CN109410238B - Wolfberry identification and counting method based on PointNet + + network - Google Patents

Wolfberry identification and counting method based on PointNet + + network Download PDF

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CN109410238B
CN109410238B CN201811098583.0A CN201811098583A CN109410238B CN 109410238 B CN109410238 B CN 109410238B CN 201811098583 A CN201811098583 A CN 201811098583A CN 109410238 B CN109410238 B CN 109410238B
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pointnet
point
layer
point cloud
network
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CN109410238A (en
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贾秀芳
王儒敬
李伟
谢成军
孙丙宇
黄河
王雪
李娇娥
徐玲玲
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Hefei Institutes of Physical Science of CAS
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/136—Segmentation; Edge detection involving thresholding
    • G06F18/23213—
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10028—Range image; Depth image; 3D point clouds
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20112—Image segmentation details
    • G06T2207/20152—Watershed segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30181—Earth observation
    • G06T2207/30188—Vegetation; Agriculture
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30242—Counting objects in image

Abstract

The invention relates to a wolfberry identification and counting method based on a PointNet + + network, which solves the problem that a two-dimensional image identification method is difficult to accurately identify due to shielding overlapping before picking of wolfberry fruits in the prior art. The invention comprises the following steps: collecting and preprocessing a PointNet + + network training sample; acquiring a PointNet + + model based on the PointNet + + network fusion context information; acquiring and preprocessing point cloud data to be identified; and identifying and counting the number of the Chinese wolfberry. The invention integrates a PointNet + + network based on context information and an optimal threshold watershed segmentation algorithm based on distance transformation, and realizes accurate segmentation and counting of the medlar.

Description

Wolfberry identification and counting method based on PointNet + + network
Technical Field
The invention relates to the technical field of image recognition, in particular to a medlar recognition counting method based on a PointNet + + network.
Background
In the planting process of the medlar, the region with low yield can be modified pertinently by accurately predicting the medlar yield, factors such as soil, variety or water content are adjusted, and resources such as manpower, material resources, storage and the like required in harvesting are reasonably arranged.
Currently, in fruit target identification and yield estimation, two-dimensional images are mostly used as data input for yield measurement. However, in practical application, because the medlar particles are small, and most of medlar fruits have the problems of shielding and overlapping before picking, the depth learning method based on the two-dimensional image is low in production efficiency and poor in robustness, and the accuracy of the method is difficult to guarantee.
Therefore, how to design a new identification and counting method to improve the accuracy of identification and counting of the medlar becomes a technical problem to be solved urgently.
Disclosure of Invention
The invention aims to solve the defect that the two-dimensional image recognition method is difficult to accurately recognize due to shielding overlapping before picking of medlar fruits in the prior art, and provides a medlar recognition and counting method based on a PointNet + + network to solve the problem.
In order to achieve the purpose, the technical scheme of the invention is as follows:
a wolfberry identification and counting method based on a PointNet + + network comprises the following steps:
11) collecting and preprocessing PointNet + + network training samples: acquiring 18 pictures and corresponding context information of each Chinese wolfberry tree, wherein the context information comprises time, space, temperature and phenological period information, constructing a three-dimensional model through the 18 pictures to obtain three-dimensional point cloud, and using the three-dimensional point cloud together with the context information as training data;
12) acquiring a PointNet + + model based on the PointNet + + network fusion context information:
setting a PointNet + + network, and setting a sampling layer, a grouping layer, a PointNet layer and a segmentation layer of the PointNet + + network; training a PointNet + + network, introducing time, space, temperature and phenological period context information when point cloud data are obtained as characteristic data, and training a PointNet + + model for segmenting the three-dimensional point cloud data of the Chinese wolfberry;
13) acquiring and preprocessing point cloud data to be identified: acquiring 18 pictures of a Chinese wolfberry tree to be identified and corresponding time, space, temperature and phenological period context information of the pictures, constructing a three-dimensional model and acquiring a three-dimensional point cloud to be identified;
14) identification and counting of the number of the medlar:
inputting the three-dimensional point cloud to be identified and the contextual information as the characteristics into a PointNet + + model to obtain the score of the medlar target, and counting the number of the medlar by a marking method.
The method for setting the PointNet + + network comprises the following steps:
21) setting a PointNet + + network to sequentially comprise a sampling layer, a grouping layer and a hierarchy combination of PointNet layers;
22) setting a sampling layer as a selection center;
obtaining central points by using a k-means + + clustering method, wherein the number of the central points in the first layer is m;
X＝{x1,x2,......,xnis the input set of labeled points,
wherein x isi∈Rd+CD is a point cloud dimension, and C is a characteristic dimension;
if x is taken randomlyk∈Rd+CThe first cluster center is then followed by centeringThe probability reaches a maximum value, where D (x)j) Is xjDistance from the nearest one of the cluster centers;
23) setting a grouping layer, wherein the grouping layer is used for creating a plurality of sub-point clouds by adjacent points in a given radius;
231) obtaining a given radius;
232) searching for fixed adjacent points by using a kNN algorithm and taking the clustering center as a reference, wherein the radiuses are respectively 0.1, 0.2 and 0.4, and the corresponding maximum points in the circle are 16, 32 and 128;
the input point set of the first layer is n x (d + C), the output point set is n' × kx (d + C), wherein k is the number of points contained in the neighborhood;
24) setting a PointNet layer, wherein the PointNet layer is used for obtaining higher dimensional representation of the sub-point cloud;
setting a point set n ' × kx (d + C) as the input of a PointNet + + network, performing feature extraction on the point set, and finally outputting n ' × (d + C ');
repeating the steps 22), 23) and 24) three times, i.e. combining 3 network layers1、l2、l3A module for continuously extracting the features;
25) setting a segmentation layer, and obtaining the score of each original point by adopting an interpolation and backtracking mode based on context information;
251) input l3Point set of layers n '× (d + C');
252) for l2Point of layer consisting of3After interpolation of the layer points, carrying out convolution of 1 x 1 with the corresponding characteristics obtained in 24) to finally obtain l2The values of the layer points are traced all the time to finally obtain l1A score of the origin;
253) while interpolation is carried out, context information of input corresponding points is taken as characteristic data and introduced into the Unit PointNet + + network of the last layer;
Figure GDA0001894222710000031
for all the context information fused, the first layer input of the multi-layer perceptron is
H1＝σ(W1xi+UD+b1)，
The rest layers are Hm＝σ(WmHm-1+bm) Wherein M ∈ {2,..., M };
254) acquisition Point (x)p,yp,zp)∈RCProbability P of belonging to target medlar class or background classc'(xp,yp,zp) It is expressed as follows:
wherein c is { target matrimony vine class, background class },
Figure GDA0001894222710000033
for the weights of the last layer belonging to class c, all parameters (W, U, b) are learned by minimizing the cross entropy loss function.
The PointNet + + network training method comprises the following steps:
31) inputting the three-dimensional point cloud into a sampling layer to obtain a central point of the three-dimensional point cloud;
32) inputting the central point of the three-dimensional point cloud into a grouping layer to obtain a plurality of sub-point clouds in the radius of the central point of the three-dimensional point cloud;
33) inputting a plurality of sub-point clouds into a PointNet + + network, and inputting corresponding characteristic data into a multilayer perceptron of the PointNet + + network for characteristic extraction;
34) repeating step 31), step 32) and step 33) three times in this order, i.e. 3 modules are assembled with such a network layer;
35) and obtaining the probability of the original point serving as the medlar or the background by adopting an interpolation and backtracking mode.
The identification and counting of the number of the medlar also comprises the step of accurately segmenting point cloud: on the basis of distance conversion, a point cloud is segmented by using an optimal threshold watershed segmentation algorithm, and the number of the Chinese wolfberries is counted by using a marking method;
the accurate segmentation of the point cloud comprises the following steps:
41) reconstructing the distance point cloud to distinguish the boundary points and the internal points of the medlar;
setting N (P) as an eight neighborhood point set, P (x)p,yp,zp) Representing the gray value of the point P, P' (x)p,yp,zp) Representing the probability, P ″ (x), that point P belongs to the target Lycium classp,yp,zp) A value representing the distance of the point p from its nearest background point;
reconstructing a point cloud of distances, an order
Wherein the content of the first and second substances,
| represents the euclidean distance between points p and q;
if distmin＜P”(xp,yp,zp) Let P "(x)p,yp,zp)＝distminThe distance point cloud constructed at this time has a value of P ″ (x)p,yp,zp) A set of (a);
42) establishing a watershed segmentation algorithm based on an optimal threshold;
421) set of distances P "(x) to reconstructp,yp,zp) The distance value of the internal points of the Chinese wolfberry is large, the distance value corresponding to the boundary point is small, and the distance value of the background point is zero; finding out local maximum value points on all distances and endowing the local maximum value points with unique identification;
422) adopting a descending method to descend on plain, descending all points until meeting the marked area, and adding the points into the marked area;
423) combining the areas, and combining the areas with the depth lower than a threshold value;
424) determining a watershed segmentation result;
43) and (4) a target mark counting method is adopted to obtain a segmentation result in the final point cloud, and the number is counted to obtain the number of the actual medlar in the point cloud.
The area merging comprises the following steps:
51) for each region, finding out an inner curvature minimum point, a region adjacent to the region and a minimum point on the boundary of each adjacent region;
52) calculating the watershed depth of each region;
53) calculating an optimal threshold value;
let the point cloud have L reconstructed distance values, the point with distance value i has niNumber of individuals and total pointsIs N, the probability of each distance value occurring isAnd is
The threshold value is set as t, the point cloud is divided into 2 parts, namely a background class a {0, 1, 2,. and t } and a medlar target class B { t +1, t +2,. and L-1} based on the distance value, and the probability of occurrence of the two classes is respectively:
the distance mean values of the two types A and B are respectivelyThe total distance mean in the point cloud is:
from this, the between-class variance of the two parts A and B is obtained
The larger the inter-class variance is, the larger the difference of the distance values between the two classes is, that is, the optimal threshold is determined as follows:
54) when the depth is lower than the threshold value, combining the region and the region adjacent to the boundary curvature minimum value point and updating the related information;
55) repeating 54) until the depth of all the areas is greater than a given threshold.
Advantageous effects
Compared with the prior art, the wolfberry identification and counting method based on the PointNet + + network integrates the PointNet + + network based on context information and the optimal threshold watershed segmentation algorithm based on distance transformation, and realizes accurate segmentation and counting of the wolfberry.
The method is based on three-dimensional point cloud, utilizes a PointNet + + network based on context information and an improved watershed segmentation algorithm to carry out accurate segmentation on the medlar, overcomes the defect that two-dimensional image processing and recognition technology is simply utilized to carry out prediction, and can accurately predict the yield of the medlar in the planting base.
The invention not only considers relevant context information (time, space, temperature and phenological period), but also provides an optimal watershed segmentation algorithm improved based on a reconstruction distance to solve the problem of accurate segmentation of the medlar aiming at the problem of inaccuracy of segmentation of overlapped and shielded medlar by using a PointNet + + network, thereby improving the medlar detection capability of a medlar planting base under a complex natural condition and improving the accuracy of medlar yield prediction.
Drawings
FIG. 1 is a sequence diagram of the method of the present invention.
Detailed Description
In order to further understand and appreciate the structural features and advantages achieved by the present invention, a preferred embodiment is described in detail with reference to the accompanying drawings, in which:
as shown in fig. 1, the method for identifying and counting medlar based on PointNet + + network according to the present invention includes the following steps:
the first step, collecting and preprocessing a PointNet + + network training sample.
The method comprises the steps of obtaining 18 pictures of each Chinese wolfberry tree and corresponding context information, wherein the context information comprises time, space, temperature and phenological period information, constructing a three-dimensional model through the 18 pictures according to the existing method to obtain three-dimensional point cloud, and using the three-dimensional point cloud together with the context information as training data.
And secondly, acquiring a PointNet + + model based on the PointNet + + network fusion context information.
First, a PointNet + + network is set, and a sampling layer, a grouping layer, a PointNet layer, and a splitting layer of the PointNet + + network are set. Secondly, training a PointNet + + network, introducing time, space, temperature and phenological period context information when the point cloud data are obtained as characteristic data, and training a PointNet + + model for segmenting the three-dimensional point cloud data of the Chinese wolfberry.
The specific steps for setting the PointNet + + network are as follows:
(1) the method is characterized in that a sampling layer, a grouping layer, a PointNet layer and a segmentation layer which are sequentially included in a PointNet + + network are set, and the hierarchical combination relationship of the sampling layer, the grouping layer and the PointNet layer of the PointNet + + network is set firstly.
(2) Setting a sampling layer as a selection center;
obtaining central points by using a k-means + + clustering method, wherein the number of the central points in the first layer is m;
X＝{x1,x2,......,xnis the input set of labeled points,
wherein x isi∈Rd+CD is a point cloud dimension, and C is a characteristic dimension;
if x is taken randomlyk∈Rd+CThe first cluster center is then followed by centeringThe probability reaches a maximum value, where D (x)j) Is xjDistance from the nearest one of the cluster centers.
(3) Setting a grouping layer, wherein the grouping layer is used for creating a plurality of sub-point clouds by adjacent points in a given radius;
A1) obtaining a given radius;
A2) searching for fixed adjacent points by using a kNN algorithm and taking the clustering center as a reference, wherein the radiuses are respectively 0.1, 0.2 and 0.4, and the corresponding maximum points in the circle are 16, 32 and 128;
the input point set of the first layer is n x (d + C), the output point set is n' × kx (d + C), wherein k is the number of points contained in the neighborhood;
(4) setting a PointNet layer, wherein the PointNet layer is used for obtaining higher dimensional representation of the sub-point cloud;
setting a point set n ' × kx (d + C) as the input of a PointNet + + network, performing feature extraction on the point set, and finally outputting n ' × (d + C ');
repeating the step (2) (setting sampling layer), the step (3) (setting grouping layer) and the step (4) (setting PointNet layer) three times, namely combining the network layers into 3 l1、l2、l3And the module is used for continuously extracting the features.
(5) Setting a segmentation layer, and obtaining the score of each original point by adopting an interpolation and backtracking mode based on context information;
B1) input l3Point set of layers n '× (d + C');
B2) for l2Point of layer consisting of3After interpolation of the layer points, carrying out convolution of 1 x 1 with the corresponding characteristics obtained in 24) to finally obtain l2The values of the layer points are traced all the time to finally obtain l1A score of the origin;
B3) while interpolation is carried out, context information of input corresponding points is taken as characteristic data and introduced into the Unit PointNet + + network of the last layer;
Figure GDA0001894222710000081
for all the context information fused, the first layer input of the multi-layer perceptron is
H1＝σ(W1xi+UD+b1)，
The rest layers are Hm＝σ(WmHm-1+bm) Wherein M ∈ {2,..., M };
B4) acquisition Point (x)p,yp,zp)∈RCProbability P of belonging to target medlar class or background classc'(xp,yp,zp) It is expressed as follows:
wherein, c ═ target lycium barbarumThe background class },for the weights of the last layer belonging to class c, all parameters (W, U, b) are learned by minimizing the cross entropy loss function.
Secondly, training the PointNet + + network comprises the following steps:
(1) and inputting the three-dimensional point cloud into a sampling layer to obtain a central point of the three-dimensional point cloud.
(2) And inputting the central point of the three-dimensional point cloud into a grouping layer to obtain a plurality of sub-point clouds in the radius of the central point of the three-dimensional point cloud.
(3) And inputting a plurality of sub-point clouds into a PointNet + + network, and inputting corresponding characteristic data into a multilayer perceptron of the PointNet + + network for characteristic extraction.
(4) And (3) repeating the steps (1), (2) and (3) in the step of training the PointNet + + network three times, namely combining 3 modules by using the network layer.
(5) And obtaining the probability of the original point serving as the medlar or the background by adopting an interpolation and backtracking mode.
And thirdly, acquiring and preprocessing point cloud data to be identified.
Acquiring 18 pictures of the Chinese wolfberry tree to be identified and corresponding time, space, temperature and phenological period context information of the pictures, constructing a three-dimensional model and acquiring a three-dimensional point cloud to be identified.
And fourthly, identifying and counting the number of the Chinese wolfberry.
Inputting the three-dimensional point cloud to be identified and the contextual information as the characteristics into a PointNet + + model to obtain the score of the medlar target, and counting the number of the medlar by a marking method.
The number of the medlar can be obtained, and counting identification of the medlar is realized. However, in practical applications, although the analysis is performed on the three-dimensional point cloud data according to the characteristic that the distribution of the medlar is easy to overlap, a certain error still occurs. In order to further effectively distinguish the sticky and blocked wolfberries, the scores of the wolfberries target obtained in the fourth step of identifying and counting the number of the wolfberries are utilized, namely, the points in the 3D point cloud with the scores are precisely segmented by utilizing an improved watershed segmentation algorithm. The improved watershed segmentation algorithm is utilized, weak edges generated due to small pixel difference can be processed, the edges are accurately positioned, and even a single Chinese wolfberry in a cluster can be effectively distinguished.
And fifthly, performing accurate segmentation on the point cloud.
On the basis of distance conversion, the point cloud is segmented by using an optimal threshold watershed segmentation algorithm, and the number of the Chinese wolfberries is counted by using a marking method. The method specifically comprises the following steps:
(1) reconstructing the distance point cloud to distinguish the boundary points and the internal points of the medlar;
setting N (P) as an eight neighborhood point set, P (x)p,yp,zp) Representing the gray value of the point P, P' (x)p,yp,zp) Representing the probability, P ″ (x), that point P belongs to the target Lycium classp,yp,zp) A value representing the distance of the point p from its nearest background point;
reconstructing a point cloud of distances, an order
Wherein the content of the first and second substances,
| represents the euclidean distance between points p and q;
if distmin＜P”(xp,yp,zp) Let P "(x)p,yp,zp)＝distminThe distance point cloud constructed at this time has a value of P ″ (x)p,yp,zp) A collection of (a).
(2) And establishing a watershed segmentation algorithm based on an optimal threshold value. In the method, the selection of the extreme points in the watershed segmentation algorithm is updated, so that the result of PointNet + + network analysis can be utilized, the calculated amount is reduced, the efficiency is improved, and both large data processing and accurate segmentation can be realized. The method comprises the following specific steps:
C1) set of distances P "(x) to reconstructp,yp,zp) And as input, the distance value of the internal points of the Chinese wolfberry is larger, the distance value corresponding to the boundary point is smaller, and the distance value of the background point is zero. Finding out local maximum value points on all distances and endowing the local maximum value points with unique identification;
C2) adopting a descending method to descend on plain, descending all points until meeting the marked area, and adding the points into the marked area;
C3) combining the areas, and combining the areas with the depth lower than a threshold value;
C4) and determining a watershed segmentation result.
Here, the regions are subjected to the merging step individually as follows:
C31) for each region, finding out an inner curvature minimum point, a region adjacent to the region and a minimum point on the boundary of each adjacent region;
C32) calculating the watershed depth of each region;
C33) calculating an optimal threshold value;
let the point cloud have L reconstructed distance values, the point with distance value i has niN, the total number of points, and the probability of each distance value appearing isAnd is
The threshold value is set as t, the point cloud is divided into 2 parts, namely a background class a {0, 1, 2,. and t } and a medlar target class B { t +1, t +2,. and L-1} based on the distance value, and the probability of occurrence of the two classes is respectively:
Figure GDA0001894222710000113
the distance mean values of the two types A and B are respectively
Figure GDA0001894222710000114
The total distance mean in the point cloud is:
Figure GDA0001894222710000115
from this, the between-class variance of the two parts A and B is obtained
The larger the inter-class variance is, the larger the difference of the distance values between the two classes is, that is, the optimal threshold is determined as follows:
C34) when the depth is lower than the threshold value, combining the region and the region adjacent to the boundary curvature minimum value point and updating the related information;
C35) repeating the step C34) until the depths of all the areas are greater than the given threshold.
(3) And (4) a target mark counting method is adopted to obtain a segmentation result in the final point cloud, and the number is counted to obtain the number of the actual medlar in the point cloud.
The segmentation result in the final point cloud can be obtained by using the segmentation method, and in practical application, the segmentation result is each basin (each medlar target object), and the actual number of the medlar in the point cloud can be accurately obtained only by counting the number of the basins.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are merely illustrative of the principles of the invention, but that various changes and modifications may be made without departing from the spirit and scope of the invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (3)

1. A wolfberry identification and counting method based on a PointNet + + network is characterized by comprising the following steps:
11) collecting and preprocessing PointNet + + network training samples: acquiring 18 pictures and corresponding context information of each Chinese wolfberry tree, wherein the context information comprises time, space, temperature and phenological period information, constructing a three-dimensional model through the 18 pictures to obtain three-dimensional point cloud, and using the three-dimensional point cloud together with the context information as training data;
12) acquiring a PointNet + + model based on the PointNet + + network fusion context information:
setting a PointNet + + network, and setting a sampling layer, a grouping layer, a PointNet layer and a segmentation layer of the PointNet + + network; training a PointNet + + network, introducing time, space, temperature and phenological period context information when point cloud data are obtained as characteristic data, and training a PointNet + + model for segmenting the three-dimensional point cloud data of the Chinese wolfberry;
the method for setting the PointNet + + network comprises the following steps:
121) setting a PointNet + + network to sequentially comprise a sampling layer, a grouping layer and a hierarchy combination of PointNet layers;
122) setting a sampling layer as a selection center;
obtaining central points by using a k-means + + clustering method, wherein the number of the central points in the first layer is m;
X＝{x1,x2,......,xnis the input set of labeled points,
wherein x isi∈Rd+CD is a point cloud dimension, and C is a characteristic dimension;
if x is taken randomlyk∈Rd+CThe first cluster center is then followed by centeringThe probability reaches a maximum value, where D (x)j) Is xjDistance from the nearest one of the cluster centers;
123) setting a grouping layer, wherein the grouping layer is used for creating a plurality of sub-point clouds by adjacent points in a given radius;
1231) obtaining a given radius;
1232) searching for fixed adjacent points by using a kNN algorithm and taking the clustering center as a reference, wherein the radiuses are respectively 0.1, 0.2 and 0.4, and the corresponding maximum points in the circle are 16, 32 and 128;
the input point set of the first layer is n x (d + C), the output point set is n' × kx (d + C), wherein k is the number of points contained in the neighborhood;
124) setting a PointNet layer, wherein the PointNet layer is used for obtaining higher dimensional representation of the sub-point cloud;
setting a point set n ' × kx (d + C) as the input of a PointNet + + network, performing feature extraction on the point set, and finally outputting n ' × (d + C ');
repeating the steps 122), 123), 124) three times in this way, i.e. 3 l of such network layer combinations1、l2、l3A module for continuously extracting the features;
125) setting a segmentation layer, and obtaining the score of each original point by adopting an interpolation and backtracking mode based on context information;
1251) input l3Point set of layers n '× (d + C');
1252) for l2Point of layer consisting of3After interpolation of the layer points, carrying out convolution of 1 x 1 with the corresponding characteristics obtained in 124) to finally obtain l2The values of the layer points are traced all the time to finally obtain l1A score of the origin;
1253) while interpolation is carried out, context information of input corresponding points is taken as characteristic data and introduced into the Unit PointNet + + network of the last layer;
for all the context information fused, the first layer input of the multi-layer perceptron is
H1＝σ(W1xi+UD+b1)，
The rest layers are Hm＝σ(WmHm-1+bm) Wherein M ∈ {2,..., M };
1254) acquisition Point (x)p,yp,zp)∈RCProbability P 'of belonging to target matrimony vine or background'c(xp,yp,zp) It is expressed as follows:
wherein c is { target matrimony vine class, background class },for the weights of the last layer belonging to class c, all parameters (W, U, b) are learned by minimizing the cross entropy loss function;
the PointNet + + network training method comprises the following steps:
131) inputting the three-dimensional point cloud into a sampling layer to obtain a central point of the three-dimensional point cloud;
132) inputting the central point of the three-dimensional point cloud into a grouping layer to obtain a plurality of sub-point clouds in the radius of the central point of the three-dimensional point cloud;
133) inputting a plurality of sub-point clouds into a PointNet + + network, and inputting corresponding characteristic data into a multilayer perceptron of the PointNet + + network for characteristic extraction;
134) repeating step 131), step 132), step 133) three times in this order, i.e. 3 modules are assembled with such a network layer;
135) obtaining the probability of the original point as the medlar or the background by adopting an interpolation and backtracking mode;
13) acquiring and preprocessing point cloud data to be identified: acquiring 18 pictures of a Chinese wolfberry tree to be identified and corresponding time, space, temperature and phenological period context information of the pictures, constructing a three-dimensional model and acquiring a three-dimensional point cloud to be identified;
14) identification and counting of the number of the medlar: inputting the three-dimensional point cloud to be identified and the contextual information as the characteristics into a PointNet + + model to obtain the score of the medlar target, and counting the number of the medlar by a marking method.
2. The method according to claim 1, wherein the identification and counting of the number of the lycium barbarum comprises the following steps of performing accurate segmentation on the point cloud: on the basis of distance conversion, a point cloud is segmented by using an optimal threshold watershed segmentation algorithm, and the number of the Chinese wolfberries is counted by using a marking method;
the accurate segmentation of the point cloud comprises the following steps:
21) reconstructing the distance point cloud to distinguish the boundary points and the internal points of the medlar;
setting N (P) as an eight neighborhood point set, P (x)p,yp,zp) Representing the gray value of the point P, P' (x)p,yp,zp) Representing the probability, P ″ (x), that point P belongs to the target Lycium classp,yp,zp) A value representing the distance of the point p from its nearest background point;
reconstructing a point cloud of distances, an order
Wherein the content of the first and second substances,
| represents the euclidean distance between points p and q;
if distmin<P”(xp,yp,zp) Let P "(x)p,yp,zp)＝distminThe distance point cloud constructed at this time has a value of P ″ (x)p,yp,zp) A set of (a);
22) establishing a watershed segmentation algorithm based on an optimal threshold;
221) set of distances P "(x) to reconstructp,yp,zp) The distance value of the internal points of the Chinese wolfberry is large, the distance value corresponding to the boundary point is small, and the distance value of the background point is zero; finding out all distancesThe local maximum value point on the distance is given with a unique identifier;
222) adopting a descending method to descend on plain, descending all points until meeting the marked area, and adding the points into the marked area;
223) combining the areas, and combining the areas with the depth lower than a threshold value;
224) determining a watershed segmentation result;
23) and (4) a target mark counting method is adopted to obtain a segmentation result in the final point cloud, and the number is counted to obtain the number of the actual medlar in the point cloud.
3. The method according to claim 2, wherein the region merging comprises the following steps:
31) for each region, finding out an inner curvature minimum point, a region adjacent to the region and a minimum point on the boundary of each adjacent region;
32) calculating the watershed depth of each region;
33) calculating an optimal threshold value;
let the point cloud have L reconstructed distance values, the point with distance value i has niN, the total number of points, and the probability of each distance value appearing isAnd is
The threshold value is set as t, the point cloud is divided into 2 parts, namely a background class a {0, 1, 2,. and t } and a medlar target class B { t +1, t +2,. and L-1} based on the distance value, and the probability of occurrence of the two classes is respectively:
the distance mean values of the two types A and B are respectivelyThe total distance mean in the point cloud is:
from this, the between-class variance of the two parts A and B is obtained
The larger the inter-class variance is, the larger the difference of the distance values between the two classes is, that is, the optimal threshold is determined as follows:
34) when the depth is lower than the threshold value, combining the region and the region adjacent to the boundary curvature minimum value point and updating the related information;
35) repeating 34) until the depth of all the areas is greater than a given threshold.
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