CN110672020A - Stand tree height measuring method based on monocular vision - Google Patents
Stand tree height measuring method based on monocular vision Download PDFInfo
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Abstract
The invention discloses a standing tree height measuring method based on monocular vision. The problem of current tree height measure have the operation complicacy, consuming time great, can't detect the standing tree height fast convenient is solved. The method comprises the steps of calibrating a camera, obtaining internal parameters of the camera, extracting a standing tree outline of an input image, establishing a geometric model of the distance between an image coordinate system and a world coordinate system, obtaining a standing tree image depth relational expression, establishing a height pixel coordinate mapping model, obtaining a target height relational expression to be measured, and calculating the standing tree height according to the shooting height and angle of the camera. The invention has low measurement cost and high working efficiency, only needs to adopt the existing smart phone and only needs to acquire the shooting angle and the shooting height, and is more convenient and faster to operate. The measuring method does not need complex operation, consumes less time, and has high algorithm precision, high automation degree and simple operation.
Description
Technical Field
The invention relates to the technical field of tree detection, in particular to a standing tree height measuring method based on monocular vision.
Background
The height of trees is one of important signs for evaluating standing tree quality and forest growth conditions, and becomes necessary work in forest resource investigation. Tree measuring tool and algorithm thereof as basic way for obtaining basic tree measuring factorThe accuracy and the practicability are very important, and the modernization level of the method also reflects the modernization level of the forestry[5]. Currently, the mainstream height measuring instruments, such as the Brunays type height measuring instrument[6]The device has the advantages of low price, simple operation, capability of meeting the requirement of general precision, wide application, limitation of measuring horizontal distance, and easiness in being influenced by shielding in actual forest measurement. The three-dimensional laser scanning technology achieves the purpose of quickly, accurately and automatically measuring the height of the tree by quickly and accurately acquiring the three-dimensional coordinate point cloud of an object, but the laser radar measurement has the defects of high equipment cost, difficulty in carrying, long processing time and the like.
In recent years, machine vision measurement technology is rapidly developed, and the depth and the breadth of the machine vision measurement technology in forestry application are increasingly increased. Currently, mainstream vision measurement systems can be divided into two types, which mainly include a monocular vision measurement system and a binocular vision measurement system. The binocular vision measuring system is mainly based on the principle that two cameras are used for shooting images of a target to be measured from different visions, and three-dimensional reconstruction is carried out through corresponding matching points in the two images, so that information of the target to be measured in a three-dimensional space is obtained. Compared with binocular vision, the monocular vision system has the advantages of simple camera calibration process and system construction, and avoids the defect of difficulty in stereo matching in the binocular vision measurement system, so that the monocular vision system becomes one of important research trends in the fields of photogrammetry and computer vision. In practice, the measurement flexibility, image acquisition rapidity and calculation method universality of monocular vision measurement also make the monocular vision measurement widely applied to non-contact measurement in natural environment. The method comprises the steps of utilizing machine vision to obtain three-dimensional phenotypes of plants in a greenhouse, carrying out three-dimensional reconstruction on corn and soybean plants growing in different periods, and carrying out precision evaluation on leaf length and leaf maximum width based on manual measurement values. Yang Kun et al use unmanned aerial vehicle high resolution image, adopt PIXThe 4D software produces three-dimensional point cloud, and the trees are divided into tree point cloud and ground point cloud based on the maximum inter-class variance method, so that the top height of the trees and the average height of the ground are extracted. Comparison meter is carried out according to known calibration object size based on trigonometric function by similar triangle principle when measuring tree height in Zhongke yogaAnd (4) quickly measuring the height of the standing timber to a certain extent. The pipe Fang calculates and extracts the minimum external rectangle of the tree through digital image processing, and calibrates and acquires the internal and external parameters of the camera in the smart phone. And reconstructing the coordinates of the three-dimensional world according to the two-dimensional image information and the known parameters of the camera to realize the measurement of the standing tree height. However, most of the existing domestic tree measuring tools and algorithms thereof have the problems of poor precision, low automation degree, complex operation and the like. The traditional measurement principle and method are still used in the aspect of tree height measurement, and the distance information of the standing tree needs to be measured in advance, in addition, complex operation is mostly needed, the consumed time is large, and the height of the standing tree cannot be measured quickly and conveniently.
Disclosure of Invention
The invention mainly solves the problems that in the prior art, tree height measurement is complex in operation, time consumption is large, and the standing tree height cannot be quickly and conveniently detected, and provides a monocular vision-based standing tree height measurement method.
The technical problem of the invention is mainly solved by the following technical scheme: a standing tree height measuring method based on monocular vision comprises the following steps,
s1, calibrating a camera to obtain internal parameters of the camera;
and the calibration adopts an improved Zhang Zhengyou calibration method, and a camera calibration model with a nonlinear distortion item is introduced for calibration, so that distortion correction is realized and camera internal parameters are obtained. Improved zhangzhengyou scaling method references: liu Yan, Li Tengfei, an improved research on Zhangyinyou camera calibration method, an optical technology, 2014,40(06), 565 and 570. If the image coordinate system (x, y) origin is (u, v) in the pixel coordinate system (u, v)0,v0). Any point (X) in the camera coordinate systemc,Yc,Zc) (x, y, f) projected onto the image coordinate system, the image coordinate system plane being at a distance f from the dots. And from the world coordinates P of the objectw(XW,YW,ZW) To camera coordinate PcThe process of transformation is a rigid body motion that can be described in terms of translation and rotation of an object. Therefore, the conversion relationship from the world coordinate system to the camera coordinate system is as follows:
wherein M isint，MextThe camera external parameters comprise a rotation matrix R and a translation matrix T. dx、dyThe material size (unit: mm) of each on the plane.
S2, performing standing tree contour extraction on the input image to obtain a standing tree height pixel difference;
s3, establishing a geometric model of the distance between the image coordinate system and the world coordinate system to obtain a stumpage image depth relational expression
Wherein d is the depth value from the camera to the target to be measured, h is the vertical horizontal height from the center of the lens to the ground, gamma is the vertical depression angle of the camera, and fyIs the focal length in the y-axis direction of the image plane, v is the y-axis coordinate in the image coordinate system, v0Is the principal point of the y axis of the image plane;
s4, establishing a height pixel coordinate mapping model to obtain a height relation formula of the target to be measured
Wherein H is the standing tree height, yy' is the standing tree height pixel difference;
and S5, calculating the height of the standing tree according to the shooting height and angle of the camera.
According to the invention, the standing tree height is measured by means of a smart phone and a machine vision technology. The method comprises the steps of utilizing a GraphCut algorithm to segment a standing tree image, achieving automatic acquisition of standing tree height pixels in the standing tree image, then automatically acquiring standing tree image depth information through a geometric similarity model, and extracting angle information of a built-in gyroscope of a mobile phone and shooting height information of the mobile phone to calculate the height of a measurement tree. A set of new measuring method is formed, the shooting angle and the shooting height can be obtained only by adopting a camera of the existing smart phone, the distance between the smart phone and a standing tree does not need to be measured in advance, and the operation is more convenient and faster. The measuring method does not need complex operation, consumes less time, and has high algorithm precision, high automation degree and simple operation. The method is low in measurement cost and high in working efficiency, strict hardware conditions are not needed when the tree height measurement is carried out by using the intelligent mobile phone device, the device integration is convenient, and the whole tree height measurement process can be completed by only one person.
Preferably, the camera internal parameters obtained after calibration in step S1 include fx、fy、u0、v0Wherein f isxIs the focal length, u, of the x-axis direction of the image plane0Is the principal point of the x-axis of the image plane.
As a preferable scheme, the specific process of step S2 includes:
s21, preprocessing an input image to obtain an image with outstanding standing tree image characteristics; the preprocessing comprises the steps of transforming the object pixel set by using the property of the point set, the integral geometric set and the topological theory, and expanding and corroding the image.
S22, performing foreground and background segmentation on the preprocessed image to obtain a standing tree contour image;
in the step, a GraphCut algorithm is adopted to carry out foreground segmentation on the image so as to obtain the bottom central point of the standing tree to be detected and the highest point on the standing tree image. The algorithm can effectively overcome the complex background interference in the natural environment under the influence of the natural environment such as different illumination intensities and accurately segment the specific standing trees.
S23, scanning the standing tree outline image to obtain the coordinate distribution condition of the standing tree outline image;
and S24, outputting two minimum points in the standing tree contour image, wherein the two minimum points are respectively the maximum value and the minimum value of the standing tree contour on the y axis under the image plane coordinate system, and calculating the standing tree height pixel difference according to the minimum points. Two maximum points in the standing tree outline image are Smax(x,y)、Smin(x, y), yy' is the pixel difference of the standing tree image in the vertical direction on the image plane. The scheme outputs the two maximum points in the image by scanning the standing tree outline image,as an input in the model for subsequent measurement of the standing timber height.
As a preferable scheme, the process of obtaining the standing tree image depth relational expression in step S3 includes:
s31, establishing a geometric model of the distance between the image coordinate system and the world coordinate system according to the camera imaging principle and the relation between the camera and the ground plane, wherein the geometric model is that a P point and an optical axis are set to form intersection points (x, y) and (x) on the image plane ABCD through the lens center respectively0,y0) (x, y) is the projection point of the point P on the two-dimensional plane figure coordinate system, namely the image plane, (x)0,y0) The distance from the camera to the depth value of a target to be detected, namely the distance from a point P to the center of the lens is d, the distance from the center of the lens to the image plane is f, the vertical horizontal height from the center of the lens to the ground is h, the vertical depression angle of the camera is gamma, and the included angle between the projection of the camera and the optical axis is beta.
S32, deriving a relational expression of the distance between the pixel coordinate and the world coordinate according to the geometric model
Beta in the three-dimensional image, the relational expression of the pixel and the focal length is
Obtained according to equations (1) and (2):
s33, according to the geometric relation between the image coordinates and the pixel coordinates
Obtaining y ═ v-v0) dy, known y0Is equal to 0, to obtain
S34. according to
Obtaining the vertical tree image depth relational expression
As a preferable scheme, the process of obtaining the height relation of the target to be measured in step S4 includes:
s41, establishing a height pixel coordinate mapping model according to the camera pinhole imaging principle, wherein the model is A in an actual imaging plane3A4Imaging in the image plane through the lens center, A3A4To the actual standing height H, A3The imaging point in the imaging plane is xy, A4The imaging point in the image plane is xy ', yy' is the pixel difference of the standing tree image in the vertical direction on the image plane, Q is the lens center, M is the intersection point of the optical axis and the actual imaging plane, QM is the position of the optical axis, the distance from the lens center P to the ideal imaging plane is d, and the vertical depression angle of the camera is gamma;
xy, xy' is the imaging point of the standing tree on the image plane, wherein the maximum value Smax(x, y) and a minimum value of Smin(x, y). The ideal imaging plane is perpendicular surface of water face, and the angle of depression of camera sends the transform along with the demand of measurement, and the actual imaging plane of formation is the slope, and M point also is actual imaging plane and ideal imaging plane intersect simultaneously. QO is the vertical height of the camera from the horizontal plane, the depression angle gamma of the camera is 0 degree under an ideal condition, the optical axis is a horizontal straight line, N is the intersection point of the optical axis and the centrifugal imaging plane under the ideal condition, and QN is the straight line where the optical axis is located under the ideal condition. When the depression angle of the camera changes, the relative included angle between the imaging surfaces can not change.
S42, obtaining the height pixel coordinate mapping model
Wherein
A3A4H, to obtain
S43, combining the vertical wood image depth relational expression to obtain a vertical wood height relational expression
The standing tree height relational expression is obtained by the scheme, the height H of the standing tree is obtained only by obtaining the pixel difference yy 'of the standing tree image on the imaging surface and the depression angle gamma obtained by a high-precision gyroscope inside a camera, wherein the physical unit of H is meter/m, yy' and fyThe material resources of (a) are all in pixels/pixels, and the distance of H is in meters/m. The method does not need to know the motion information of the camera, does not need to carry out auxiliary operation, and has the advantages of short time consumption, quick and convenient operation, and higher accuracy and effectiveness.
Therefore, the invention has the advantages that:
only need adopt current smart mobile phone, acquire shooting angle and shooting height, and need not to record in advance and found the distance of wood, operate convenient and fast more.
The measuring method does not need complex operation, consumes less time, and has high algorithm precision, high automation degree and simple operation.
The tree height measuring device is low in measuring cost and high in working efficiency, strict hardware conditions are not needed when the tree height measuring device is used for tree height measuring, the device integration is convenient, and the whole tree height measuring process can be completed only by one person.
Drawings
FIG. 1 is a schematic diagram of a geometric model of the distance between the image coordinate system and the world coordinate system according to the present invention;
FIG. 2 is a diagram of a transformation model of an image coordinate system and a pixel coordinate system according to the present invention;
FIG. 3 is a schematic diagram of a height pixel coordinate mapping model according to the present invention;
FIG. 4a is an image of a log to be tested according to the present invention;
FIG. 4b is an image of a segmented stumpage profile in accordance with the present invention;
fig. 5 is a graph of relative error comparison in an embodiment of the present invention.
Detailed Description
The technical scheme of the invention is further specifically described by the following embodiments and the accompanying drawings.
Example (b):
the embodiment provides a standing tree height measuring method based on monocular vision, and particularly relates to a standing tree height measuring method based on a smart phone combined with a machine vision technology, wherein the smart phone is provided with a camera and a high-precision gyroscope. The method comprises the steps of segmenting a standing tree image by using a GraphCut algorithm, automatically obtaining standing tree height pixels in the standing tree image, then automatically obtaining standing tree image depth information through a geometric similarity model, extracting angle information of a gyroscope built in a mobile phone and shooting height information of the mobile phone to calculate the height of a measured tree, and providing a set of new measuring method and technical means for forest resource investigation.
The method of the embodiment comprises the following specific steps:
s1, calibrating a camera to obtain internal parameters of the camera;
s2, performing standing tree contour extraction on the input image to obtain a standing tree height pixel difference;
s3, establishing a geometric model of the distance between the image coordinate system and the world coordinate system to obtain the image depth of the standing tree;
s4, establishing a height pixel coordinate mapping model to obtain a height relation of the target to be measured;
and S5, calculating the height of the standing tree according to a relation between the shooting height and the angle of the camera and the height.
In step S1, the calibration is performed by using an improved zhangzhengyou calibration method, and a camera calibration model with a nonlinear distortion term is introduced for calibration, so as to achieve distortion correction and obtain the internal part of the cameraAnd (4) parameters. Improved zhangzhengyou scaling method references: liu Yan, Li Tengfei, an improved research on Zhangyinyou camera calibration method, an optical technology, 2014,40(06), 565 and 570. If the image coordinate system (x, y) origin is (u, v) in the pixel coordinate system (u, v)0,v0). Any point (X) in the camera coordinate systemc,Yc,Zc) (x, y, f) projected onto the image coordinate system, the image coordinate system plane being at a distance f from the dots. And from the world coordinates P of the objectw(XW,YW,ZW) To camera coordinate PcThe process of transformation is a rigid body motion that can be described in terms of translation and rotation of an object. Therefore, the conversion relationship from the world coordinate system to the camera coordinate system is as follows:
wherein M isint，MextThe camera external parameters comprise a rotation matrix R and a translation matrix T. dx、dyThe material size (unit: mm) of each on the plane.
The camera internal parameters obtained by calibration in the method comprise fx、fy、u0、v0。fxFocal length in the x-axis direction of the image plane, fyIs the focal length in the y-axis direction of the image plane, v is the y-axis coordinate in the image coordinate system, u0Is a principal point of the x-axis of the image plane, v0Is the principal point of the y-axis of the image plane.
Step S2 is to perform preprocessing and feature extraction on the input image, and the specific process includes:
s21, preprocessing an input image to obtain an image with outstanding standing tree image characteristics;
due to complex background interference in a natural scene, when the standing tree image is collected, the original image needs to be preprocessed, useful information is highlighted, and useless information is suppressed, so that the image quality is improved to facilitate the extraction of the standing tree image characteristic information.
S22, performing foreground and background segmentation on the preprocessed image to obtain a standing tree contour image;
and performing foreground and background segmentation on the acquired standing tree image by using a Graph Cut algorithm to help obtain the bottom central point of the standing tree to be detected and the highest point of the standing tree on the image. The algorithm can effectively overcome the complex background interference in the natural environment under the influence of the natural environment such as different illumination intensities and accurately segment the specific standing trees.
S23, scanning the standing tree outline image to obtain the coordinate distribution condition of the standing tree outline image; due to the fact that the standing tree to be measured has the conditions of irregular shape, free position and direction and the like in the image, the coordinate distribution condition of the two-dimensional image is scanned.
And S24, outputting two minimum points in the standing tree contour image, namely the maximum value and the minimum value of the standing tree contour in the Y axis under the image plane coordinate system, and calculating the standing tree height pixel difference according to the minimum points. Two maximum points in the standing tree outline image are Smax(x,y)、Smin(x, y), yy' is the pixel difference of the standing tree image in the vertical direction on the image plane. By scanning the standing timber contour image, these two most significant points in the image are output as inputs in a model for subsequent measurement of standing timber height.
In the tree height measurement model, a geometric similarity method is adopted to measure information such as the shape, the two-dimensional position and the like of a target to be measured, and the principle is that when the geometric parameters of the target to be measured are located on the same plane and the image plane of a camera is parallel to the target to be measured, the actual parameters of the target to be measured are obtained through the spatial geometric similarity relation between the target to be measured and the image. Step S3, according to the camera imaging principle and the relation between the camera and the ground plane, a geometric model of the distance between the image coordinate system and the world coordinate system is established, and the image depth information is obtained by adopting a geometric similarity method.
The process of obtaining the standing tree image depth relational expression in step S3 includes:
and S31, establishing a geometric model of the distance between the image coordinate system and the world coordinate system according to the camera imaging principle and the relation between the camera and the ground plane.
As shown in FIG. 1, the geometric model is that the point P and the optical axis are set to form intersection points (x, y), (x) on the image plane ABCD through the lens center respectively0,y0) (x, y) is the projection point of the point P on the two-dimensional plane figure coordinate system, namely the image plane, (x)0,y0) The method is characterized in that the intersection point of an optical axis and an image plane of an image is formed, the distance from a camera to a depth value of a target to be measured, namely a point P to the center of a lens is d, the distance from the center of the lens to the image plane is f, the vertical horizontal height from the center of the lens to the ground is h, the vertical depression angle of the camera is gamma, the included angle between the projection of the camera and the optical axis is beta, and the straight line where the point L is located represents a.
S32, deriving a relational expression of the distance between the pixel coordinate and the world coordinate according to the geometric model
Beta in the three-dimensional image, the relational expression of the pixel and the focal length is
Obtained according to equations (1) and (2):
s33, as shown in figure 2, according to the geometrical relationship between the image coordinates and the pixel coordinates
Obtaining y ═ v-v0) dy, known y0Is equal to 0, to obtain
S34. according toObtaining the vertical tree image depth relational expression
Wherein d is the depth value from the camera to the target to be measured, h is the vertical horizontal height from the center of the lens to the ground, gamma is the vertical depression angle of the camera, and fyIs the focal length in the y-axis direction of the image plane, v is the y-axis coordinate in the image coordinate system, v0Is the principal point of the y-axis of the image plane. v. of0And fyAre all internal parameters of the camera, obtained by the camera calibration of step S1.
The step S4 of obtaining the height relation of the target to be measured includes:
s41, establishing a height pixel coordinate mapping model according to a camera pinhole imaging principle.
As shown in FIG. 3, the model is an actual imaging plane A3A4Imaging in the image plane through the lens center, A3A4To the actual standing height H, A3The imaging point in the imaging plane is xy, A4The imaging point in the image plane is xy ', yy' is the pixel difference of the standing tree image in the vertical direction on the image plane, Q is the lens center, M is the intersection point of the optical axis and the actual imaging plane, QM is the position of the optical axis, the distance from the lens center P to the ideal imaging plane is d, and the vertical depression angle of the camera is gamma;
xy, xy' is the imaging point of the standing tree on the image plane, wherein the maximum value Smax(x, y) and a minimum value of Smin(x,y)。A1A2The imaging plane is an ideal imaging plane, the ideal imaging plane is a vertical water surface plane, the depression angle of the camera is transmitted and converted along with the measurement requirement, the formed actual imaging plane is inclined, the point M is the intersection point of the actual imaging plane and the ideal imaging plane, QO is the vertical height of the camera from the horizontal plane, the depression angle gamma of the camera is 0 degree under the ideal condition, ∠ OQN is 90 degrees, the optical axis is a horizontal straight line, N is the intersection point of the optical axis and the centrifugal imaging plane under the ideal condition, QN is the straight line where the optical axis is under the ideal condition, QN ⊥ A is a straight line where the optical axis is under the ideal condition1A2，A1A2The length represented is the tree height H to be measured in the three-dimensional space. In actual measurement, the depression angle of the camera can change along with the measurement requirement, and the model is mapped according to the height pixel coordinate when the depression angle of the cameraWhen the angle of depression ∠ OQG is γ, ∠ GQM is 90 degrees, QN ⊥ a3A4Therefore ∠ GQM is γ.
S42, obtaining A according to the height pixel coordinate mapping model3A4Formula for height
Wherein
Given as QN ═ d, A3A4H, to obtain
S43, combining the vertical wood image depth relational expression to obtain a vertical wood height relational expression
Wherein the physical units of h are meter/m, yy' and fyThe material resources of (a) are all in pixels/pixels, and the distance of H is in meters/m. The height H of the standing tree is obtained only by obtaining the pixel difference yy' of the standing tree image on the imaging surface and the depression angle 2 obtained by a high-precision gyroscope inside the camera.
The present embodiment will be specifically described below using a mobile phone with millet having a model number of MI 2S. The Android version of the mobile phone is 4.1.2. After the camera is frozen, the internal parameters of the camera are obtained as follows: f. ofx＝3486.5637，u0＝1569.0383，fy＝3497.4652，v02107.9898, the vertical height h of the mobile phone from the ground is 1.10cm, and the image resolution is 3120 × 4208. And measuring the actual height of the standing tree to be 6.2 meters by taking the measured data as a true height value of the standing tree. As shown in fig. 4a, a standing tree image to be measured is input, the standing tree image is segmented by using Graph Cut through an image processing technology, and as shown in fig. 4b, a standing tree contour image is output.
The maximum value S in the Y-axis direction can be obtained by scanning the coordinate distribution of the standing tree profile imagemax(977,365) and a minimum value Smin(1299,3463), yy' 3144.6897 is obtained, where γ is 10.0 ° for the angle of depression of the phone, and h is 1.10m for the vertical height of the phone from the ground. The standing timber height H is 6.2815m according to the standing timber height relational expression.
In order to verify the precision of the tree height measurement model between sample trees with different heights, the present embodiment uses a common sample tree height range as a sample tree interval, and measures the height of the standing tree to be measured from different angles. The trees are numbered and compared and analyzed with a standing tree height extraction method (Yangkun, Zhaoyingling, Zhanjiayong, and the like) based on high-resolution influence of an unmanned aerial vehicle, the tree height extraction is carried out by utilizing a high-resolution image of the unmanned aerial vehicle, the university of Beijing forestry, 2017,39(8):17-23, which is hereinafter referred to as a comparison method). The results are shown in Table 1.
TABLE 1 standing tree height measurement data table
As shown in figure 5, the measurement relative error range is calculated to be-0.029-0.338 m, the highest relative error of the standing tree height measurement is 4.22%, and the lowest relative error is 0.30%. The highest relative error of the comparative method is 16.23%, and the lowest relative error is 0.33%. The error of the method of the embodiment is only 13.64% and is more than 6%, and the relative error of 72.74% of the standing tree height measuring method based on the comparison method exceeds 6%, so that the method has large fluctuation. Through comparative analysis, the method provided by the embodiment can be used for measuring the standing tree height with higher precision and more stable measurement result. When the mobile phone camera is used for image acquisition, the rotation angle of the mobile phone is selected to ensure that the image of the standing tree to be measured can be completely imaged on the imaging surface of the mobile phone camera, and simultaneously, the pixel at the bottom end of the standing tree to be measured in the image is imaged on the main point of the optical center plane, so that the height of the standing tree to be measured can be accurately measured. In fig. 5, the method of this embodiment is the present method, and the method of reference (15) is the comparison method.
The specific embodiments described herein are merely illustrative of the spirit of the invention. Various modifications or additions may be made to the described embodiments or alternatives may be employed by those skilled in the art without departing from the spirit or ambit of the invention as defined in the appended claims.
Claims (5)
1. A standing tree height measuring method based on monocular vision is characterized in that: comprises the following steps of (a) carrying out,
s1, calibrating a camera to obtain internal parameters of the camera;
s2, performing standing tree contour extraction on the input image to obtain a standing tree height pixel difference;
s3, establishing a geometric model of the distance between the image coordinate system and the world coordinate system to obtain a stumpage image depth relational expression
Wherein d is the depth value from the camera to the target to be measured, h is the vertical horizontal height from the center of the lens to the ground, gamma is the vertical depression angle of the camera, and fyIs the focal length in the y-axis direction of the image plane, v is the y-axis coordinate in the image coordinate system, v0Is the principal point of the y axis of the image plane;
s4, establishing a height pixel coordinate mapping model to obtain a height relation formula of the target to be measured
Wherein H is the standing tree height, yy' is the standing tree height pixel difference;
and S5, calculating the height of the standing tree according to the shooting height and angle of the camera.
2. The method as claimed in claim 1, wherein the camera internal parameters obtained after calibration in step S1 include fx、fy、u0、v0Wherein f isxIs the focal length, u, of the x-axis direction of the image plane0Is the principal point of the x-axis of the image plane.
3. The standing tree height measuring method based on the monocular vision as claimed in claim 1, wherein the step S2 comprises the following steps:
s21, preprocessing an input image to obtain an image with outstanding standing tree image characteristics;
s22, performing foreground and background segmentation on the preprocessed image to obtain a standing tree contour image;
s23, scanning the standing tree outline image to obtain the coordinate distribution condition of the standing tree outline image;
and S24, outputting two minimum points in the standing tree contour image, wherein the two minimum points are respectively the maximum value and the minimum value of the standing tree contour on the y axis under the image plane coordinate system, and calculating the standing tree height pixel difference according to the minimum points.
4. The method for measuring the height of the standing tree based on the monocular vision as claimed in claim 1, wherein the step of obtaining the depth relation of the standing tree image in the step S3 comprises:
s31, establishing a geometric model of the distance between the image coordinate system and the world coordinate system according to the camera imaging principle and the relation between the camera and the ground plane, wherein the geometric model is that a P point and an optical axis are set to form intersection points (x, y) and (x) on the image plane ABCD through the lens center respectively0,y0) (x, y) is the projection point of the point P on the two-dimensional plane figure coordinate system, namely the image plane, (x)0,y0) The distance from the camera to the depth value of a target to be detected, namely the distance from a point P to the center of the lens is d, the distance from the center of the lens to the image plane is f, the vertical horizontal height from the center of the lens to the ground is h, the vertical depression angle of the camera is gamma, and the included angle between the projection of the camera and the optical axis is beta.
S32, deriving a relational expression of the distance between the pixel coordinate and the world coordinate according to the geometric model
Beta in the three-dimensional image, the relational expression of the pixel and the focal length is
Obtained according to equations (1) and (2):
s33, according to the geometric relation between the image coordinates and the pixel coordinates
Obtaining y ═ v-v0) dy, known y0Is equal to 0, to obtain
S34. according to
Obtaining the vertical tree image depth relational expression
5. The method for measuring the height of the standing tree based on the monocular vision as claimed in claim 4, wherein the step S4 of obtaining the relation of the height of the object to be measured comprises:
s41, establishing a height pixel coordinate mapping model according to the camera pinhole imaging principle, wherein the model is A in an actual imaging plane3A4Imaging in the image plane through the lens center, A3A4To the actual standing height H, A3In the image planeThe imaging point is xy, A4The imaging point in the image plane is xy ', yy' is the pixel difference of the standing tree image in the vertical direction on the image plane, Q is the lens center, M is the intersection point of the optical axis and the actual imaging plane, QM is the position of the optical axis, the distance from the lens center P to the ideal imaging plane is d, and the vertical depression angle of the camera is gamma;
s42, obtaining the height pixel coordinate mapping model
Wherein
A3A4H, to obtain
S43, combining the vertical wood image depth relational expression to obtain a vertical wood height relational expression
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