WO2020024079A1 - Système de reconnaissance d'image - Google Patents

Système de reconnaissance d'image Download PDF

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Publication number
WO2020024079A1
WO2020024079A1 PCT/CN2018/097687 CN2018097687W WO2020024079A1 WO 2020024079 A1 WO2020024079 A1 WO 2020024079A1 CN 2018097687 W CN2018097687 W CN 2018097687W WO 2020024079 A1 WO2020024079 A1 WO 2020024079A1
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WO
WIPO (PCT)
Prior art keywords
imaging
different
microlenses
image recognition
image information
Prior art date
Application number
PCT/CN2018/097687
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English (en)
Chinese (zh)
Inventor
王星泽
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合刃科技（深圳）有限公司
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Publication date
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Priority to CN201880002314.1A priority Critical patent/CN109496316B/zh
Priority to PCT/CN2018/097687 priority patent/WO2020024079A1/fr
Publication of WO2020024079A1 publication Critical patent/WO2020024079A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/10—Image acquisition
    • G06V10/12—Details of acquisition arrangements; Constructional details thereof
    • G06V10/14—Optical characteristics of the device performing the acquisition or on the illumination arrangements
    • G06V10/147—Details of sensors, e.g. sensor lenses
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks

Definitions

  • the present application relates to the technical field of optical image processing, and in particular, to an image recognition system.
  • the object recognition process can be divided into: image acquisition, feature extraction, classifier classification, and classification results.
  • 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.
  • 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;
  • 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.
  • 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:
  • 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;
  • 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.
  • 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 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 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.
  • microlens array is a one-time imaging microlens array or a multi-time imaging microlens array.
  • the multi-imaging microlens array includes at least two microlens arrays arranged in parallel.
  • the photosensitive element is a complementary metal oxide semiconductor image sensor or a charge-coupled device image sensor.
  • the photosensitive element is provided with a multi-pixel photosensitive array
  • 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.
  • the method further includes:
  • 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 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.
  • 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:
  • 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.
  • 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.
  • FIG. 1 is a schematic structural diagram of a multi-view imaging module according to an embodiment of the present invention.
  • FIG. 2 is a schematic structural diagram of a photosensitive element of the multi-view imaging module in FIG. 1;
  • FIG. 3 is a schematic structural diagram of an image recognition system according to an embodiment of the present invention.
  • FIG. 4 is a schematic diagram of an image recognition result according to an embodiment of the present invention.
  • FIG. 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.
  • 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.
  • 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.
  • 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 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. .
  • 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.
  • CMOS complementary metal oxide semiconductor
  • CCD charge-coupled device
  • 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 is set on the focal plane of the imaging side of the lens 10
  • a photosensitive element 30 CCD or CMOS
  • 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.
  • 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.
  • 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.
  • 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.
  • 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 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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;
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • the focal length of the microlens 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.
  • 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;
  • 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.
  • 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.
  • 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 .
  • 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.
  • 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.
  • the step S10 may include:
  • 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.

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Abstract

L'invention concerne un système de reconnaissance d'image, comprenant un module d'imagerie multivue ayant un réseau de microlentilles. La lumière d'un objet imagé est réfractée par une pluralité de microlentilles respectivement et est ensuite incidente sur différentes régions photosensibles de l'élément photosensible. L'imagerie peut être réalisée une fois pour obtenir des images et des informations de profondeur d'une pluralité d'angles de visualisation faibles différents de l'objet ; des informations d'image d'un objet reconnu à une pluralité d'angles différents obtenus par imagerie d'angle de visualisation unique de l'objet reconnu par le module d'imagerie multivue sont acquises au moyen d'une répétition ; un ensemble d'informations d'image de l'objet reconnu au niveau de la pluralité d'angles différents acquis chaque fois est utilisé en tant que données d'apprentissage d'échantillon, et l'apprentissage est réalisé sur la base d'un modèle de réseau neuronal convolutionnel pour obtenir un modèle cible ; et ainsi, un objet à reconnaître est soumis à une reconnaissance d'image. Des images du même objet à une pluralité d'angles de visualisation faibles différents sont utilisées pour la reconnaissance, ce qui améliore considérablement la précision de la reconnaissance d'objets.
PCT/CN2018/097687 2018-07-28 2018-07-28 Système de reconnaissance d'image WO2020024079A1 (fr)

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CN201880002314.1A CN109496316B (zh) 2018-07-28 2018-07-28 图像识别系统
PCT/CN2018/097687 WO2020024079A1 (fr) 2018-07-28 2018-07-28 Système de reconnaissance d'image

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