CN109345554A - A kind of adhesion mushroom vision in-situ measuring method based on RGB-D camera - Google Patents
A kind of adhesion mushroom vision in-situ measuring method based on RGB-D camera Download PDFInfo
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- CN109345554A CN109345554A CN201811083567.4A CN201811083567A CN109345554A CN 109345554 A CN109345554 A CN 109345554A CN 201811083567 A CN201811083567 A CN 201811083567A CN 109345554 A CN109345554 A CN 109345554A
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- G—PHYSICS
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/136—Segmentation; Edge detection involving thresholding
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
- G01B11/08—Measuring arrangements characterised by the use of optical techniques for measuring diameters
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
- G01B11/22—Measuring arrangements characterised by the use of optical techniques for measuring depth
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
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- G06V10/40—Extraction of image or video features
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Abstract
The invention discloses a kind of adhesion mushroom vision in-situ measuring method based on RGB-D camera, using RGB-D camera continuous acquisition deep video stream；Dynamic threshold segmentation is carried out to collected each frame depth image, to remove Soil Background, extracts mushroom connected domain and smooth edges；To mushroom connected domain its boundary profile of eight neighborhood tracing order traversal of extraction, based on its center of circle of circular fit Preliminary detection and radius, sequence is extracted the boundary point around the center of circle in 1.3 times of radius and is transformed under polar coordinates, it is denoised between the adhesion point found, interpolation, and then obtains the two-dimensional coordinate of monomer mushroom boundary profile；Thus calibration camera coordinate system calculates the position of monomer mushroom, diameter, angle of declination in camera world coordinate system based on the precision of ceramic plectane verifying in-situ measuring method.The inventive method accurately can quickly identify the similar round mushroom of adhesion, and picking robot crawl fruit registration is high, and runing time is short, and real-time is higher.
Description
Technical field
The present invention relates to field of machine vision, and in particular to the image recognition and Measurement Algorithm of fruit and vegetable picking robot, it is special
It is not the vision in-situ measuring method to the round mushroom of class.
Background technique
The mushroom of industrialized cultivation is full of nutrition, since the market price of mushroom is related with size, it is necessary to mushroom
Mushroom carries out selective picking, and worker climbs on the spot, picks round the clock, large labor intensity, need to study one kind can substitute worker into
The intelligent Mushroom Picking Robot equipment of the original position on-line measurement of row mushroom, picking.
Batch production mushroom planting environment is moist, and illumination is dark weak irregular, there is 2 mushroom framves, each mushroom in each mushroom house
Frame has 6 layers, and each layer has 18 mushroom beds, and picking robot is along mushroom frame automatic sliding, stop, lifting in mushroom bedside, machinery
The S-type traversal mushroom bed of arm, the camera for being mounted on mechanical arm tail end acquire mushroom bed video flowing on the way, are realized by image processing algorithm
The quick positioning and in situ measurement of mushroom, provide parameter for mechanical arm programming movement track.
The quick positioning of mushroom has following three difficult points in situ measurement algorithm: (1) being mixed with a large amount of bacterium in the upper soil of mushroom bed
The gray scale of silk, mushroom and soil does not differ significantly.Existing research mainly uses two-dimensional color camera, is based on gray threshold
Method successfully eliminate with the apparent Soil Background of mushroom gray difference, more successfully eliminated based on Harris textural characteristics
With the approximate Soil Background of mushroom gray scale, but the Soil Background of large area mycelia is mixed with without removing.(2) go out the mushroom of stubble in soil
Inclination, the adhesion of all directions is presented in mushroom disorganised outgrowths.Existing research is typically based on range image, watershed algorithm
Equal matrix operation identifies adhesion mushroom, and computing cost is big, not good enough to the recognition effect of overlapping mushroom.(3) mushroom quickly position with
The accuracy of in situ measurement.Forefathers are in the application studies such as the positioning of mushroom, measurement, maturity and damage differentiation, based on two dimension
The size of image coordinate system and ellipse fitting measurement mushroom is inaccurate.
Summary of the invention
For deficiency existing for prior art, the present invention provides a kind of, and the adhesion mushroom vision based on RGB-D camera is former
Position measurement method.
The technical solution adopted in the present invention, steps are as follows:
Step 1, mushroom image acquires: using RGB-D camera continuous acquisition deep video stream；
Step 2, mushroom image is divided: dynamic threshold segmentation is carried out to collected each frame depth image, to remove soil
Earth background extracts mushroom connected domain and smooth edges；
Step 3, adhesion mushroom identifies: to mushroom connected domain its boundary profile of eight neighborhood tracing order traversal of extraction
B, is based on its center of circle of circular fit Preliminary detection and radius, and sequence extracts the boundary point wheel around the center of circle in 1.3 times of radius
Wide sub_b is simultaneously transformed under polar coordinates, is denoised between the adhesion point found, interpolation, and then obtains monomer mushroom boundary
The two-dimensional coordinate of profile；
Step 4, mushroom in situ measurement: calibration camera coordinate system verifies the precision of in-situ measuring method based on ceramic plectane,
Thus the position of monomer mushroom, diameter, angle of declination in camera world coordinate system are calculated.
Further, the process of dynamic threshold segmentation is carried out in the step 2 to acquired image are as follows:
Step 2.1: the mean value of non-zero grey scale pixel value in depth image is calculated, the mushroom bed region greater than this mean value is taken, it is right
The depth value of these area pixels is marked；
Step 2.2: counting the frequency that each depth value occurs, taking the maximum depth of frequency is depth of soil, adaptively
Dynamic threshold is selected, binaryzation is carried out to depth image.
Further, in the step 3 circular fit process are as follows:
Step 3.1, it detects the center of circle: marking the connected domain in binary map and its side is detected by Canny edge detection algorithm
Edge point, draws the normal of all marginal points, given threshold 30, and the summation line number amount at normal joint is greater than the threshold value
The step of point as center of circle, Canny edge detection algorithm, is as follows:
Convolution is made with noise reduction to initial data and Gaussian smoothing template, finds gradient, detection level, vertical and diagonal
Edge, generate in image the direction of each brightness gradient map and brightness step from original image to find intensity ladder
Degree obtains bianry image by hysteresis threshold, and the improvement realization at an acquisition sub-pixel precision edge is detected in gradient direction
The zero crossing of Second order directional:
It meets sign condition in three rank directional derivatives of gradient direction:
Wherein: Lx, Ly…LyyyThe partial derivative that expression is calculated with the scale space that the smooth original image of Gaussian kernel obtains, obtains
To edge segments be full curve；
Step 3.2, it derives radius: calculating a certain center of circle to the distance of all marginal points, and determine least radius threshold value, maximum
Radius threshold carries out radius sequence to circle to be detected in the radius defined by and successively counts with minimum distance of center circle, if
Determining count threshold is 30, and the radius that votes are greater than the threshold value is the radius of circle detected.
Compared with prior art, the invention has the advantages that being directed to batch production mushroom cropping pattern, the method for the present invention energy
Enough quickly identification and measurement adhesion mushroom, error is small, and runing time is short, and real-time is higher.
Detailed description of the invention
Fig. 1 is the adhesion mushroom vision in situ measurement flow chart based on RGB-D camera.
Fig. 2 is mushroom image segmentation.
Fig. 3 is the identification of adhesion mushroom, wherein Fig. 3 a is the image after circular fit, and Fig. 3 b is accurate fitting monomer mushroom
The image on boundary.
Specific embodiment
The invention patent specific flow chart as shown in Figure 1, with reference to the accompanying drawing to the specific steps of the invention patent make into
The explanation of one step.
1, mushroom image acquires
The present invention uses RGB-D camera continuous acquisition image, and video acquisition speed is 60 frames/second.
2, mushroom image is divided
This patent calculates first image the mean value of non-zero grey scale pixel value in depth image, represents soil greater than the mean value
The gray value of surface point cloud represents the depth value of soil surface point cloud with its mode, the mushroom height rised sheer from level ground from soil
At least 20mm sets dynamic threshold, carries out binaryzation to depth image, it is ensured that the mushroom region for extracting special diameter, to two
Value figure carries out the edges smoothing processings such as morphology opening operation, gaussian filtering, and 0 noise is caused also to fall on mushroom boundary profile.Knot
Fruit is as shown in Figure 2.
3, adhesion mushroom identifies
To connected domain eight neighborhood tracing order traversal its boundary profile b of extraction, it is based on circular fit Preliminary detection
Its center of circle and radius, sequence extract the boundary dot profile sub_b around the center of circle in 1.3 times of radius and are transformed into polar coordinates
Under, it is denoised between the adhesion point found, interpolation, and then obtain the two-dimensional coordinate of monomer mushroom boundary profile.Adhesion mushroom
Mushroom recognition effect is as shown in Figure 3.
The algorithm steps of circular fit are as follows:
Detect the center of circle.It marks the connected domain in binary map and its marginal point is detected by Canny edge detection algorithm, draw
The normal of all marginal points, given threshold 30, the point that the summation line number amount at normal joint is greater than the threshold value is to justify
The step of heart, Canny edge detection algorithm, is as follows:
Convolution is made with noise reduction to initial data and Gaussian smoothing template, finds gradient, detection level, vertical and diagonal
Edge, generate in image the direction of each brightness gradient map and brightness step from original image to find intensity ladder
Degree obtains bianry image by hysteresis threshold, and the improvement realization at an acquisition sub-pixel precision edge is detected in gradient direction
The zero crossing of Second order directional:
It meets sign condition in three rank directional derivatives of gradient direction:
Wherein: Lx, Ly…LyyyThe partial derivative that expression is calculated with the scale space that the smooth original image of Gaussian kernel obtains, obtains
To edge segments be full curve.
Derive radius.The a certain center of circle is calculated to the distance of all marginal points, and determines least radius threshold value, maximum radius threshold value
With minimum distance of center circle, radius sequence is carried out to circle to be detected in the radius defined by and is successively counted, counting threshold is set
Value is 30, and the radius that votes are greater than the threshold value is the radius of circle detected.
4, mushroom in situ measurement
The world coordinate system of calibration camera, verify camera world coordinate system under in situ measurement ceramics plectane center location,
Diameter, the deviation angle and the precision at inclination angle.
Claims (4)
1. a kind of adhesion mushroom vision in-situ measuring method based on RGB-D camera, which comprises the following steps:
Step 1, mushroom image acquires: using the continuous deep video stream of RGB-D camera；
Step 2, mushroom image is divided: the mean value of non-zero grey scale pixel value first in calculating depth image is greater than the mean value and represents soil
The gray value of earth surface point cloud represents the depth value of soil surface point cloud with its mode, and the mushroom rised sheer from level ground from soil is high
At least 20mm is spent, dynamic threshold is set, binaryzation is carried out to depth image, it is ensured that the mushroom region of special diameter is extracted, it is right
Binary map carries out the edges smoothing processings such as morphology opening operation, gaussian filtering, and 0 noise is caused also to fall on mushroom boundary profile；
Step 3, adhesion mushroom identifies: to connected domain eight neighborhood tracing order traversal its boundary profile b of extraction, based on circle
Shape is fitted its center of circle of Preliminary detection and radius, and sequence extracts the boundary dot profile sub_b around the center of circle in 1.3 times of radius simultaneously
It is transformed under polar coordinates, is denoised between the adhesion point found, interpolation, and then obtain the two dimension of monomer mushroom boundary profile
Coordinate；
Step 4, mushroom in situ measurement: the center location of in situ measurement ceramics plectane under verifying camera world coordinate system, diameter, partially
To the precision at angle and inclination angle.
2. the adhesion mushroom vision in-situ measuring method according to claim 1 based on RGB-D camera, which is characterized in that
The step 2 improves process are as follows:
Step 2.1: calculating the mean value of non-zero grey scale pixel value in depth image, the mushroom bed region greater than this mean value is taken, to these
The depth value of area pixel is marked；
Step 2.2: counting the frequency that each depth value occurs, taking the maximum depth h of frequency is depth of soil, is adaptive selected
Dynamic threshold carries out binaryzation to depth image.
3. the mushroom method for quickly identifying according to claim 1 based on RGB-D camera, which is characterized in that the step 3
Improve process are as follows:
Step 3.1, it detects the center of circle: marking the connected domain in binary map and its marginal point is detected by Canny edge detection algorithm,
Draw the normal of all marginal points, given threshold 30, the point that the summation line number amount at normal joint is greater than the threshold value is
For the center of circle, the step of Canny edge detection algorithm, is as follows:
Convolution is made with noise reduction to initial data and Gaussian smoothing template, finds gradient, detection level, vertical and diagonal
Edge, generate in image the direction of each brightness gradient map and brightness step from original image to find intensity ladder
Degree obtains bianry image by hysteresis threshold, and the improvement realization at an acquisition sub-pixel precision edge is detected in gradient direction
The zero crossing of Second order directional:
It meets sign condition in three rank directional derivatives of gradient direction:
Wherein: Lx, Ly...LyyyThe partial derivative that expression is calculated with the scale space that the smooth original image of Gaussian kernel obtains, obtains
To edge segments be full curve；
Step 3.2, it derives radius: calculating a certain center of circle to the distance of all marginal points, and determine least radius threshold value, maximum radius
Threshold value carries out radius sequence to circle to be detected in the radius defined by and successively counts, set meter with minimum distance of center circle
Number threshold value is 30, and the radius that votes are greater than the threshold value is the radius of circle detected.
4. the mushroom method for quickly identifying according to claim 1 based on RGB-D camera, which is characterized in that the step 4
Improve process are as follows:
Step 4.1: calibration camera coordinate system；
Step 4.2: thus the precision based on ceramic plectane verifying in-situ measuring method calculates monomer mushroom in camera world coordinate system
The position of mushroom, diameter, angle of declination.
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| CN111105394A (en) * | 2019-11-27 | 2020-05-05 | 北京华捷艾米科技有限公司 | Method and device for detecting characteristic information of luminous ball |
| CN111445482A (en) * | 2020-03-24 | 2020-07-24 | 上海第二工业大学 | Segmentation and identification method of overlapped agaricus bisporus |
| CN111862043A (en) * | 2020-07-21 | 2020-10-30 | 北京林业大学 | Mushroom detection method based on laser and machine vision |
| CN112037287A (en) * | 2020-08-26 | 2020-12-04 | 深圳市广宁股份有限公司 | Camera calibration method, electronic device and storage medium |
| CN112270652A (en) * | 2020-10-19 | 2021-01-26 | 西安工程大学 | Method for detecting existence of yarn tube in yarn storage type bobbin winder |
| CN112418323A (en) * | 2020-11-24 | 2021-02-26 | 哈尔滨市科佳通用机电股份有限公司 | Railway wagon coupler knuckle pin fault detection method based on image processing |
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| CN114087989A (en) * | 2021-11-19 | 2022-02-25 | 江苏理工学院 | Method and system for measuring three-dimensional coordinates of circle center of workpiece positioning hole of automobile cylinder |
| CN114199139A (en) * | 2021-11-16 | 2022-03-18 | 国网安徽省电力有限公司电力科学研究院 | Method and equipment for detecting thickness of cable insulating layer |
| CN114838664A (en) * | 2022-07-04 | 2022-08-02 | 江西农业大学 | In-situ pileus size measuring method based on black-skin termitomyces albuminosus |
| CN114838665A (en) * | 2022-07-04 | 2022-08-02 | 江西农业大学 | Size in-situ measurement method based on black-skin termitomyces albuminosus |
| CN116147508A (en) * | 2022-10-18 | 2023-05-23 | 广西科技大学 | Visual measurement method for sugarcane height of sugarcane harvester |
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| CN110631475A (en) * | 2019-05-31 | 2019-12-31 | 安琳 | On-site target size detection method |
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| CN111105394A (en) * | 2019-11-27 | 2020-05-05 | 北京华捷艾米科技有限公司 | Method and device for detecting characteristic information of luminous ball |
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| CN111445482A (en) * | 2020-03-24 | 2020-07-24 | 上海第二工业大学 | Segmentation and identification method of overlapped agaricus bisporus |
| CN111445482B (en) * | 2020-03-24 | 2023-03-28 | 上海第二工业大学 | Segmentation and identification method of overlapped agaricus bisporus |
| CN111862043A (en) * | 2020-07-21 | 2020-10-30 | 北京林业大学 | Mushroom detection method based on laser and machine vision |
| CN112037287A (en) * | 2020-08-26 | 2020-12-04 | 深圳市广宁股份有限公司 | Camera calibration method, electronic device and storage medium |
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| CN114087989A (en) * | 2021-11-19 | 2022-02-25 | 江苏理工学院 | Method and system for measuring three-dimensional coordinates of circle center of workpiece positioning hole of automobile cylinder |
| CN114087989B (en) * | 2021-11-19 | 2023-09-22 | 江苏理工学院 | Method and system for measuring three-dimensional coordinates of circle center of positioning hole of automobile cylinder workpiece |
| CN114838665B (en) * | 2022-07-04 | 2022-09-02 | 江西农业大学 | Size in-situ measurement method based on black-skin termitomyces albuminosus |
| CN114838665A (en) * | 2022-07-04 | 2022-08-02 | 江西农业大学 | Size in-situ measurement method based on black-skin termitomyces albuminosus |
| CN114838664A (en) * | 2022-07-04 | 2022-08-02 | 江西农业大学 | In-situ pileus size measuring method based on black-skin termitomyces albuminosus |
| CN116147508A (en) * | 2022-10-18 | 2023-05-23 | 广西科技大学 | Visual measurement method for sugarcane height of sugarcane harvester |
| CN116147508B (en) * | 2022-10-18 | 2023-09-19 | 广西科技大学 | Visual measurement method for sugarcane height of sugarcane harvester |
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