorithm uses a traditional Canny detector to perform edge detection so as to extract the position of the oil sludge. However, the quality of images inside the oil storage tank shot by the infrared camera is reduced due to the totally dark and closed environment inside the oil storage tank; meanwhile, the traditional Canny edge detection operator is sensitive to image noise, has the problems of false edges, lack of self-adaptability and the like, is applied to the complex scene in the oil storage tank, greatly reduces the detection effect, and can generate the phenomenon of false detection. Therefore, a more accurate and efficient apparatus and method for identifying sludge in a large oil tank are needed.
Disclosure of Invention
The invention provides a three-dimensional reconstruction and oil sludge identification method for the interior of a large crude oil tank, which is used for solving the problems that no one carries out three-dimensional reconstruction on the interior of the large crude oil tank, the quality of images shot by an infrared camera in the interior of the oil tank is reduced, and the effect of extracting the position of oil sludge is greatly reduced. In order to solve the technical problems, the invention provides a method for three-dimensional reconstruction and oil sludge identification of the interior of a crude oil large tank, which comprises the following steps:
acquiring images of the interior of a plurality of crude oil large tanks after pretreatment;
extracting characteristic points of the image in the crude oil tank;
searching the position of a pixel point corresponding to the characteristic point in the image in the other frame of crude oil tank obtained by shooting after the camera moves by adopting an epipolar line search and block matching technology;
calculating the pose of the camera according to the positions of the feature point pairs which are matched with the images shot before and after the movement of the two cameras;
performing depth estimation on pixel points according to the pose of the camera, and recovering three-dimensional space coordinates of the pixel points according to the depth estimation;
converting the three-dimensional space coordinates into point clouds for splicing to obtain a point cloud map, and performing triangular meshing dense reconstruction and texture pasting on the point cloud map to obtain a visual three-dimensional reconstruction model of the interior of the oil storage tank.
Preferably, after the images of the interior of the plurality of crude oil tanks after the pretreatment are acquired, the method further comprises the following steps:
performing edge protection and noise reduction on the image in the crude oil tank by adopting rapid guide filtering to obtain a noise reduction image;
calculating the gradient amplitude and direction of the noise-reduced image to estimate the edge strength and direction at each point;
carrying out non-maximum suppression according to the gradient amplitude and direction;
adaptively selecting a threshold value by an OTSU maximum inter-class variance method to determine and connect image edges;
and converting and outputting an image sequence containing the object contour line, and automatically identifying whether the image sequence contains oil sludge.
Preferably, the step of acquiring the images of the interior of the plurality of crude oil tanks after pretreatment further comprises the following steps:
receiving an image sequence acquired by monitoring equipment;
and adopting nonlinear transformation and multi-scale convolution calculation to improve the brightness and contrast of the image sequence to obtain the preprocessed images of the interior of the large crude oil tanks.
Preferably, the specific method for extracting the feature points of the image of the interior of the crude oil tank is as follows:
and (4) extracting the feature points by adopting an ORB feature extraction algorithm.
Preferably, the specific steps of calculating the pose of the camera according to the positions of the feature point pairs matched by the images shot before and after the movement of the two cameras are as follows:
according to the matched characteristic point pairs on the two images, an essential matrix can be solved by using an eight-point method, wherein the essential matrix meets the formula:
wherein E is an essential matrix, K is an internal reference matrix of the camera, and p1 and p2 are feature point pairs matched on the two images;
the following formula: e ═ tΛAnd R, solving the translation vector t and the rotation vector R of the camera according to singular value decomposition.
Preferably, the depth estimation of the pixel point is performed according to the pose of the camera, and the specific steps of recovering the three-dimensional space coordinates of the pixel point according to the depth estimation are as follows:
s151: assuming that the depth d of a certain pixel follows gaussian distribution, the formula is as follows:
P(d)＝N(μ，σ2)；
s152: when new data is generated, assuming that the new data also obeys Gaussian distribution, the position of a certain pixel in the reference frame at the projection point of the current frame can be determined according to epipolar line search and block matching;
s153: calculating the depth and uncertainty of the triangulated depth according to the geometric relationship;
s154: and fusing the current observation into the last estimation, if the uncertainty is smaller than the set threshold, stopping the calculation, otherwise, returning to the step S152.
The invention has the beneficial effects that: the three-dimensional visual display can be stably and efficiently carried out on the visible surface in the crude oil tank in real time, and the oil sludge position can be accurately identified by utilizing an oil sludge identification algorithm, so that the cleaning gun can accurately and efficiently clean the oil sludge and the oil stains on the tank body.
Drawings
FIG. 1 is a flow chart of three-dimensional reconstruction;
FIG. 2 is a flow chart of a sludge identification algorithm;
FIG. 3 is a flow chart of the ORB feature extraction algorithm key point direction;
FIG. 4 is a flow chart of camera pose estimation;
FIG. 5 is a flow chart for recovering three-dimensional spatial coordinates of a pixel point.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
Referring to the attached figure 1, a method for three-dimensional reconstruction and oil sludge identification in a crude oil tank comprises the following steps:
s11: acquiring images of the interior of a plurality of crude oil large tanks after pretreatment;
s12: extracting characteristic points of the image in the crude oil tank;
s13: searching the position of a pixel point corresponding to the characteristic point in the image in the other frame of crude oil tank obtained by shooting after the camera moves by adopting an epipolar line search and block matching technology;
s14: calculating the pose of the camera according to the positions of the feature point pairs which are matched with the images shot before and after the movement of the two cameras;
s15: performing depth estimation on pixel points according to the pose of the camera, and recovering three-dimensional space coordinates of the pixel points according to the depth estimation;
s16: converting the three-dimensional space coordinates into point clouds for splicing to obtain a point cloud map, and performing triangular meshing dense reconstruction and texture pasting on the point cloud map to obtain a visual three-dimensional reconstruction model of the interior of the oil storage tank.
As a preferred embodiment, as shown in fig. 2, after acquiring the images of the interior of the pretreated crude oil tanks, the method further comprises the following steps:
s21: performing edge protection and noise reduction on the image in the crude oil tank by adopting rapid guide filtering to obtain a noise reduction image;
s22: calculating the gradient amplitude and direction of the noise-reduced image to estimate the edge strength and direction at each point;
s23: carrying out non-maximum suppression according to the gradient amplitude and direction;
s24: adaptively selecting a threshold value by an OTSU maximum inter-class variance method to determine and connect image edges;
s25: and converting and outputting an image sequence containing the object contour line, and automatically identifying whether the image sequence contains oil sludge.
As a preferred embodiment, the step of acquiring the images of the interior of the plurality of crude oil tanks after the pretreatment further comprises the following steps:
s01: receiving an image sequence acquired by monitoring equipment;
s02: and adopting nonlinear transformation and multi-scale convolution calculation to improve the brightness and contrast of the image sequence to obtain the preprocessed images of the interior of the large crude oil tanks.
As a preferred embodiment, the specific method for extracting the feature points of the image of the interior of the crude oil tank is as follows:
extracting feature points by adopting an ORB feature extraction algorithm, wherein the ORB feature extraction algorithm comprises scale invariance description and rotation invariance description; the scale invariance description is realized by constructing an image pyramid for an image sequence input by a system and setting key points on each layer of the pyramid; the rotational invariance description is that the ORB feature detection and vector creation algorithm assigns a direction to each key point according to the change of the gray value around the key point, the geometric center points to the gray centroid, which is the direction of the key point, as shown in fig. 3, the direction calculation steps of the key point are as follows:
s121: in an image block B, the moments of the image block are defined as:
s122: the centroid of the image block can be found by the moments:
s123: connecting the geometric center O and the centroid C of the image block to obtain a direction vector OC, so that the direction of the feature point can be defined as:
θ＝arctan(m01/m10)。
as a preferred embodiment, as shown in fig. 4, the specific steps of calculating the pose of the camera according to the positions of the feature point pairs obtained by matching the images captured before and after the movement of the two cameras are as follows:
s141: according to the matched characteristic point pairs on the two images, an essential matrix can be solved by using an eight-point method, wherein the essential matrix meets the formula:
wherein E is an essential matrix, K is an internal reference matrix of the camera, and p1 and p2 are feature point pairs matched on the two images;
s142: the following formula: e ═ tΛAnd R, solving the translation vector t and the rotation vector R of the camera according to singular value decomposition.
As a preferred embodiment, as shown in fig. 5, depth estimation is performed on a pixel point according to the pose of the camera, and the specific steps of recovering the three-dimensional space coordinates of the pixel point according to the depth estimation include:
s151: assuming that the depth d of a certain pixel follows gaussian distribution, the formula is as follows:
P(d)＝N(μ，σ2)；
s152: when new data is generated, assuming that the new data also obeys Gaussian distribution, the position of a certain pixel in the reference frame at the projection point of the current frame can be determined according to epipolar line search and block matching;
s153: calculating the depth and uncertainty of the triangulated depth according to the geometric relationship;
s154: and fusing the current observation into the last estimation, if the uncertainty is smaller than the set threshold, stopping the calculation, otherwise, returning to the step S152.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (6)
1. A three-dimensional reconstruction and oil sludge identification method for the interior of a crude oil large tank is characterized by comprising the following steps:
acquiring images of the interior of a plurality of crude oil large tanks after pretreatment;
extracting characteristic points of the image in the crude oil tank;
searching the position of a pixel point corresponding to the characteristic point in the image in the other frame of crude oil tank obtained by shooting after the camera moves by adopting an epipolar line search and block matching technology;
calculating the pose of the camera according to the positions of the feature point pairs which are matched with the images shot before and after the movement of the two cameras;
performing depth estimation on pixel points according to the pose of the camera, and recovering three-dimensional space coordinates of the pixel points according to the depth estimation;
converting the three-dimensional space coordinates into point clouds for splicing to obtain a point cloud map, and performing triangular meshing dense reconstruction and texture pasting on the point cloud map to obtain a visual three-dimensional reconstruction model of the interior of the oil storage tank.
2. The method for three-dimensional reconstruction and sludge identification of the interior of the large crude oil tank as claimed in claim 1, wherein after the pre-processed images of the interior of the large crude oil tank are obtained, the method further comprises the following steps:
performing edge protection and noise reduction on the image in the crude oil tank by adopting rapid guide filtering to obtain a noise reduction image;
calculating the gradient amplitude and direction of the noise-reduced image to estimate the edge strength and direction at each point;
carrying out non-maximum suppression according to the gradient amplitude and direction;
adaptively selecting a threshold value by an OTSU maximum inter-class variance method to determine and connect image edges;
and converting and outputting an image sequence containing the object contour line, and automatically identifying whether the image sequence contains oil sludge.
3. The method of claim 1, wherein the step of obtaining the preprocessed images of the interior of the crude oil tank further comprises the following steps:
receiving an image sequence acquired by monitoring equipment;
and adopting nonlinear transformation and multi-scale convolution calculation to improve the brightness and contrast of the image sequence to obtain the preprocessed images of the interior of the large crude oil tanks.
4. The method for three-dimensional reconstruction and oil sludge identification of the interior of the large crude oil tank as claimed in claim 1, wherein the specific method for extracting the feature points of the image of the interior of the large crude oil tank is as follows:
and (4) extracting the feature points by adopting an ORB feature extraction algorithm.
5. The method for three-dimensional reconstruction and oil sludge identification of the interior of the crude oil large tank according to claim 1, wherein the specific steps of calculating the pose of the camera according to the positions of the feature point pairs which are matched and completed by the images shot before and after the movement of the two cameras are as follows:
according to the matched characteristic point pairs on the two images, an essential matrix can be solved by using an eight-point method, wherein the essential matrix meets the formula:
wherein E is an essential matrix, K is an internal reference matrix of the camera, and p1 and p2 are feature point pairs matched on the two images;
the following formula: e ═ tΛAnd R, solving the translation vector t and the rotation vector R of the camera according to singular value decomposition.
6. The method for three-dimensional reconstruction and oil sludge identification in the crude oil large tank according to claim 1 is characterized in that depth estimation of pixel points is performed according to the pose of the camera, and the specific steps of recovering three-dimensional space coordinates of the pixel points according to the depth estimation are as follows:
s151: assuming that the depth d of a certain pixel follows gaussian distribution, the formula is as follows:
P(d)＝N(μ，σ2)；
s152: when new data is generated, assuming that the new data also obeys Gaussian distribution, the position of a certain pixel in the reference frame at the projection point of the current frame can be determined according to epipolar line search and block matching;
s153: calculating the depth and uncertainty of the triangulated depth according to the geometric relationship;
s154: and fusing the current observation into the last estimation, if the uncertainty is smaller than the set threshold, stopping the calculation, otherwise, returning to the step S152.
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