CN112800929B - Bamboo shoot quantity and high growth rate online monitoring method based on deep learning - Google Patents
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Abstract
The invention discloses a deep learning-based online monitoring method for the quantity and high growth rate of bamboo shoots, which comprises the following steps: 1, regularly acquiring a bamboo forest image containing bamboo shoots through a fixed network camera; 2, acquiring target area detection frames and vertex coordinates of the detection frames by adopting the trained bamboo shoot depth learning model, wherein the number of the detection frames represents the number of the bamboo shoots; 3, sequencing all the detection frames, and storing the sequenced corresponding coordinates into a basic database or a temporary database; 4, screening the bamboo shoots; and 5, calculating the high growth rate of the bamboo shoots. The method provided by the invention can monitor the quantity and high growth rate of the bamboo shoots in the bamboo forest in real time, thereby judging the growth conditions of the bamboo shoots. The method has strong practicability and accurate result, and is suitable for various aspects of bamboo forest management, especially cultivation of bamboo shoot forests and under-forest economy.
Description
Technical Field
The invention relates to the field of intelligent agriculture and forestry, in particular to an online monitoring method for the quantity and the high growth rate of bamboo shoots based on deep learning, which is suitable for various aspects of bamboo forest management, especially cultivation of bamboo shoots and forest economy.
Background
Bamboo has wide distribution in the global range, and is known as the second forest in the world. China is the world with the most bamboo resources. Bamboo not only plays an important role in absorbing carbon dioxide, preventing water and soil loss, maintaining biological diversity and the like, but also provides various processing materials for human beings, and bamboo shoots are green healthy food. The quantity and high growth state of bamboo shoots are closely related to the quality and yield of bamboo shoots, and therefore, in bamboo forest management, bamboo shoot forest cultivation and under-forest economy, the quantity and high growth state of bamboo shoots need to be known in time.
Due to the large density and complex environment of bamboo forests, the quantity and the high growth rate of the bamboo shoots are difficult to obtain efficiently and accurately. The traditional bamboo shoot quantity mainly depends on manual counting, and the high growth rate is calculated after manual contact type height measurement is carried out mainly through tools such as a tape measure and a marker post. The computer vision and image measurement are a feasible solution, the bamboo forest image is obtained regularly through a monitoring camera, the bamboo forest image is transmitted to a cloud server in a wireless mode, an image processing and analyzing algorithm is designed, and the number and the high growth rate of the bamboo shoots are obtained. The core of the common image processing and analyzing algorithm is image segmentation, and the quantity and the height can be further calculated only by segmenting bamboo shoots. Due to the fact that influences of various adverse factors such as background and illumination are large, characteristics such as colors, textures and shapes need to be comprehensively applied to a segmentation algorithm, complexity is high, and the effect is poor. A series of image processing operations are required subsequently, and errors are further introduced.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, and provides an online monitoring method for the number and the high growth rate of the bamboo shoots based on deep learning, so that the online automatic detection of the number and the high growth rate of the bamboo shoots can be realized, the growth conditions of the bamboo shoots can be judged, and corresponding measures can be taken.
The invention adopts the following technical scheme for solving the technical problems:
the invention relates to a method for monitoring the quantity and the high growth rate of bamboo shoots on line based on deep learning, which is characterized by comprising the following steps:
step 1: defining the number of currently acquired image frames as n, and initializing n to 1;
step 2: acquiring the nth bamboo forest image containing bamboo shoots;
and step 3: using trainedCarrying out bamboo shoot detection on the obtained nth frame of bamboo forest image by a good bamboo shoot deep learning model to obtain m in the nth frame of bamboo forest imagenA bamboo shoot area detection frame and coordinate values of four vertexes of each bamboo shoot area detection frame, wherein the number m of the bamboo shoot area detection framesnRepresenting the number of the bamboo shoots in the detected n-th frame bamboo forest image;
and 4, step 4: for m in the n-th frame bamboo forest imagenSequencing the bamboo shoot area detection frames according to a sequencing principle;
and 5: judging whether n is 1, if so, storing the coordinates corresponding to the sorted bamboo shoot area detection frames into a basic database; otherwise, storing the coordinates corresponding to the sorted bamboo shoot area detection frames into a temporary database;
step 6: judgment mn＝mn-1If yes, directly executing the step 7; otherwise, m is judgedn-mn-1If the number is more than 0, increasing m in the n-th frame bamboo forest imagen-mn-1Storing the bamboo shoot area detection frames into a basic database according to a sorting principle, and returning to the step 2 to continue detection if the bamboo shoot area detection frames are not sorted; when n is 1, let mn＝mn-1；
And 7: screening bamboo shoots;
screening coordinate information of the bamboo shoot area detection frames in the temporary database and the basic database by using a formula (1), if the formula (1) is met, indicating that the corresponding bamboo shoot detection frame is valid, and otherwise, indicating that the corresponding bamboo shoot detection frame is invalid;
in the formula (1), xeAnd yeRespectively representing the maximum abscissa error and the maximum ordinate error; x is the number ofi1 n-1The x-axis coordinate and x of the top point of the lower left corner of the sorted ith bamboo shoot area detection frame in the n-1 th frame of bamboo forest imagei1 nShowing the lower left of the detection frame of the i-th bamboo shoot area after sorting in the n-th bamboo forest imageX-axis coordinate of angular point, yi1 n-1The y-axis coordinate and the y-axis coordinate of the top of the lower left corner of the sorted ith bamboo shoot area detection frame in the n-1 th frame of bamboo forest imagei1 nRepresenting the y-axis coordinate of the top point of the lower left corner of the sorted ith bamboo shoot area detection frame in the nth frame of bamboo forest image; i ═ 1, mn]；
And 8: the height p of the ith bamboo shoot in the (n-1) th frame bamboo forest image is obtained by the formula (2)i n-1And a height p in the n-th frame of the bamboo forest imagei n；
In the formula (2), yi2 n-1The y-axis coordinate and the y-axis coordinate of the top of the lower right corner of the sorted ith bamboo shoot area detection frame in the n-1 th frame of bamboo forest imagei2 nThe y-axis coordinate of the top point of the lower right corner of the sorted ith bamboo shoot area detection frame in the nth frame of bamboo forest image is represented, and yi3 n-1The y-axis coordinate and the y-axis coordinate of the top right corner vertex of the i-th bamboo shoot area detection frame sequenced in the n-1 th frame of bamboo forest imagei3 nRepresenting the y-axis coordinate of the top right corner vertex of the sorted ith bamboo shoot area detection frame in the nth frame of bamboo forest image;
and step 9: calculating the high growth rate of the ith bamboo shoot in the nth bamboo forest image by using the formula (3)
In the formula (3), αi nRepresents the high growth rate, p, of the ith bamboo shoot in the n-th bamboo forest imagei n-1P represents the height of the ith bamboo shoot in the (n-1) th frame bamboo forest imagei nThe height of the ith bamboo shoot in the n-th frame bamboo forest image is shown.
The method for monitoring the quantity and the high growth rate of the bamboo shoots on line based on the deep learning is also characterized in that the ordering principle in the step 4 is as follows:
and performing ascending sorting on each bamboo shoot area detection frame by using the x-axis coordinate of the vertex at the lower left corner, and performing ascending sorting by using the y-axis coordinate of the vertex at the lower left corner if the x-axis coordinates are the same, so as to obtain the sorted bamboo shoot area detection frames.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the method, the trained bamboo shoot deep learning model is combined with a unique target detection frame vertex coordinate sorting, screening and calculating method, so that the quantity and the high growth rate of the bamboo shoots can be simultaneously and accurately acquired, and the on-line automatic monitoring requirement is met.
2. The method is used for detecting the bamboo shoots based on the deep learning principle, and basically, new samples are continuously obtained subsequently along with the increase of application time, the detection precision has continuously improved space, and a target detection model can obtain a better training result.
Drawings
FIG. 1 is a general flow diagram of the process of the present invention;
FIG. 2 is a block diagram showing the actual detection of bamboo shoots by the method of the present invention;
FIG. 3 is a schematic view of a bamboo shoot detection box according to the method of the present invention;
FIG. 4 is a schematic diagram of a detection box of the bamboo shoot after growing up.
Detailed Description
In this embodiment, as shown in fig. 1, an online monitoring method for bamboo shoot number and high growth rate based on deep learning is performed according to the following steps:
step 1: acquiring bamboo shoot images in a real forest environment through a network camera, uploading the bamboo shoot images to an FTP (file transfer protocol) server in Aliyun, and downloading enough bamboo shoot images as an initial data set by a computer;
step 2: the initial data set is rotated by 90 degrees, 180 degrees and 270 degrees, the brightness is increased by 1.5 times, and is reduced by 0.1 time, and the like, so that a new data set enhanced by 6 times is obtained;
and step 3: labeling all bamboo shoots in the new data set by using image labeling software labellimg, and performing image labeling on the new data set according to the following steps of 8: 1: 1, dividing the ratio into a training set, a test set and a verification set;
and 4, step 4: selecting a target detection model training data set based on YOLOv4, testing the training result by using a test set and a verification set, and dynamically adjusting parameters such as learning rate, confidence coefficient and the like to obtain an optimal bamboo shoot deep learning model;
and 5: defining the number of image frames currently acquired from a server as n, and initializing n to 1;
step 6: acquiring the nth bamboo forest image containing bamboo shoots;
and 7: adopting a trained bamboo shoot deep learning model to carry out bamboo shoot detection on the obtained nth frame of bamboo forest image to obtain m in the nth frame of bamboo forest imagenCoordinate values of four vertexes of each of the bamboo shoot region detection frames. Wherein the number m of the bamboo shoot area detection framesnThe number of bamboo shoots in the detected n-th frame bamboo forest image is shown. As shown in the actual detection block diagram of bamboo shoots in FIG. 2, there are 19 detection blocks in the area of bamboo shoots, which means that 19 bamboo shoots are detected;
and 8: for m in the n-th frame bamboo forest imagenSequencing the bamboo shoot area detection frames according to a sequencing principle;
sorting principle: and (4) sorting each bamboo shoot area detection frame in an ascending order according to the x-axis coordinate of the vertex at the lower left corner of the bamboo shoot area detection frame, and if the x-axis coordinates are the same, sorting each bamboo shoot area detection frame in an ascending order according to the y-axis coordinate of the vertex at the lower left corner of the bamboo shoot area detection frame, so that the sorted bamboo shoot area detection frames are obtained. FIG. 3 is a schematic diagram of the bamboo shoot detection frames of the n-1 th frame bamboo forest image, wherein the sequence obtained by the four bamboo shoot region detection frames according to the sorting rule is A, B, C, D. Wherein the black triangular region represents bamboo shoot, (x)i1 n-1,yi1 n-1)、(xi2 n-1,yi2 n-1)、(xi3 n-1,yi3 n-1) Respectively representing the vertex coordinates of the lower left corner, the lower right corner and the upper right corner of the ith bamboo shoot area detection frame after sequencing in the image;
and step 9: judging whether n is 1, if so, storing the coordinates corresponding to the sorted bamboo shoot area detection frames into a basic database; otherwise, storing the coordinates corresponding to the sorted bamboo shoot area detection frames into a temporary database;
step 10: judgment mn＝mn-1If yes, directly executing step 11; otherwise, m is judgedn-mn-1If the number is more than 0, increasing m in the n-th frame bamboo forest imagen-mn-1Storing the bamboo shoot area detection frames into a basic database according to a sorting principle, and returning to the step 6 to continue detection if the bamboo shoot area detection frames are not stored in the basic database; when n is 1, let mn＝mn-1；
Step 11: screening bamboo shoots;
FIG. 4 is a schematic diagram of a bamboo shoot detection box in the n-th frame of the bamboo forest image. Wherein, the non-overlapping parts of the two triangles represent the parts corresponding to the bamboo shoot length in the n-1 th frame of bamboo forest image, (x)i1 n,yi1 n)、(xi2 n,yi2 n)、(xi3 n,yi3 n) Respectively representing the vertex coordinates of the lower left corner, the lower right corner and the upper right corner of the ith bamboo shoot area detection frame after sorting in the image. Generally, the coordinates of the vertex of the lower left corner of a strain of bamboo shoots do not have too large error, and the error of the coordinates of the vertex of the lower left corner before and after the growth of the bamboo shoots is considered as xeAnd yeThe case (1); screening coordinate information of the bamboo shoot area detection frames in the temporary database and the basic database by using a formula (1), if the formula (1) is met, indicating that the corresponding bamboo shoot detection frame is valid, and otherwise, indicating that the corresponding bamboo shoot detection frame is invalid;
in the formula (1), xeAnd yeRespectively representing the maximum abscissa error and the maximum ordinate error; x is the number ofi1 n-1The lower left corner of the detection frame of the ith bamboo shoot area sequenced in the n-1 th bamboo forest image is representedX-axis coordinate of vertex, xi1 nThe x-axis coordinate and y of the top point of the lower left corner of the sorted ith bamboo shoot area detection frame in the nth frame of bamboo forest image are representedi1 n-1The y-axis coordinate and the y-axis coordinate of the top of the lower left corner of the sorted ith bamboo shoot area detection frame in the n-1 th frame of bamboo forest imagei1 nRepresenting the y-axis coordinate of the top point of the lower left corner of the sorted ith bamboo shoot area detection frame in the nth frame of bamboo forest image; i ═ 1, mn]；
Step 12: the height p of the ith bamboo shoot in the (n-1) th frame bamboo forest image is obtained by the formula (2)i n-1And a height p in the n-th frame of the bamboo forest imagei n；
In the formula (2), yi2 n-1The y-axis coordinate and the y-axis coordinate of the top of the lower right corner of the sorted ith bamboo shoot area detection frame in the n-1 th frame of bamboo forest imagei2 nThe y-axis coordinate of the top point of the lower right corner of the sorted ith bamboo shoot area detection frame in the nth frame of bamboo forest image is represented, and yi3 n-1The y-axis coordinate and the y-axis coordinate of the top right corner vertex of the i-th bamboo shoot area detection frame sequenced in the n-1 th frame of bamboo forest imagei3 nRepresenting the y-axis coordinate of the top right corner vertex of the sorted ith bamboo shoot area detection frame in the nth frame of bamboo forest image;
step 13: calculating the high growth rate alpha of the ith bamboo shoot in the nth frame bamboo forest image by using the formula (3)i n：
In the formula (3), αi nRepresents the high growth rate, p, of the ith bamboo shoot in the n-th bamboo forest imagei n-1P represents the height of the ith bamboo shoot in the (n-1) th frame bamboo forest imagei nThe height of the ith bamboo shoot in the n-th frame bamboo forest image is shown.
Claims (2)
1. A bamboo shoot quantity and high growth rate on-line monitoring method based on deep learning is characterized by comprising the following steps:
step 1: defining the number of currently acquired image frames as n, and initializing n to 1;
step 2: acquiring the nth bamboo forest image containing bamboo shoots;
and step 3: adopting a trained bamboo shoot deep learning model to carry out bamboo shoot detection on the obtained nth frame of bamboo forest image to obtain m in the nth frame of bamboo forest imagenA bamboo shoot region detection frame and coordinate values of four vertexes of each bamboo shoot region detection frame, wherein the number m of the bamboo shoot region detection framesnRepresenting the number of the bamboo shoots in the detected n-th frame bamboo forest image;
and 4, step 4: for m in the n-th frame bamboo forest imagenSequencing the bamboo shoot area detection frames according to a sequencing principle;
and 5: judging whether n is equal to 1, if yes, storing the coordinates corresponding to the sorted bamboo shoot area detection frames into a basic database; otherwise, storing the coordinates corresponding to the sorted bamboo shoot area detection frames into a temporary database;
step 6: judgment mn＝mn-1If yes, directly executing the step 7; otherwise, m is judgedn-mn-1If the number is more than 0, increasing m in the n-th frame bamboo forest imagen-mn-1Storing the bamboo shoot area detection frames into a basic database according to a sorting principle, and returning to the step 2 to continue detection if the bamboo shoot area detection frames are not sorted; when n is 1, let mn＝mn-1；
And 7: screening bamboo shoots;
screening coordinate information of the bamboo shoot area detection frames in the temporary database and the basic database by using a formula (1), if the formula (1) is met, indicating that the corresponding bamboo shoot detection frame is valid, and otherwise, indicating that the corresponding bamboo shoot detection frame is invalid;
in the formula (1), xeAnd yeRespectively representing the maximum abscissa error and the maximum ordinate error; x is the number ofi1 n-1X-axis coordinate, x, of the top of the lower left corner of the sorted ith bamboo shoot region detection box in the n-1 th bamboo forest imagei1 nThe x-axis coordinate and y of the top point of the lower left corner of the sorted ith bamboo shoot area detection frame in the nth frame of bamboo forest image are representedi1 n-1The y-axis coordinate and the y-axis coordinate of the top of the lower left corner of the sorted ith bamboo shoot area detection frame in the n-1 th frame of bamboo forest imagei1 nRepresenting the y-axis coordinate of the top point of the lower left corner of the sorted ith bamboo shoot area detection frame in the nth frame of bamboo forest image; i ═ 1, mn]；
And 8: the height p of the ith bamboo shoot in the n-1 frame bamboo forest image is obtained by the formula (2)i n-1And a height p in the n-th frame of the bamboo forest imagei n；
In the formula (2), yi2 n-1The y-axis coordinate and the y-axis coordinate of the top of the lower right corner of the sorted ith bamboo shoot area detection frame in the n-1 th frame of bamboo forest imagei2 nThe y-axis coordinate of the top point of the lower right corner of the sorted ith bamboo shoot area detection frame in the nth frame of bamboo forest image is represented, and yi3 n-1Representing the y-axis coordinate of the top right corner vertex of the i-th bamboo shoot area detection frame after being sequenced in the n-1 th frame of bamboo forest image, yi3 nRepresenting the y-axis coordinate of the top right corner vertex of the sorted ith bamboo shoot area detection frame in the nth frame of bamboo forest image;
and step 9: calculating the high growth rate of the ith bamboo shoot in the nth frame bamboo forest image by using the formula (3)
In the formula (3), αi nShows the high growth rate, p, of the ith bamboo shoot in the n-th bamboo forest imagei n-1P represents the height of the ith bamboo shoot in the (n-1) th frame bamboo forest imagei nThe height of the ith bamboo shoot in the n-th frame bamboo forest image is shown.
2. The on-line monitoring method for the quantity and the high growth rate of the bamboo shoots based on the deep learning as claimed in claim 1, wherein the sequencing principle in the step 4 is as follows:
and performing ascending sorting on each bamboo shoot area detection frame by using the x-axis coordinate of the vertex at the lower left corner, and performing ascending sorting by using the y-axis coordinate of the vertex at the lower left corner if the x-axis coordinates are the same, so as to obtain the sorted bamboo shoot area detection frames.
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