CN113538350A - Method for identifying depth of foundation pit based on multiple cameras - Google Patents
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Abstract
The invention discloses a method for identifying the depth of a foundation pit based on multiple cameras in the field of buildings, which comprises the following steps: s201: and acquiring and obtaining paired data of the two cameras, including position parameters of the cameras, videos of the cameras and calibration and correction of the two cameras. S202: and preprocessing the acquired video data, including extracting key frames and preprocessing frame images. S203: and (4) segmenting the network by utilizing the trained depth semantic fast RCNN, and segmenting to obtain a machine pit part. S204: and acquiring the upper edge and the lower edge of the pit by using an edge detection algorithm image processing technology, and correcting the position of a part needing to be measured. S205: and (3) predicting the depth of the part to be measured by using the imaging principle of a binocular camera, and estimating the depth of the pit. S206: and (5) integrating the camera pit depth information and the measured position information in the S204 and the S205, and visually feeding back the measured position and the estimated height.
Description
Technical Field
The invention relates to the field of constructional engineering, in particular to a method for identifying the depth of a foundation pit based on multiple cameras.
Background
The foundation pit depth identification of the construction project site at the present stage is mainly completed by visual inspection of human eyes and matching with measuring instruments such as box rulers, tape measures and the like. And after the measurement is finished, data recording is carried out, and finally the identification of the depth of the foundation pit is achieved. In the identification process, the identification of the depth of the foundation pit is manually completed, which causes the increase of project cost, and certainly, the accuracy and the real-time performance of identification data are difficult to ensure.
Disclosure of Invention
The invention aims to provide a method for identifying the depth of a foundation pit based on multiple cameras, which can automatically identify the depth of the foundation pit and reduce the real-time measurement of field constructors.
The purpose of the invention is realized as follows: a method for identifying the depth of a foundation pit based on multiple cameras comprises the following steps:
s201: acquiring and obtaining paired double-camera data, including position parameters of the cameras, videos of the cameras and calibration and correction of the double cameras;
s202: preprocessing the acquired video data, including extracting key frames and preprocessing frame images;
s203: segmenting a network by utilizing the trained depth semantic fast RCNN, and segmenting to obtain a machine pit part;
s204: acquiring the upper edge and the lower edge of a pit by using an edge detection algorithm image processing technology, and correcting the position of a part needing to be measured;
s205: predicting the depth of a part to be measured by using a binocular camera imaging principle, and estimating the depth of a machine pit;
s206: and (5) integrating the camera pit depth information and the measured position information in the S204 and the S205, and visually feeding back the measured position and the estimated height.
Preferably, the two cameras belong to a unified model and have the same parameter information.
Preferably, the calibration in the calibration correction of the two cameras is to measure the internal and external parameters of the cameras, and the calibration is to calibrate the positions of the images acquired by the two cameras, so that the two corrected images are in the same plane and parallel to each other, and the basic distance between the cameras is b.
Preferably, the key frame extraction is to respectively take one frame of image by two cameras at an interval of 10 minutes; the frame image preprocessing includes picture size processing and normalization.
Preferably, the step of dividing the fast RCNN into network partitions and obtaining the pit part includes:
a, training a machine pit side wall detector based on deep learning on the basis of an existing data set;
b, putting the frame image of the binocular camera into a trained machine pit side wall detector to obtain the range of the machine pit side wall;
and c, taking the interruption of the machine pit range as the height of the machine pit in the frame image to be measured in a weighted average mode and the like.
Preferably, the image processing technique of the edge detection algorithm includes the following specific steps:
a. converting the partial image of the side wall area of the machine pit, which is obtained by the side wall detector in the step S203 and causes the side wall area of the machine pit, into an HSV color space, and extracting the approximate range of the machine pit by using the color attribute of the side wall of the machine pit;
b. calculating to obtain a further area of the side wall of the machine pit by using a contour extraction method, obtaining the upper and lower boundaries of the side wall, recording the approximate distance weighting of the upper and lower boundaries, and further correcting the height part of the machine pit to be measured;
preferably, the imaging principle of the binocular camera in step S205 is as follows:
let P be (x,y, z) whose projection points on the image planes of the two cameras are respectively (x)r，yr)，(xl，yl) (ii) a From the principle of similar triangles we can derive:
obtaining by solution:
b, the reference distance between the two cameras;
f, focal lengths of two identical cameras;
d＝xl-xrparallax between the two cameras;
x and Z are the X-axis direction and the Z-axis direction in the three-dimensional space;
and x, y, z are the coordinates of the P point in the three-dimensional space.
Compared with the prior art, the invention has the advantages that: the artificial intelligent algorithm of the construction site monitoring camera is used for automatically identifying the depth of the foundation pit, the excavation depth of the foundation pit can be monitored in real time, an automatic early warning value can be set according to the design requirements of a drawing, the accuracy of excavation of the foundation pit is guaranteed, and overexcavation of the foundation pit is prevented; the monitoring equipment on the construction site is automatically attended, so that the manual measurement of the depth of the foundation pit is reduced, the personnel investment is reduced, and the cost is saved; the manual measurement is not needed, the depth of the foundation pit is avoided, the cross operation of operating personnel and excavation machinery is avoided, the personal safety is ensured, and the occurrence of safety accidents is reduced.
Drawings
FIG. 1 is a flow chart of the present invention.
Fig. 2 is an imaging schematic diagram of the binocular camera of the present invention.
Wherein, b is the reference distance between the two cameras; f, focal lengths of two identical cameras;
d＝xl-xrparallax between the two cameras; x and Z are the X-axis direction and the Z-axis direction in the three-dimensional space; and x, y, z are the coordinates of the P point in the three-dimensional space.
Detailed Description
As shown in fig. 1, a method for identifying the depth of a foundation pit based on multiple cameras includes the following steps:
s201: acquiring and obtaining paired double-camera data, including position parameters of the cameras, including a focal length f of the cameras and a reference distance b between the double cameras, and performing calibration and correction on videos of the cameras and the double cameras to ensure that image planes of the double cameras are overlapped, so that subsequent depth estimation is accurate;
s202: preprocessing the acquired video data, including extracting key frames and preprocessing frame images, wherein the aim is to extract frame images for depth estimation from the video data;
s203: dividing a network by using the trained depth semantic fast RCNN, dividing and obtaining a machine pit part, and preliminarily obtaining a machine pit depth part area needing to be measured;
s204: acquiring the upper edge and the lower edge of a pit by using an edge detection algorithm image processing technology, and correcting the position of a part needing to be measured, wherein the step is to further determine the depth part needing to be measured in order to supplement the deficiency of a depth semantic network by using a traditional image processing method, so as to obtain two points needing to be measured in an image plane;
s205: predicting the depth of a part to be measured by using a binocular camera imaging principle, and estimating the depth of a machine pit;
s206: and (5) integrating the camera pit depth information and the measured position information in the S204 and the S205, and visually feeding back the measured position and the estimated height.
The two cameras belong to a unified model and have the same parameter information.
The calibration in the calibration correction of the double cameras measures the internal and external parameters of the cameras, and the calibration refers to calibrating the positions of the images acquired by the double cameras, so that the two calibrated images are positioned on the same plane and are parallel to each other, and the basic distance between the cameras is obtained as b.
The key frame extraction is to respectively extract one frame of image by two cameras at an interval of 10 minutes; the frame image preprocessing includes picture size processing and normalization.
The concrete steps of the Faster RCNN network segmentation and machine pit part obtaining method comprise:
a, training a machine pit side wall detector based on deep learning on the basis of an existing data set;
b, putting the frame image of the binocular camera into a trained machine pit side wall detector to obtain the range of the machine pit side wall;
and c, taking the interruption of the machine pit range as the height of the machine pit in the frame image to be measured in a weighted average mode and the like.
The image processing technology of the edge detection algorithm comprises the following specific steps:
a. converting the partial image of the side wall area of the machine pit, which is obtained by the side wall detector in the step S203 and causes the side wall area of the machine pit, into an HSV color space, and extracting the approximate range of the machine pit by using the color attribute of the side wall of the machine pit;
b. calculating to obtain a further area of the side wall of the machine pit by using a contour extraction method, obtaining the upper and lower boundaries of the side wall, recording the approximate distance weighting of the upper and lower boundaries, and further correcting the height part of the machine pit to be measured;
the imaging principle of the binocular camera in the step S205 is as follows:
let P be (x, y, z), and its projection point on the image plane of the dual-camera be (x)r，yr)，(xl，yl) (ii) a From the principle of similar triangles we can derive:
obtaining by solution:
b, the reference distance between the two cameras;
f, focal lengths of two identical cameras;
d＝xl-xrparallax between the two cameras;
x and Z are the X-axis direction and the Z-axis direction in the three-dimensional space;
and x, y, z are the coordinates of the P point in the three-dimensional space.
The implementation method comprises the following steps: if in a dual-camera system, b is 0.2m, f is 0.008m, and the resolution of the camera is 300dip, the length of one pixel is about 8 × 10-5Rice; upper end point P of machine pit depth part1The coordinates in the two-camera image are (100 ) and (50, 100), respectively, and the lower endpoint P2The coordinates in the dual camera image are (500, 400) and (495, 400), respectively, then
Then
The present invention is not limited to the above-mentioned embodiments, and based on the technical solutions disclosed in the present invention, those skilled in the art can make some substitutions and modifications to some technical features without creative efforts according to the disclosed technical contents, and these substitutions and modifications are all within the protection scope of the present invention.
Claims (7)
1. A method for identifying the depth of a foundation pit based on multiple cameras is characterized by comprising the following steps:
s201: acquiring and obtaining paired double-camera data, including position parameters of the cameras, videos of the cameras and calibration and correction of the double cameras;
s202: preprocessing the acquired video data, including extracting key frames and preprocessing frame images;
s203: segmenting a network by utilizing the trained depth semantic fast RCNN, and segmenting to obtain a machine pit part;
s204: acquiring the upper edge and the lower edge of a pit by using an edge detection algorithm image processing technology, and correcting the position of a part needing to be measured;
s205: predicting the depth of a part to be measured by using a binocular camera imaging principle, and estimating the depth of a machine pit;
s206: and (5) integrating the camera pit depth information and the measured position information in the S204 and the S205, and visually feeding back the measured position and the estimated height.
2. The method for identifying the depth of the foundation pit based on the multiple cameras according to claim 1, wherein the two cameras are of a uniform type and have the same parameter information.
3. The method for identifying the depth of the foundation pit based on the multiple cameras as claimed in claim 1, wherein the calibration in the calibration correction of the double cameras is to measure the internal and external parameters of the cameras, and the calibration is to calibrate the positions of the images acquired by the double cameras, so that the two calibrated images are in the same plane and are parallel to each other, and the basic distance between the cameras is b.
4. The method for identifying the depth of the foundation pit based on the multiple cameras as claimed in claim 1, wherein the extracting the key frames is to take one frame of image from each of the two cameras at an interval of 10 minutes; the frame image preprocessing includes picture size processing and normalization.
5. The method for identifying the depth of the foundation pit based on the multiple cameras according to claim 1, wherein the step of dividing the machine pit part by the Faster RCNN division network comprises the following steps:
a, training a machine pit side wall detector based on deep learning on the basis of an existing data set;
b, putting the frame image of the binocular camera into a trained machine pit side wall detector to obtain the range of the machine pit side wall;
and c, taking the interruption of the machine pit range as the height of the machine pit in the frame image to be measured in a weighted average mode and the like.
6. The method for identifying the depth of the foundation pit based on the multiple cameras according to claim 1, wherein the image processing technology of the edge detection algorithm comprises the following specific steps:
a. converting the partial image of the side wall area of the machine pit, which is obtained by the side wall detector in the step S203 and causes the side wall area of the machine pit, into an HSV color space, and extracting the approximate range of the machine pit by using the color attribute of the side wall of the machine pit;
b. calculating to obtain a further area of the side wall of the machine pit by using a contour extraction method, obtaining the upper and lower boundaries of the side wall, recording the approximate distance weighting of the upper and lower boundaries, and further correcting the height part of the machine pit to be measured;
7. the method for identifying the depth of the foundation pit based on the multiple cameras according to claim 1, wherein the imaging principle of the binocular cameras in the step S205 is as follows:
let P be (x, y, z), and its projection point on the image plane of the dual-camera be (x)r，yr)，(xl，yl) (ii) a From the principle of similar triangles we can derive:
obtaining by solution:
wherein, b: a reference distance between the two cameras;
f: focal lengths of two identical cameras;
d＝xl-xr: parallax between the two cameras;
x, Z: the X-axis direction and the Z-axis direction in the three-dimensional space;
x, y, z: the coordinates of the point P in three-dimensional space.
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