WO2020024079A1

WO2020024079A1 - Image recognition system

Info

Publication number: WO2020024079A1
Application number: PCT/CN2018/097687
Authority: WO
Inventors: 王星泽
Original assignee: 合刃科技（深圳）有限公司
Priority date: 2018-07-28
Filing date: 2018-07-28
Publication date: 2020-02-06
Also published as: CN109496316B; CN109496316A

Abstract

An image recognition system, comprising a multi-view imaging module having a micro-lens array. The light of an imaged object is refracted by a plurality of micro-lenses respectively and is then incident on different photosensitive regions of the photosensitive element. Imaging may be carried out once to obtain images and depth information of a plurality of different weak viewing angles of the object; image information of a recognized object at a plurality of different angles obtained by single viewing angle imaging of the recognized object by the multi-view imaging module is acquired by means of repetition; a set of image information of the recognized object at the plurality of different angles acquired each time is used as sample training data, and training is carried out on the basis of a convolutional neural network model to obtain a target model; and thus, an object to be recognized is subject to image recognition. images of the same object at a plurality of different weak viewing angles are used for recognition, which greatly improves the accuracy of object recognition.

Description

Image recognition system

Technical field

The present application relates to the technical field of optical image processing, and in particular, to an image recognition system.

Background technique

Existing scientific research shows that 80% to 85% of human learning and cognitive activities are completed through vision. In the field of artificial intelligence, computer vision is also a very important research direction. Larry, who studied at the Massachusetts Institute of Technology in 1963: In his doctoral dissertation "Machine Perception of Three-Dimensional Solids", Larry Roberts proposed that edges are the most critical information used to describe the shape of objects. Since then, computer vision has entered a rapid development path. At present, the main application scenarios are: identity authentication, security, unmanned driving, and industrial inspection.

All of the above applications require computer vision to have a highly accurate object recognition capability. The object recognition process can be divided into: image acquisition, feature extraction, classifier classification, and classification results. At present, image acquisition mainly uses ordinary imaging systems to project the three-dimensional image information into two-dimensional color pictures, so the third-dimensional information of the actual object is lost, that is, there is no depth and longitudinal information, and the computer finally obtains only the features on the planar image. the difference. There are also a few methods to reconstruct the 3D image of the object and then recognize it, such as:

The probe method, which uses a probe to directly locate the surface of an object. This method is inefficient and will damage the object itself;

The binocular vision method, which uses the triangle principle to calculate the distance between objects, requires two cameras, is expensive, and is not suitable for objects with smooth and non-textured surfaces;

Structured light method, which projects a specific light signal onto the surface of the object, calculates the position and depth information of the object through the change of the light signal caused by the object, is not suitable for long-distance use, and can not be used under strong light environment, because the projected Coded light will be overwhelmed;

The time-of-flight method emits light pulses from an emitter to an object, and determines the distance of the measured object by calculating the time of flight of the light pulse. This method has low depth accuracy, the recognition distance is limited by the intensity of the light source, and consumes a lot of energy;

As the public is familiar, traditional imaging technology combined with neural networks for object recognition uses pictures collected by ordinary cameras to input trained models to identify objects. One disadvantage of this method is that the pictures collected by ordinary cameras only have two-dimensional information. To achieve higher accuracy, the number of layers of the convolutional neural network is required, the algorithm is complex, and a large number of samples are required to train the model. The probe method, binocular vision method, structured light method, and time-of-flight method have limited scope of application and are only suitable for use in specific environments.

Therefore, it is necessary to provide a new image recognition system to solve the above technical problems.

Application content

The main purpose of this application is to provide an image recognition system with a multi-view imaging module capable of collecting three-dimensional image data and a high recognition rate.

To achieve the above object, the present invention provides an image recognition system including a multi-view imaging module, a memory, a processor, and a computer program stored in the memory and executable on the processor.

The multi-view imaging module includes:

Lens

Photosensitive element

A microlens array disposed between the lens and the photosensitive element and located on a focal plane of an imaging side of the lens, the microlens array including a plurality of microlenses arranged in an array;

Wherein, the light of the imaged object is projected onto the plurality of microlenses of the microlens array from the different directions through the lens, refracted through the plurality of microlenses, and then incident on the light receiving element. Image information of multiple imaged objects with different angles from different photosensitive areas;

When the processor executes the computer program, the steps of the image recognition method described below are implemented:

Repeatedly collecting image information of a plurality of different-angled identified objects obtained by imaging the multi-angled imaging module for a single perspective of the identified object;

Using the set of image information of the identified objects at different angles collected each time as sample training data, training based on a convolutional neural network model to obtain a target model;

The image information of multiple to-be-identified objects at different angles obtained by imaging the to-be-recognized object through the multi-view imaging module is brought into the target model to perform image recognition on the to-be-recognized object.

Further, the structure of the plurality of microlenses in the microlens array is one selected from the following structures:

The lens shapes and sizes of the plurality of microlenses are the same, and the focal lengths are the same and fixed;

Lens shapes and sizes of the plurality of microlenses are different, and lens focal lengths of the plurality of microlenses are different and fixed;

The lens shapes and sizes of the plurality of microlenses are the same, and the focal length is adjustable;

The lens shapes and sizes of the plurality of micro lenses are different, and the focal length is adjustable.

Further, the plurality of microlenses in the microlens array are distributed on a transparent structure in a uniform or non-uniform manner; the transparent structure is a convex transparent structure, a concave transparent structure, or a planar transparent structure.

Further, the microlens array is a one-time imaging microlens array or a multi-time imaging microlens array.

Further, the multi-imaging microlens array includes at least two microlens arrays arranged in parallel.

Further, the photosensitive element is a complementary metal oxide semiconductor image sensor or a charge-coupled device image sensor.

Further, the photosensitive element is provided with a multi-pixel photosensitive array, and the multi-pixel photosensitive array includes a plurality of photosensitive regions provided in one-to-one correspondence with a plurality of micro lenses on the micro lens array.

Further, after the step of repeatedly collecting image information of multiple identified objects at different angles obtained by imaging the multi-view imaging module for a single perspective of the identified object, the method further includes:

Obtaining image and depth information of the identified object at different angles through a light reconstruction algorithm according to the image information of the identified objects at different angles obtained through the multiple imaging;

The step of obtaining the target model by training the convolutional neural network model based on the collection of the image information of the multiple identified objects at different angles each time as the sample training data includes:

A set of multiple different-angle images and depth information of the identified object collected each time is used as sample training data, and training is performed based on a convolutional neural network model to obtain a target model.

Further, the method further includes the step of repeatedly collecting image information of multiple identified objects at different angles obtained by imaging the multi-view imaging module for multiple different perspectives of the identified object.

Further, when the focal length of the microlens is adjustable, the step of repeatedly collecting image information of multiple identified objects at different angles obtained by the multi-view imaging module imaging a single perspective of the identified object includes:

Adjusting a focal length of a plurality of microlenses on the microlens array;

At different focal lengths, multiple pieces of image information of the identified object at different angles obtained by the multi-view imaging module imaging at multiple different perspectives of the identified object are repeatedly collected.

In the proposed image recognition system provided in the present invention, a multi-view imaging module introduces a micro lens array into a conventional imaging system, and can obtain images and depth information of multiple weakly different perspectives of an object at a time, which has a simple structure and is applicable. A wide range; combined with the image recognition technology of the neural network algorithm model, in the later recognition of the recognized object, multiple weakly different images of the same object are used for recognition, which greatly improves the accuracy of object recognition.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to explain the technical solutions in the embodiments of the present application more clearly, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application. Those of ordinary skill in the art can obtain other drawings according to the drawings without paying creative labor.

1 is a schematic structural diagram of a multi-view imaging module according to an embodiment of the present invention;

2 is a schematic structural diagram of a photosensitive element of the multi-view imaging module in FIG. 1;

3 is a schematic structural diagram of an image recognition system according to an embodiment of the present invention;

4 is a schematic diagram of an image recognition result according to an embodiment of the present invention;

5 is a schematic diagram of another image recognition result in an embodiment of the present invention;

FIG. 6 is a flowchart of an image recognition method according to an embodiment of the present invention.

detailed description

In the following, the technical solutions in the embodiments of the present invention will be clearly and completely described with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by a person of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

It should be noted that all directional indicators (such as up, down, left, right, front, back, horizontal, radial, horizontal, vertical, etc.) in the embodiments of the present invention are only used to explain in a specific posture (such as the accompanying drawings) (Shown) The relative positional relationship, motion, etc. of the components under the direction, if the specific posture changes, the directionality indication will change accordingly.

In addition, descriptions such as “first”, “second”, and the like in the present invention are for descriptive purposes only, and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "a plurality" is at least two, for example, two, three, etc., unless it is specifically and specifically defined otherwise.

In the present invention, the terms “connected”, “fixed” and the like shall be understood in a broad sense unless otherwise specified and defined, for example, “fixed” may be a fixed connection, may be a detachable connection, or be integrated into one; It is a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium. It can be the internal connection of two elements or the interaction relationship between two elements, unless it is clearly defined otherwise. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

In addition, the technical solutions between the various embodiments of the present invention can be combined with each other, but must be based on what can be achieved by a person of ordinary skill in the art. When the combination of technical solutions conflicts or cannot be achieved, such a combination of technical solutions should be considered It does not exist and is not within the scope of protection claimed by the present invention.

As shown in FIG. 1, it is a schematic structural diagram of a multi-view imaging module 100 in a first embodiment of the present invention.

The multi-view imaging module 100 includes a lens 10, a microlens array 20, and a photosensitive element 30.

The microlens array 20 is disposed between the lens 10 and the photosensitive element 30 and is located on a focal plane of the imaging side of the lens 10. The micro lens array 20 includes a plurality of micro lenses 21 arranged in an array.

The light of the imaged object 101 is incident on the inside of the multi-view imaging module 100 through the lens 10, that is, the light of the imaged object 101 is projected through the lens 10 to the micro lens array 20 from different directions. The microlenses 21 are refracted by the plurality of microlenses 21 and are incident on different photosensitive areas 31 of the photosensitive element 30 to form image information of a plurality of imaged objects with different angles. Since the source angles of the light collected by each photosensitive region 31 are different, it is possible to record images and depth information of multiple weakly different perspectives of the imaged object 101.

The multi-view imaging module 100 provided in this embodiment introduces a micro lens array 20 into a conventional imaging system, and can obtain images and depth information of multiple weakly different perspectives of an object at a time, with a simple structure and a wide application range. .

Further, in conjunction with FIG. 2, the photosensitive element 30 is provided with a multi-pixel photosensitive array 33. The multi-pixel photosensitive array 33 includes a plurality of photosensitive elements arranged one-to-one corresponding to the plurality of micro-lenses on the micro-lens array 20. Area 31.

The photosensitive element 30 may be a complementary metal oxide semiconductor (CMOS) image sensor or a charge-coupled device (CCD) image sensor.

Specifically, the microlens array 20 is set on the focal plane of the imaging side of the lens 10, and a photosensitive element 30 (CCD or CMOS) is placed behind the microlens array 20, and the light refracted by each microlens 21 can be covered correspondingly. A plurality of pixels on the photosensitive element 30 form a large pixel unit; a corresponding one of the plurality of micro lenses on the micro lens array 20 is provided on the multi-pixel photosensitive array 33 corresponding to each of the large pixel units. Set the photosensitive area 31.

The light emitted by the imaged object 101 enters from the lens 10 of the multi-view imaging module 100, and then is projected from different directions to the microlens array 20. Each microlens 21 of the microlens array 20 refracts and then projects the light to the back of the light. On the element 20, a complete image can be collected by the photosensitive area 31 corresponding to each large pixel unit; and the light angle collected by the photosensitive area 31 corresponding to each large pixel unit is different, so that the imaged image can be recorded. Images and depth information of multiple weakly different perspectives of the object 101.

Optionally, in a preferred embodiment, the structures of the plurality of microlenses 21 in the microlens array 20 may be microlenses with the same lens shape and size, the same focal length, and a fixed structure. For example, each of the microlenses 21 may be a convex lens with convex surfaces on both sides, or a semi-convex convex lens with convex surfaces on one side; the shape of the microlenses 21 may be circular, square, hexagonal, or octagonal. Shape etc.

Optionally, in a preferred embodiment, the structures of the plurality of microlenses 21 in the microlens array 20 may be different in lens shape and size, and the lens focal lengths of the plurality of microlenses are different and fixed. Micro lens composition. For example, the microlens 21 may be a plurality of convex lenses having convex surfaces on both sides having different sizes and focal lengths, or the microlens 21 may be a plurality of semi-convex convex lenses having convex surfaces on different sides having different sizes and focal lengths.

Optionally, in a preferred embodiment, the structures of the plurality of microlenses 21 in the microlens array 20 may be microlenses with the same lens shape and size and adjustable focal lengths. For example, the micro-lens 21 may be composed of multiple micro-lenses with the same size and size but adjustable focal length; wherein the implementation of the adjustable focal length may be electrical, light, thermal, etc. The technician can set it according to the needs, and it will not be repeated here.

Optionally, in a preferred embodiment, the structures of the plurality of microlenses 21 in the microlens array 20 may be microlenses with different lens shapes and sizes and adjustable focal lengths. For example, the micro-lens 21 may be composed of multiple micro-lenses with different sizes and sizes, but with adjustable focal length; wherein the implementation of the adjustable focal length may be electrical, light, thermal, etc. Those skilled in the art can make settings according to requirements, and details are not described herein again.

Further, in an embodiment, the plurality of microlenses 21 in the microlens array 20 may be distributed on a transparent structure in a uniform or non-uniform manner; the transparent structure is a convex transparent structure and a concave surface. Transparent structure or flat transparent structure.

For example, the micro lens array 20 may be a plurality of convex lenses 211 uniformly distributed on a flat transparent structure, and the plurality of convex lenses 211 are connected to form a whole micro lens array 20 through a flat transparent structure;

For another example, the micro lens array 20 may be a plurality of convex lenses 211 unevenly distributed on a planar transparent structure, and the plurality of convex lenses 211 are connected to form a whole micro lens array 20 through a planar transparent structure;

The micro lens array 20 may also be a plurality of convex lenses 211 uniformly distributed on a convex transparent structure. The plurality of convex lenses 211 are connected to form a whole micro lens array 20 through the convex transparent structure.

Further, the microlens array 20 may be a one-time imaging microlens array or a multi-time imaging microlens array. The multi-imaging microlens array 20 may include multiple layers of microlenses, for example, at least two microlens arrays arranged in parallel. The light reaching the microlens array 20 is refracted by the multiple layers of microlenses, and then emitted to Imaging is performed on the photosensitive element 30.

With reference to FIG. 3 together, the present invention further provides an image recognition system 200 including the multi-view imaging module 100, a memory 201, a processor 202, and stored in the memory 201 and stored in the processor 202. Computer program running on.

The image recognition system 200 uses the multi-view imaging module 100 to repeatedly acquire image information of multiple identified objects at different angles obtained by repeatedly imaging a single perspective of the identified object; and then collects each time The set of image information of the identified objects at different angles is used as sample training data, and training is performed based on the convolutional neural network model to obtain a target model; the object to be identified is acquired through the multi-view imaging module to be imaged. The obtained image information of the identified objects at different angles is brought into the target model to perform image recognition on the object to be identified.

Please combine Figure 4 together. Generally, when identifying objects to be recognized, if the targets match, you will get results similar to Figure 4, the picture matching rate of one view is the highest, and the picture matching rates of other views decline sequentially; The angle of view of the picture with the highest matching rate is the single angle of view when the multi-view imaging module 100 collects training samples of the identified object;

If the targets do not match, you will get something like that shown in Figure 5, the matching rate is very low, and it is irregular.

In an optional embodiment, multiple pieces of image information of the identified objects at different angles obtained by imaging the multi-angle imaging module 100 for multiple different perspectives of the identified object may be repeatedly collected; it can be understood that In this way, after collecting image information of multiple recognized objects at different angles obtained by imaging at different different perspectives as training samples, training can be performed to accurately identify each angle of the object to be recognized.

In the image recognition system 200 provided in this embodiment, a microlens array 20 is introduced into a conventional imaging system, and images and depth information of multiple weakly different perspectives of an object can be obtained at one time, which is combined with image recognition of a neural network algorithm model Technology, in the later recognition of the identified object, the use of multiple weakly different images of the same object for recognition, greatly improving the accuracy of object recognition.

Further, according to the image information of multiple identified objects at different angles obtained through the multiple imaging, a plurality of different angle images and depth information of the identified object can be obtained through a light reconstruction algorithm; The set of multiple different angle images and depth information of the identified object is used as sample training data, and training is performed based on the convolutional neural network model to obtain the target model.

Through the image recognition system 200 in this embodiment, images and depth information of multiple different angles of the identified object can be obtained; compared with the traditional imaging system, only two-dimensional image information of the object can be obtained, and the third-dimensional information recognition method is lost. The image recognition system 200 in this embodiment greatly improves the accuracy of object recognition.

Optionally, when the focal length of the microlens is adjustable, the focal lengths of multiple microlenses on the microlens array can also be adjusted; at different focal lengths, repeatedly acquiring the multi-view imaging module Image information of a plurality of identified objects at different angles obtained by imaging at different perspectives of the identified object. Therefore, a large-distance multi-point reversal can be implemented in the vertical range, and each object in the vertical range can be accurately identified.

In conjunction with FIG. 6, the present invention further provides an image recognition method 300 including steps:

Step S10: repeatedly collecting image information of multiple identified objects at different angles obtained by imaging the multi-view imaging module for a single perspective of the identified object;

Step S20: Use the set of image information of the plurality of identified objects of different angles collected each time as sample training data, and perform training based on the convolutional neural network model to obtain a target model;

In step S30, image information of multiple to-be-identified objects at different angles obtained by imaging the to-be-recognized object through the multi-view imaging module is brought into the target model to perform image recognition on the to-be-recognized object.

In the image recognition method 300 provided in this embodiment, images and depth information of multiple weakly different perspectives of an object can be obtained by imaging at one time, and combined with the image recognition technology of the neural network algorithm model, when the recognized object is recognized at a later stage , Using multiple weakly different images of the same object for recognition, greatly improving the accuracy of object recognition.

Optionally, after step S10, the method further includes: obtaining, based on the image information of multiple identified objects at different angles obtained through the multiple imaging, multiple images at different angles of the identified object through a light reconstruction algorithm, and Depth information

The step 20 may specifically include: using a collection of multiple different-angle images and depth information of the identified object each time as sample training data, training based on a convolutional neural network model to obtain a target model .

As a result, images and depth information of multiple different angles of the identified object can be obtained. Compared with the conventional imaging system, only the two-dimensional image information of the object can be obtained, and the recognition method of the third-dimensional information is lost. Greatly improved the accuracy of object recognition.

Optionally, the image recognition method 300 may further include: repeatedly collecting image information of multiple identified objects at different angles obtained by imaging the multi-view imaging module for multiple different perspectives of the identified object.

It can be understood that, after image information of a plurality of recognized objects at different angles obtained by imaging a plurality of different perspectives of the recognized object, the step S20 is correspondingly performed during training based on a convolutional neural network model, The input is the characteristic data of each angle of the identified object. In this way, after collecting image information of multiple recognized objects at different angles obtained by imaging at different perspectives as training samples, training can be performed to accurately identify each angle of the object to be recognized.

Optionally, when the focal length of the micro lens is adjustable, the step S10 may include:

Adjusting a focal length of a plurality of microlenses on the microlens array;

Please refer to FIG. 3 again. In an embodiment of the present invention, when a computer program stored in the memory 201 and executable on the processor 202 is executed, an image according to any one of the foregoing may be implemented. Identify method steps.

It can be understood that, in the description of this specification, referring to the description of the terms “one embodiment”, “another embodiment”, “other embodiments”, or “first embodiment to the Nth embodiment” and the like means Specific features, structures, materials, or characteristics described in connection with this embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

It should be noted that, in this article, the terms "including", "including" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, It also includes other elements not explicitly listed, or elements inherent to such a process, method, article, or system. Without more restrictions, an element limited by the sentence "including a ..." does not exclude the existence of other identical elements in the process, method, article, or system that includes the element.

The preferred embodiments of the present invention do not therefore limit the patent scope of the present invention. Under the concept of the present invention, equivalent structural transformations made using the description and drawings of the present invention, or directly / indirectly used in other related technical fields All are included in the patent protection scope of the present invention.

Claims

An image recognition system includes a multi-view imaging module, a memory, a processor, and a computer program stored in the memory and operable on the processor.

The multi-view imaging module includes:

Lens

Photosensitive element

A microlens array disposed between the lens and the photosensitive element and located on a focal plane of an imaging side of the lens, the microlens array including a plurality of microlenses arranged in an array;

Wherein, the light of the imaged object is projected onto the plurality of microlenses of the microlens array from the different directions through the lens, refracted through the plurality of microlenses, and then incident on the light receiving element. Image information of multiple imaged objects with different angles from different photosensitive areas;

When the processor executes the computer program, the steps of the image recognition method described below are implemented:

Repeatedly collecting image information of a plurality of different-angled identified objects obtained by imaging the multi-angled imaging module for a single perspective of the identified object;

Using the set of image information of the identified objects at different angles collected each time as sample training data, training based on a convolutional neural network model to obtain a target model;

The image information of multiple to-be-identified objects at different angles obtained by imaging the to-be-recognized object through the multi-view imaging module is brought into the target model to perform image recognition on the to-be-recognized object.
The image recognition system according to claim 1, wherein a structure of the plurality of microlenses in the microlens array is one selected from the following structures:

The lens shapes and sizes of the plurality of microlenses are the same, and the focal lengths are the same and fixed;

Lens shapes and sizes of the plurality of microlenses are different, and lens focal lengths of the plurality of microlenses are different and fixed;

The lens shapes and sizes of the plurality of microlenses are the same, and the focal length is adjustable;

The lens shapes and sizes of the plurality of micro lenses are different, and the focal length is adjustable.
The image recognition system according to claim 1, wherein the plurality of microlenses in the microlens array are distributed on a transparent structure in a uniform or non-uniform manner; the transparent structure is a convex transparent structure , Concave transparent structure or flat transparent structure.
The image recognition system according to claim 1, wherein the microlens array is a one-time imaging microlens array or a multi-time imaging microlens array.
The image recognition system according to claim 4, wherein the multi-imaging microlens array comprises at least two microlens arrays arranged in parallel.
The image recognition system according to any one of claims 1-5, wherein the photosensitive element is a complementary metal oxide semiconductor image sensor or a charge-coupled device image sensor.
The image recognition system according to claim 6, wherein a multi-pixel photosensitive array is provided on the photosensitive element, and the multi-pixel photosensitive array comprises a plurality of micro-lenses on the micro-lens array. Corresponding setting of the photosensitive area.
The image recognition system according to claim 1, wherein after the step of repeatedly collecting image information of a plurality of different angles of the identified object obtained by imaging the multi-angle imaging module for a single perspective of the identified object, Also includes:

Obtaining image and depth information of the identified object at different angles through a light reconstruction algorithm according to the image information of the identified objects at different angles obtained through the multiple imaging;

The step of obtaining the target model by training the convolutional neural network model based on the collection of the image information of the multiple identified objects at different angles each time as the sample training data includes:

A set of multiple different-angle images and depth information of the identified object collected each time is used as sample training data, and training is performed based on a convolutional neural network model to obtain a target model.
The image recognition system according to claim 1, further comprising the step of: repeatedly collecting the images of the identified objects at different angles obtained by imaging the multi-view imaging module for multiple different perspectives of the identified objects. Image information.
The image recognition system according to claim 9, wherein, when the focal length of the microlens is adjustable, multiple different angles obtained by imaging the multi-angle imaging module for a single angle of view of the identified object are repeatedly acquired. The step of image information of the identified object includes:

Adjusting a focal length of a plurality of microlenses on the microlens array;

At different focal lengths, multiple pieces of image information of the identified object at different angles obtained by the multi-view imaging module imaging at multiple different perspectives of the identified object are repeatedly collected.