CN111366072B - Data acquisition method for image deep learning - Google Patents
Data acquisition method for image deep learning Download PDFInfo
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- CN111366072B CN111366072B CN202010086353.3A CN202010086353A CN111366072B CN 111366072 B CN111366072 B CN 111366072B CN 202010086353 A CN202010086353 A CN 202010086353A CN 111366072 B CN111366072 B CN 111366072B
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- G01B11/00—Measuring arrangements characterised by the use of optical techniques
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
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Abstract
The invention relates to a data acquisition method for deep learning of images, which comprises the following steps: placing a product on the surface of a backlight light source, then sending a command for controlling multi-axis motion through a computer, receiving the command by a horizontal motion axis and a horizontal motion axis, driving the product to do random displacement motion in a plane, and receiving the command by a lifting motion axis, an inclined motion axis and a pitching motion axis, and driving an industrial camera to do lifting, inclining and pitching motion in a space; when the industrial camera moves to a certain position in space, the computer sends out an instruction for controlling the illumination of the light source, controls the on-off of the flat light sources at different positions annularly arranged in the inner cavity of the system, the annular light source carried by the camera and the backlight light source arranged under the product, tracks the position of the product by the industrial camera within a period of time to carry out space shooting under different conditions, simulates various actual shooting conditions, and obtains a data set.
Description
Technical Field
The invention relates to a data acquisition system for deep learning of images.
Background
In the production and application process of products, the detection, identification and classification of the surfaces of the products are a crucial link. With the development of computer technology, the detection and identification of product surfaces are developed from relying on manual work to realizing automatic detection through digital image processing technology. However, the traditional digital image processing technology has high requirements on the image acquisition environment, and when the image acquisition environment is slightly changed, the problem of reduction of the object identification accuracy rate can be brought. In recent years, with the rise of artificial intelligence technology, image recognition and detection become a more reliable mode through a deep learning method, and a convolutional neural network, as one of the representative algorithms of deep learning, is prominent in two-dimensional image processing, and particularly has good robustness and higher operation efficiency in recognizing images with displacement, scaling and other forms of distortion invariance. However, training of deep learning requires a large enough data set to support, and a traditional method for expanding a data set mainly uses an image processing means, that is, the data set is further enriched on the basis of original data, and the method has certain limitations, which is a difficult problem that the image deep learning is restricted to be applied to surface detection and identification classification of products in various industries. Currently, the following problems mainly exist in the aspect of product surface data set acquisition:
(1) considering the data set acquisition mode, the traditional data set acquisition mode is single and ideal, and the situation of image acquisition in the practical application process cannot be fully explained.
(2) In consideration of the data set, the data acquired at the present stage is small in quantity and insufficient in richness;
(3) in consideration of the data collection process, the data collection period is long, a large amount of manpower and material resources are needed, and the cost is high.
Disclosure of Invention
In view of the above problems, an object of the present invention is to provide a data acquisition method for deep image learning, which can comprehensively simulate shooting conditions in different actual scenes, so as to solve the problems of simplification, idealization, small data volume, insufficient richness, etc. of the traditional data set creation method. In order to achieve the purpose, the invention adopts the following technical scheme:
a data acquisition method for deep learning of images adopts a data acquisition system which comprises an industrial camera arranged in an inner cavity of the system, a displacement mechanism, a multi-directional illumination light source and a computer, and is characterized in that,
the industrial camera is used for acquiring surface images of products under different poses and different illumination conditions to acquire a data set;
the displacement mechanism comprises a horizontal motion shaft system, a lifting motion shaft, an inclined motion shaft and a pitching motion shaft, wherein the horizontal motion shaft system consists of a horizontal motion shaft X and a horizontal motion shaft Y and is used for driving a product to be detected to move in a plane; the lifting motion shaft, the tilting motion shaft and the pitching motion shaft are connected with the industrial camera and used for carrying the industrial camera to carry out lifting, tilting or pitching motion so as to realize shooting of different spatial angles of the surface of a product;
the multi-azimuth illumination light source comprises flat light sources at different angles annularly arranged in an inner cavity of the system, an annular light source carried on a lens of the industrial camera and a backlight light source arranged below a product to be detected;
and the computer is used for controlling the action of the displacement mechanism and the multi-directional illumination light source.
The data acquisition comprises the following steps:
(1) placing a product 4 on the surface of a backlight light source 3, then sending a command for controlling multi-axis motion through a computer 1, receiving the command by a horizontal motion axis X5 and a horizontal motion axis Y6, driving the product 4 to do random displacement motion in a plane, and receiving the command by a lifting motion axis 7, an inclined motion axis 8 and a pitching motion axis 9, driving an industrial camera 10 to do lifting, inclining and pitching motion in a space;
(2) when the industrial camera 10 moves to a certain position in space, the computer 1 sends out a command for controlling the illumination of the light source, controls the on-off of the flat light source 2 at different positions annularly arranged in the inner cavity of the system, the annular light source 11 carried by the camera and the backlight light source 3 arranged under the product 4, then collects the image of the product 4 in the state by adjusting the focusing position of the industrial camera 10, tracks the position of the product for space shooting under different conditions within a period of time, simulates various actual shooting conditions, and obtains a large amount of abundant data sets.
The method for tracking the position of the product by the industrial camera comprises the following steps:
the industrial camera 10 is moved from an initial position N (0,0, z)0) The point moves to Q (0,0, z)1) The product 4 moves from the initial position O (0,0,0) to P (x)1,y10) point, in order to ensure that the product 4 is within the field of view of the industrial camera 10, the industrial camera 10 needs to rotate around the Y axis in the direction a, and also needs to rotate around the X axis in the direction B, so that the rotation angle in the direction A, B needs to be calculated, the industrial camera 10 is first rotated around the Y axis by a certain angle β and a rotation momentMatrix of
Then the industrial camera 10 rotates a certain angle alpha around the X axis and tracks the product 4, and the matrix is rotated
According to a space vector two-point distance formula and a rigid body rotation transformation principle, the calculation formula of alpha and beta is obtained as follows:
due to the adoption of the technical scheme, the invention has the following advantages:
(1) the invention can realize the shooting of different positions and different visual angles of the surface of the product through the displacement mechanism;
(2) according to the invention, the situation of uneven illumination during actual shooting can be simulated by the aid of the multi-directional illumination light sources;
(3) the displacement mechanism and the multi-directional illumination light source are matched with each other, so that the random lighting of the spatial multi-directional light source and the random shooting of different positions of the spatial fabric can be realized, and a product surface information data set can be comprehensively obtained.
(4) The invention realizes the automation of the image acquisition process, has high acquisition speed and can construct abundant and massive data sets in a short time.
Drawings
FIG. 1 is a system diagram of a data acquisition system for image depth learning of the present invention.
Fig. 2 is a photographing flow chart of the data acquisition system for image depth learning of the present invention.
Fig. 3 is a schematic diagram of the industrial camera tracking the position of the product according to the present invention.
The reference numbers in the figures illustrate: 1, a computer; 2, a flat light source; 3 backlight light source; 4, preparing a product; 5 horizontal axis of motion X; 6 horizontal axis of motion Y; 7 lifting movement shaft; 8, tilting the motion axis; 9 pitching motion axis; 10 an industrial camera; 11 annular light source
Detailed Description
The invention is described below with reference to the figures and examples.
As shown in fig. 1, the data acquisition system for image depth learning provided by the present invention mainly comprises an industrial camera 10, a displacement mechanism, a multi-directional illumination light source, and a computer. The industrial camera 10 is used for acquiring surface images of products under different poses and different illumination conditions to acquire a data set; the displacement mechanism has five freedom degrees of movement such as translation, lifting, inclination and pitching, wherein the horizontal movement axis ties the product to move randomly in a plane, and the three movement axes of lifting, inclination and pitching carry the industrial camera to perform lifting, inclination and pitching movement, so that different spatial angles of the surface of the product can be shot; the multi-azimuth illumination light source mainly comprises a flat light source 2 arranged in the system cavity in an annular mode and arranged at different angles, an annular light source 11 carried by a camera lens and a backlight light source 3 arranged below a product.
The product image shooting process is shown in fig. 2, and the specific acquisition process is as follows: the product 4 is firstly placed on the surface of the backlight light source 3, then a command for controlling multi-axis motion is sent out through the computer 1, the horizontal motion axis X5 and the horizontal motion axis Y6 drive the product 4 to do random displacement motion in a plane after receiving the command, and meanwhile, the lifting motion axis 7, the tilting motion axis 8 and the pitching motion axis 9 drive the industrial camera 10 to do lifting, tilting and pitching motion in a space after receiving the command. When the industrial camera 10 moves to a certain position in space, the computer 1 sends a command for controlling the illumination of the light source, at the moment, the flat light sources 2 at different directions annularly arranged in the system cavity, the annular light source 11 carried by the camera and the backlight light source 3 arranged below the product 4 are randomly switched on and off, and then the focusing position of the industrial camera 10 is adjusted through an automatic focusing algorithm to acquire an image in the state. Shooting in random space within a period of time, comprehensively simulating various actual shooting conditions, and finally obtaining a large amount of abundant data sets.
Fig. 3 is a schematic diagram of the industrial camera tracking the position of the product according to the present invention. The industrial camera 10 is moved from an initial position N (0,0, z)0) The point moves to Q (0,0, z)1) The product 4 moves from the initial position O (0,0,0) to P (x)1,y10) point, in order to ensure that the product 4 is within the field of view of the industrial camera 10, the industrial camera 10 needs to make rotational motions in the directions of a (rotating around the Y axis) and B (rotating around the X axis), and therefore the rotation angle in the direction of A, B needs to be calculated, assuming that the industrial camera 10 rotates around the Y axis by a certain angle β first and the rotation matrix rotates
Then the industrial camera 10 rotates a certain angle alpha around the X axis to just track the product 4, and the rotation matrix
According to a space vector two-point distance formula and a rigid body rotation transformation principle, a specific calculation formula is as follows:
QO″＝RX×Ry×QO
the calculation results of α and β can be obtained by the above formula as follows:
Claims (2)
1. a data acquisition method for deep learning of images adopts a data acquisition system which comprises an industrial camera arranged in an inner cavity of the system, a displacement mechanism, a multi-directional illumination light source and a computer, and is characterized in that,
the industrial camera is used for acquiring surface images of products under different poses and different illumination conditions to acquire a data set;
the displacement mechanism comprises a horizontal motion shaft system, a lifting motion shaft, an inclined motion shaft and a pitching motion shaft, wherein the horizontal motion shaft system consists of a horizontal motion shaft X and a horizontal motion shaft Y and is used for driving a product to be detected to move in a plane; the lifting motion shaft, the tilting motion shaft and the pitching motion shaft are connected with the industrial camera and used for carrying the industrial camera to carry out lifting, tilting or pitching motion so as to realize shooting of different spatial angles of the surface of a product;
the multi-azimuth illumination light source comprises flat light sources at different angles annularly arranged in an inner cavity of the system, an annular light source carried on a lens of the industrial camera and a backlight light source arranged below a product to be detected;
the computer is used for controlling the action of the displacement mechanism and the multi-directional lighting light source;
the data acquisition comprises the following steps:
(1) placing a product on the surface of a backlight light source, then sending a command for controlling multi-axis motion through a computer, receiving the command by two horizontal motion axes, driving the product to do random displacement motion in a plane, and receiving the command by a lifting motion axis, an inclined motion axis and a pitching motion axis, and driving an industrial camera to do lifting, inclining and pitching motion in a space;
(2) when the industrial camera moves to a certain position in space, the computer sends out an instruction for controlling the illumination of the light source, controls the on-off of the flat light sources at different positions annularly arranged in the inner cavity of the system, the annular light source carried by the camera and the backlight light source arranged under the product, then collects the image of the product in the state by adjusting the focusing position of the industrial camera, tracks the position of the product by the industrial camera within a period of time to carry out space shooting under different conditions, simulates various actual shooting conditions, and obtains a large amount of abundant data sets.
2. The method of claim 1, wherein the industrial camera tracks the position of the product as follows: industrial camera from initial position N (0,0, z)0) The point moves to Q (0,0, z)1) The product is discharged from the initial position O (0,0) point movement to P (x)1,y10) point, in order to ensure that the product is within the field of view of the industrial camera, the industrial camera needs to rotate around the Y axis in the direction a, and also needs to rotate around the X axis in the direction B, so that the rotation angle in the direction A, B needs to be calculated, the industrial camera is firstly rotated around the Y axis by a certain angle β, and a rotation matrix is set
Then the industrial camera rotates a certain angle alpha around the X axis and tracks the product, and the matrix is rotated
According to a space vector two-point distance formula and a rigid body rotation transformation principle, the calculation formula of alpha and beta is obtained as follows:
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