WO2021027890A1 - Procédé et dispositif de production d'image de plaque d'immatriculation, et support de stockage informatique - Google Patents

Procédé et dispositif de production d'image de plaque d'immatriculation, et support de stockage informatique Download PDF

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Publication number
WO2021027890A1
WO2021027890A1 PCT/CN2020/108967 CN2020108967W WO2021027890A1 WO 2021027890 A1 WO2021027890 A1 WO 2021027890A1 CN 2020108967 W CN2020108967 W CN 2020108967W WO 2021027890 A1 WO2021027890 A1 WO 2021027890A1
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WIPO (PCT)
Prior art keywords
license plate
virtual
virtual license
characters
dimensional model
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PCT/CN2020/108967
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English (en)
Chinese (zh)
Inventor
李欢
Original Assignee
杭州海康威视数字技术股份有限公司
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Publication of WO2021027890A1 publication Critical patent/WO2021027890A1/fr

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    • 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
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/60—Type of objects
    • G06V20/64—Three-dimensional objects
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/60—Type of objects
    • G06V20/62—Text, e.g. of license plates, overlay texts or captions on TV images
    • G06V20/625—License plates
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V30/00—Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
    • G06V30/10—Character recognition

Definitions

  • the theoretical three-dimensional view of the virtual license plate is adjusted to obtain the three-dimensional model of the virtual license plate.
  • the determining module is used to determine the surface attribute information of the virtual license plate
  • a three-dimensional model of the virtual license plate is generated according to the shape of the frame, the background color, the plurality of characters, and the sequence of the plurality of characters.
  • the building module is specifically configured to determine the shape of the frame of the virtual license plate according to the deformation of the virtual license plate.
  • the surface attribute information of the license plate includes at least one or more of the following attribute information:
  • the shooting parameters of the virtual shooting scene include at least one or more of the following parameters:
  • the color of the light in the virtual shooting scene The color of the light in the virtual shooting scene, the brightness of the light, the exposure parameter of the virtual camera in the virtual shooting scene, the positions of the virtual camera and the virtual license plate in the virtual shooting scene.
  • the device further includes:
  • the marking module is used for marking the license plate recognition result on the virtual license plate in the license plate image.
  • a computer-readable storage medium is provided, and instructions are stored in the computer-readable storage medium, which when run on a computer, cause the computer to execute the steps of the card image generation method provided in any one of the above aspects.
  • a computer program product containing instructions which when running on a computer, causes the computer to execute the steps of the card image generation method provided by any of the above aspects.
  • a virtual license plate can be simulated according to actual needs, and then a three-dimensional model of the virtual license plate can be constructed, and the license plate surface attribute information of the virtual license plate can be determined to generate a license plate image for the virtual license plate. Therefore, when determining the training samples for the neural network model for license plate recognition, the license plate image can be directly generated through the embodiment of the application according to actual needs, and the license plate image can be obtained without the need for the camera to collect the real license plate, which improves the acquisition The efficiency of license plate images.
  • Fig. 1 is a flowchart of a method for generating a license plate image provided by an embodiment of the present application.
  • Fig. 2 is a block diagram of a device for generating a license plate image provided by an embodiment of the present application.
  • License plate recognition refers to: recognizing the characters used to indicate the license plate mark in the license plate image to obtain the license plate mark corresponding to the license plate image.
  • the license plate identification includes multiple characters, and the multiple characters may be one or more of numbers, English letters, Chinese characters, and other characters.
  • the license plate identification can also be called the license plate number.
  • fast license plate recognition can be achieved through neural network technology.
  • the diversity of training samples used to train neural network directly affects the recognition accuracy of neural network after training. Therefore, it is necessary to obtain a large number of different types of training samples to improve the recognition accuracy of the trained neural network model.
  • the training samples refer to multiple license plate images, and each license plate image is marked with a label indicating the license plate identification.
  • the license plate image generation method adopted in the embodiment of the present application is applied to the scene of obtaining training samples for the neural network model for license plate recognition.
  • Fig. 1 is a flowchart of a method for generating a license plate image provided by an embodiment of the present application. As shown in Figure 1, the method includes the following steps:
  • Step 101 Construct a three-dimensional model of a virtual license plate.
  • the virtual license plate is a simulated license plate according to requirements.
  • the embodiment of the present application can simulate a virtual license plate according to actual needs, and then generate a license plate image for the virtual license plate through step 101 to step 103. Therefore, when determining the training samples for the neural network model for license plate recognition, the license plate image can be directly generated through steps 101 to 103 according to actual needs, and the license plate image can be obtained without the need for the camera to collect the real license plate. The efficiency of obtaining license plate images.
  • the virtual license plate is a license plate simulated according to actual needs, the license plate images corresponding to different types of virtual license plates can be generated through steps 101 to 103, which improves the diversity of license plate images in the training sample, thereby improving the subsequent basis The recognition accuracy of the neural network model trained by the training sample.
  • step 101 may specifically be: determine the shape of the frame of the virtual license plate; determine the background color of the virtual license plate; determine the multiple characters and the arrangement of multiple characters on the virtual license plate for indicating the license plate identifier Sequence: According to the shape of the frame, the background color, multiple characters and the sequence of multiple characters, a three-dimensional model of the virtual license plate is generated.
  • the shape of the frame of the virtual license plate may include the shape of the outer frame and may also include the shape of the inner frame.
  • the realization of determining the shape of the outer frame of the virtual license plate may be: a plurality of outer frame options are displayed in the current display interface, and each outer frame option indicates a kind of outer frame shape.
  • the shape of the outer frame of the virtual license plate is determined based on the outer frame option corresponding to the selection operation.
  • the selection operation can be triggered by the manager according to actual needs. That is, the shape of the outer frame of the virtual license plate is determined according to actual needs. For example, if it is currently necessary to generate a license plate image for a virtual license plate of a military license plate, you can select the frame option corresponding to the military license plate from a number of frame options, so that the shape of the frame of the virtual license plate is determined to have the shape of the military license plate. The shape of the frame.
  • the foregoing determination of the shape of the inner frame of the license plate and the background color of the virtual license plate can refer to the foregoing implementation of determining the shape of the outer frame of the virtual license plate, which will not be elaborated here.
  • the deformation of the virtual license plate can also be considered.
  • the deformation conditions include deformation due to breakage, deformation due to folding, deformation due to wrinkle distortion, and so on.
  • the shape of the frame of a normal license plate is a rectangle. When the license plate is damaged, a certain corner of the frame of the license plate may be worn away. At this time, the shape of the outer frame of the license plate is not a rectangle, but may be a trapezoid.
  • a virtual virtual machine After determining the frame shape, background color, multiple characters, and the sequence of multiple characters by any of the above methods, a virtual virtual machine can be generated according to the frame shape, background color, multiple characters, and the sequence of multiple characters.
  • Three-dimensional model of license plate The three-dimensional model of the virtual license plate is used to indicate the three-dimensional view of the virtual license plate.
  • the surface attribute information of the license plate includes at least one or more of the following attribute information:
  • Step 103 Generate a license plate image for the virtual license plate according to the surface attribute information of the license plate and the three-dimensional model.
  • the license plate image for the virtual license plate can be generated through step 103.
  • the shooting parameters of the virtual shooting scene described above include at least one or more of the following parameters: the color of the light in the virtual shooting scene, the brightness of the light, and the exposure parameters of the virtual camera in the virtual shooting scene , The position of the virtual camera and the virtual license plate in the virtual shooting scene.
  • the exposure parameters of the virtual camera may include the exposure time and exposure intensity of the virtual camera.
  • the shooting parameters are only used for illustration.
  • the shooting parameters may include any shooting factors that can affect the license plate image collected by the camera, and the examples are not described here.
  • the virtual license plate constructed by the embodiment of the present application is used for subsequent training of the recognition model. Therefore, after generating the license plate image for the virtual license plate according to the license plate attribute information and the three-dimensional model in step 103, it can also be included in the license plate image Annotate the license plate recognition result on the virtual license plate, so that the license plate recognition result and the license plate image are used as a training sample in the future.
  • a virtual license plate can be simulated according to actual needs, and then a three-dimensional model of the virtual license plate is constructed, and the license plate surface attribute information of the virtual license plate is determined to generate a license plate image for the virtual license plate. Therefore, when determining the training samples for the neural network model for license plate recognition, the license plate image can be directly generated through the embodiment of the application according to actual needs, and the license plate image can be obtained without the need for the camera to collect the real license plate, which improves the acquisition The efficiency of license plate images.
  • the building module is specifically used for:
  • license plate attribute information and three-dimensional model generate virtual photos for virtual license plates
  • a virtual license plate can be simulated according to actual needs, and then a three-dimensional model of the virtual license plate is constructed, and the license plate surface attribute information of the virtual license plate is determined to generate a license plate image for the virtual license plate. Therefore, when determining the training samples for the neural network model for license plate recognition, the license plate image can be directly generated through the embodiment of the application according to actual needs, and the license plate image can be obtained without the need for the camera to collect the real license plate, which improves the acquisition The efficiency of license plate images.
  • the virtual license plate is a license plate simulated according to actual needs
  • the license plate images corresponding to different types of virtual license plates can be generated through the embodiments of the present application, which improves the diversity of the license plate images in the training samples, thereby improving the subsequent training according to The recognition accuracy of the neural network model trained by the sample.
  • the terminal 300 includes a processor 301 and a memory 302.
  • the power supply 309 is used to supply power to various components in the terminal 300.
  • the power source 309 may be alternating current, direct current, disposable batteries, or rechargeable batteries.
  • the rechargeable battery may support wired charging or wireless charging.
  • the rechargeable battery can also be used to support fast charging technology.
  • the acceleration sensor 311 can detect the magnitude of acceleration on the three coordinate axes of the coordinate system established by the terminal 300.
  • the acceleration sensor 311 may be used to detect the components of the gravitational acceleration on three coordinate axes.
  • the processor 301 may control the touch screen 305 to display the user interface in a horizontal view or a vertical view according to the gravity acceleration signal collected by the acceleration sensor 311.
  • the acceleration sensor 311 may also be used for the collection of game or user motion data.
  • the gyroscope sensor 312 can detect the body direction and rotation angle of the terminal 300, and the gyroscope sensor 312 can cooperate with the acceleration sensor 311 to collect the user's 3D actions on the terminal 300.
  • the processor 301 can implement the following functions according to the data collected by the gyroscope sensor 312: motion sensing (for example, changing the UI according to the user's tilt operation), image stabilization during shooting, game control, and inertial navigation.
  • the pressure sensor 313 may be disposed on the side frame of the terminal 300 and/or the lower layer of the touch screen 305.
  • the processor 301 performs left and right hand recognition or quick operation according to the holding signal collected by the pressure sensor 313.
  • the processor 301 controls the operability controls on the UI interface according to the user's pressure operation on the touch display screen 305.
  • the operability control includes at least one of a button control, a scroll bar control, an icon control, and a menu control.
  • the embodiment of the present application also provides a computer program product containing instructions, which when running on a terminal, causes the terminal to execute the method for generating a license plate image provided in the foregoing embodiment.

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Multimedia (AREA)
  • Data Mining & Analysis (AREA)
  • Software Systems (AREA)
  • Geometry (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Computer Graphics (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • General Engineering & Computer Science (AREA)
  • Evolutionary Computation (AREA)
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  • Bioinformatics & Computational Biology (AREA)
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  • Life Sciences & Earth Sciences (AREA)
  • Processing Or Creating Images (AREA)

Abstract

La présente invention se rapporte au domaine technique de l'apprentissage des réseaux neuronaux, et concerne un procédé, et un dispositif de production d'image de plaque d'immatriculation, et un support de stockage informatique. Le procédé consiste à : construire un modèle tridimensionnel d'une plaque d'immatriculation virtuelle, et déterminer des informations d'attribut de surface de plaque d'immatriculation de la plaque d'immatriculation virtuelle, de façon à produire une image de plaque d'immatriculation pour la plaque d'immatriculation virtuelle. Ainsi, lorsqu'un échantillon d'apprentissage pour un modèle de réseau neuronal pour la reconnaissance de plaque d'immatriculation est déterminé, une image de plaque d'immatriculation peut être directement produite au moyen des modes de réalisation de la présente invention selon les demandes réelles, sans collecter une plaque de d'immatriculation réelle au moyen d'une caméra pour obtenir l'image de plaque d'immatriculation, ce qui permet d'améliorer l'efficacité d'obtention de l'image de plaque d'immatriculation. De plus, parce que la plaque d'immatriculation virtuelle est une plaque d'immatriculation simulée selon les demandes réelles, des images de plaque d'immatriculation correspondant à différents types de plaques d'immatriculation virtuelles peuvent être produites au moyen des modes de réalisation de la présente invention, améliorant ainsi la diversité d'images de plaque d'immatriculation dans l'échantillon d'apprentissage, et améliorant ainsi la précision de reconnaissance du modèle de réseau neuronal entraîné sur la base de l'échantillon d'apprentissage.
PCT/CN2020/108967 2019-08-15 2020-08-13 Procédé et dispositif de production d'image de plaque d'immatriculation, et support de stockage informatique WO2021027890A1 (fr)

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CN201910755413.3 2019-08-15
CN201910755413.3A CN112396076A (zh) 2019-08-15 2019-08-15 车牌图像生成方法、装置及计算机存储介质

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CN113052174A (zh) * 2021-03-26 2021-06-29 北京百度网讯科技有限公司 车牌数据样本生成方法、装置、电子设备和存储介质
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CN115497084A (zh) * 2022-11-14 2022-12-20 深圳天海宸光科技有限公司 一种仿真车牌图片和字符级别标注的生成方法

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Publication number Priority date Publication date Assignee Title
CN113052174A (zh) * 2021-03-26 2021-06-29 北京百度网讯科技有限公司 车牌数据样本生成方法、装置、电子设备和存储介质
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CN115497084B (zh) * 2022-11-14 2023-03-31 深圳天海宸光科技有限公司 一种仿真车牌图片和字符级别标注的生成方法

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