WO2019127075A1 - Procédé d'identification de l'année d'une pièce de monnaie, dispositif de terminal et support d'enregistrement lisible par ordinateur - Google Patents

Procédé d'identification de l'année d'une pièce de monnaie, dispositif de terminal et support d'enregistrement lisible par ordinateur Download PDF

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Publication number
WO2019127075A1
WO2019127075A1 PCT/CN2017/118891 CN2017118891W WO2019127075A1 WO 2019127075 A1 WO2019127075 A1 WO 2019127075A1 CN 2017118891 W CN2017118891 W CN 2017118891W WO 2019127075 A1 WO2019127075 A1 WO 2019127075A1
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WIPO (PCT)
Prior art keywords
coin
year
image
target
coordinates
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PCT/CN2017/118891
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English (en)
Chinese (zh)
Inventor
陈红磊
王磊
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中国科学院深圳先进技术研究院
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Priority to PCT/CN2017/118891 priority Critical patent/WO2019127075A1/fr
Publication of WO2019127075A1 publication Critical patent/WO2019127075A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/24—Aligning, centring, orientation detection or correction of the image

Definitions

  • the present invention belongs to the field of image recognition technologies, and in particular, to a method for identifying a coin year, a terminal device, and a computer readable storage medium.
  • the present invention provides a method for identifying a coin year, a terminal device, and a computer readable storage medium, to solve the existing method for manually recycling coins, which has a large workload, low efficiency, and high labor cost. problem.
  • a first aspect of the invention provides a method of identifying a year of a coin, comprising:
  • a second aspect of the invention provides a terminal device comprising means for performing the method as described in the first aspect above.
  • a third aspect of the present invention provides a terminal device including a memory, a processor, and the ⁇ 0 2019/127075 ⁇ (: 17 ⁇ 2017/118891 a computer program in a memory and operable on the processor, wherein the processor executes the computer program to implement the method as described in the first aspect above A step of.
  • a fourth aspect of the invention provides a computer readable storage medium storing a computer program, wherein the computer program is executed by a processor to implement the first aspect as described above The steps of the method.
  • the present invention obtains a target coin image to be identified; inputs the target coin image to a trained coin year recognition model; and determines, in the target coin image, based on year digital information output by the coin year recognition model The year of the coin. Since the year of the coin to be recognized is recognized by the pre-trained coin year recognition model, it is not necessary to manually classify the coin according to the year of the coin, which not only improves the efficiency of coin recovery but also saves labor costs.
  • FIG. 1 is a flowchart of an implementation of a method for recognizing a coin year according to an embodiment of the present invention
  • FIG. is a schematic diagram of a coin image provided by an embodiment of the present invention.
  • FIG. 215 is a schematic diagram of a lower half of a circular display area in which a coin image is located in a coin image according to an embodiment of the present invention
  • FIG. 2 is a schematic diagram showing the upper half of the circular display area where the coin is located in the coin image provided by the embodiment of the present invention
  • FIG. 2 is a schematic diagram of a coin image in an embodiment of the present invention in which the year number is located in the right half of the circular display area where the coin is located;
  • FIG. is a schematic diagram of a coin image in which the year number is located in the left half of the circular display area where the coin is located;
  • FIG. 3 is a flowchart of an implementation of a method for recognizing a coin year according to another embodiment of the present invention.
  • FIG. 4 is a flowchart of an implementation of 301 in a method for recognizing a coin year according to an embodiment of the present invention
  • FIG. 6 is a flowchart of an implementation of a method for identifying a coin year according to an embodiment of the present invention. ⁇ 0 2019/127075 ⁇ (:17 ⁇ 2017/118891
  • FIG. 7 is a schematic structural diagram of a terminal device according to an embodiment of the present invention.
  • FIG. 8 is a schematic structural diagram of a terminal device according to another embodiment of the present invention.
  • FIG. 9 is a schematic structural diagram of a terminal device according to still another embodiment of the present invention.
  • FIG. 1 is a flowchart of an implementation of a method for recognizing a coin year according to an embodiment of the present invention.
  • the execution subject of the method for recognizing the coin year in this embodiment is a terminal device.
  • the terminal device may be a mobile terminal device such as a smart phone or a tablet computer, or may be another terminal device, and is not limited herein.
  • the method of identifying the year of the coin as shown in FIG. 1 may include the following steps:
  • the image of the coin to be recognized by the camera device may be image-collected to obtain a coin image, and the collected coin image is input to the terminal device.
  • the terminal device receives the coin image input by the user if an instruction for identifying the year of the coin is detected, and determines the coin image input by the user as the target coin image to be recognized. ⁇ 0 2019/127075 ⁇ (:17 ⁇ 2017/118891
  • the target coin image may be one sheet or at least two, and is not limited herein.
  • the coin to be identified may be a renminbi coin, a euro coin, or other types of hard coins, and is not limited herein.
  • the pre-trained coin year model is invoked to process the target coin image.
  • the input amount of the coin year identification unit is a coin image
  • the output amount is the year digital information of the coin in the coin image.
  • the year number information includes the name and coordinates of the four-digit year number used to form the coin year on the coin. For example, if the release date of the coin is 1987, the names of the four digits on the coin are "1", "9", "8", "7".
  • the coordinates of the four-digit year number can be represented by the coordinates of a rectangular area occupied by the four-digit year number in the coin image.
  • the coordinates of the rectangular area can be represented by the coordinates of the two end points of the preset diagonal of the rectangular area.
  • FIG. 2 & FIG. 2 for a schematic diagram of a coin image provided by an embodiment of the present invention.
  • the rectangular area occupied by the four digits "1", “9", “8", and “7" on the coin in the coin image is Can pass through the rectangular area
  • the coordinates of each rectangular region may be represented by the coordinates of the end points of the lower left corner of each rectangular region and the coordinates of the endpoints of the upper right corner. For example, if the coordinates of the rectangular area & the lower left end point are (X)
  • the coordinates of the rectangular area & the upper right corner are (X , 2 ), the shell wrist area &
  • the terminal device invoking the pre-trained coin year model to process the target coin image may be: the terminal device inputs the target coin image to be identified into the coin year recognition model, and acquires the coin year recognition model. The year digital information of the coin in the output target coin image.
  • [0039] 813 Determine a year of the coin in the target coin image according to the year digital information output by the coin year identification model.
  • the terminal device After acquiring the year number information of the coin in the target coin image output by the coin year identification model, the terminal device determines the year of the coin in the target coin image according to the year number information of the coin in the target coin image. ⁇ 0 2019/127075 ⁇ (:17 ⁇ 2017/118891
  • 313 may include the following steps:
  • the terminal device may sort the four-digit year number according to the coordinates of the four-digit year number on the coin in the target coin image and the preset sorting strategy, thereby obtaining the year of the coin.
  • the four-digit year number on the coin is presented along the edge of the coin.
  • the arc shape is sequentially arranged as an example, and the specific process of sorting the four-digit year numbers according to the preset sorting strategy by the terminal device is described in detail.
  • the four-digit year number is sorted according to the coordinates of the four-digit year number on the coin in the target coin image and the preset sorting strategy, to obtain the year of the coin, specifically Includes the following steps:
  • the four digits are located in the lower half of the circular display area, the four digits are sorted according to a first preset order to obtain a year of the coin;
  • the four digits are located in the upper half of the circular display area, the four digits are sorted according to a second preset order to obtain the year of the coin;
  • the four digits are located in the right half of the circular display area, the four digits are sorted according to a third preset order to obtain the year of the coin;
  • the four digits are located in the left half of the circular display area, the four digits are sorted according to a fourth preset order to obtain the year of the coin.
  • the terminal device processes the target coin image by the background difference method to extract the coin from the target coin image. Round display area.
  • the terminal device determines based on the original coordinate system corresponding to the target coin image ⁇ 0 2019/127075 ⁇ (:17 ⁇ 2017/118891 The coordinates of the center point of the circular display area.
  • the terminal device can call The ⁇ ⁇ 1 ⁇ 1 ]'(3 ⁇ 4)8 function in the application is used to find the coordinates of the center point of the circular display area.
  • the terminal device Since the coordinates of the four-digit year number output by the coin year recognition model are represented by the coordinates of the two end points of the preset diagonal of the rectangular area occupied by the four-digit year number, the terminal device identifies the coin year The coordinates of the four-digit year number output by the model are converted, and the coordinates of the center point of the rectangular area occupied by the four-digit year number are obtained, and the coordinates of the center point of the rectangular area occupied by the four-digit year number and the circular display area are The center point coordinates are compared to determine the position of the four-digit year number in the circular display area.
  • the first preset order, the second preset order, the third preset order, and the fourth preset order need to be referred to according to the coin year identification model when determining the coordinates of the digital year on the coin.
  • the coordinate system is determined. Assume that the coordinates of the year number output by the coin year recognition model are based on the origin of the upper left corner of the coin image, the vertical side where the coordinate origin is located as the positive direction of the X axis, and the horizontal side where the coordinate origin is located as the positive direction of the X axis. Coordinate system, Bay 1
  • the terminal device detects that the X-axis coordinates corresponding to the center points of the four rectangular regions occupied by the four-digit year numbers are greater than the ⁇ -axis coordinates corresponding to the center point of the circular display region, the terminal device recognizes The four-digit year numbers are located in the lower half of the circular display area. At this time, the terminal device sorts the four-digit year numbers in the order of the ⁇ -axis coordinates corresponding to the center point of the rectangular area from the smallest to the largest, and obtains the year of the coin. In Fig. 215, the year of the coin obtained after sorting is "1987".
  • the terminal device detects that the X-axis coordinates corresponding to the center points of the four rectangular regions occupied by the four-digit year number are smaller than the ⁇ -axis coordinates corresponding to the center point of the circular display region, the terminal device recognizes that The four-digit year numbers are located in the upper half of the circular display area. At this time, the terminal device sorts the four-digit year numbers in descending order of the ⁇ -axis coordinates corresponding to the center point of the rectangular area to obtain the year of the coin. The year of the coin obtained after sorting the figure is "1987".
  • the terminal device detects that the X-axis coordinate corresponding to the center point of the four rectangular regions occupied by the four-digit year number is greater than the ⁇ -axis coordinate corresponding to the center point of the circular display region, Then, the four-digit year numbers are located in the right half of the circular display area, and the terminal device sorts the four-digit year numbers in descending order of the ⁇ -axis coordinates corresponding to the center point of the rectangular area to obtain the coin.
  • Year, in Figure 2 (1, the year of the coin obtained after sorting is "1987". ⁇ 0 2019/127075 ⁇ (:17 ⁇ 2017/118891
  • the terminal device detects that the X-axis coordinate corresponding to the center point of the four rectangular regions occupied by the four-digit year number is smaller than the ⁇ -axis coordinate corresponding to the center point of the circular display region, the terminal device recognizes that The four-digit year numbers are located in the left half of the circular display area. At this time, the terminal device sorts the four-digit year numbers according to the ⁇ axis coordinate corresponding to the center point of the rectangular area from small to large, and obtains the year of the coin. The year of the coin obtained after sorting is "1987".
  • the method for identifying the coin year obtained by the embodiment obtains the target coin image to be identified; the target coin image is input to the trained coin year recognition model; The year number information of the recognition model output determines the year of the coin in the target coin image. Since the year of the coin to be recognized is recognized by the pre-trained coin year recognition model, it is not necessary to manually classify the coin according to the year of the coin, which not only improves the efficiency of coin recovery but also saves labor costs.
  • FIG. 3 is a flow chart of an implementation of a method for recognizing a coin year according to another embodiment of the present invention.
  • the execution subject of the method for recognizing the coin year in this embodiment is a terminal device.
  • the terminal device may be a mobile terminal device such as a mobile phone or a tablet computer, or may be another terminal device, and is not limited herein.
  • the embodiment may further include 0 before 311.
  • the coin year recognition model needs to be trained before the pre-trained coin year recognition model is called to identify the year of the coin.
  • the training sample set includes a plurality of sets of sample data, and each set of sample data is composed of a coin image and year digital information of coins in the coin image.
  • the year number information for the coin includes the name and seat of the four digits on the coin.
  • 301 can be implemented by using 3011 ⁇ 3015 as shown in FIG. 4, as follows:
  • a large number of coin images for training the model may be collected by the camera device, and the collected coin images for training the model are uniformly stored in the terminal device. ⁇ 0 2019/127075 ⁇ (:17 ⁇ 2017/118891 First image folder.
  • the terminal device receives the model training instruction, it acquires the coin image for training the model from the first image folder.
  • all the coin images collected by the imaging device are the same size.
  • all coin images acquired by the camera are of MxN pixels.
  • M and N respectively represent the number of pixels included in each row and column of the image, and M and N are positive integers, and M and N can be set according to actual needs, and are not limited herein.
  • all the coin images in the first image folder may be renamed.
  • the terminal device can be preset The algorithm renames all the coin images in the first image folder, and sets the name of the coin image uniformly. Where X is any number from 0 to 9, for example, the name of the renamed coin image may be 000001.jpg, 000002.jpg, 000003.jpg, and the like.
  • the terminal device can store all the coin images after the rename in the second image folder.
  • the rectangular area is used to represent a display area occupied by a year number on the coin.
  • the terminal device After the terminal device renames all the coin images for training the model, the name and coordinates of the four-digit year number on the coin in each coin image are determined.
  • the user in determining the coordinates of the four digits of the number on the coin in each coin image, the user is required to assist in marking the four digits of the number on the coin in each coin image.
  • the user can frame the four-digit year number on the coin in each coin image through a rectangular frame, and then select four rectangular regions.
  • the four rectangular areas selected by the user in the coin image are used to respectively represent the display area occupied by the four-digit year number on the coin in the coin image.
  • 3012 may specifically include the following steps:
  • the terminal device acquires four rectangular areas selected by the user on the coins in each coin image, and Determining the coordinates of the two end points of the preset diagonal of each rectangular area according to the preset coordinate calibration strategy, and determining the coordinates of the two end points of the preset diagonal of each rectangular area as the coordinates of the rectangular area .
  • the preset coordinate calibration strategy is used to represent coordinate calibration based on a preset coordinate system.
  • the preset coordinate system can be determined according to actual needs, and there is no restriction here.
  • the preset coordinate system may be the coordinate origin of the upper left corner of the coin image, the vertical direction where the coordinate origin is located as the positive direction of the X axis, and the horizontal edge where the coordinate origin is located as the coordinate system established by the positive direction of the axis.
  • the terminal device determines coordinates of two end points of a preset diagonal of each rectangular area in each coin image based on a preset coordinate system.
  • the preset diagonal line can be set according to actual requirements, and is not limited herein.
  • the preset diagonal line may be a diagonal line connecting the lower left corner end point and the upper right corner end point of the rectangular area, and the preset is The two endpoints of the diagonal are the lower left endpoint and the upper right endpoint, respectively.
  • the terminal device after determining the coordinates of the consecutive endpoints of the preset diagonal of each rectangular area in each coin image, the terminal device recognizes the coordinates of the consecutive endpoints of each rectangular area as The coordinates of the corresponding rectangular area.
  • [0081] 8013 Determine coordinates of four rectangular regions in the coin image as coordinates of four-digit year numbers on coins in the coin image, respectively.
  • the coordinates of the four rectangular regions are respectively determined as the four-digit year numbers on the coins in the coin image.
  • coordinate of That is, the coordinates of the year number in the rectangular area are expressed by the coordinates of the rectangular area.
  • the terminal device while determining the coordinates of the four-digit year number on the coin in each coin image, the terminal device also determines the name of the four-digit year number on the coin in each coin image.
  • the name of the four-digit year number on the coin in each coin image may be input by the user. For example, the user inputs the name of the number while selecting each year number through the rectangular frame. The terminal device obtains the name of each year number entered by the user.
  • each coin image and the four-digit year on the coin in each coin image After the terminal device acquires the name and coordinates of the four-digit year number on the coin in each coin image, each coin image and the four-digit year on the coin in each coin image The names and coordinates of the numbers are stored in association.
  • the terminal device may first name the name of each coin image and four digits on the coin in each coin image.
  • the name and coordinates of the year number are stored in a table form in the first text file.
  • Table 1 shows part of the first text file, the part is named 001352.
  • the terminal device associates the name of each coin image, the name and coordinates of the four digits on the coin in each coin image in a table form in the first text file
  • the text information with the same name of the coin image in the first text file is further stored in the same Extensible Markup Language (XML) file, and the names of the coin images in the first text file are different.
  • Text information is stored in different XML files. in this way , you can get multiple XML files.
  • Each XML file stores the name and coordinates of a four-digit year number on a coin in a coin image.
  • the resulting multiple XML files can be: 000001. xml 000002.xml s 000003 Pass.
  • the terminal device After obtaining the XML file corresponding to each coin image, the terminal device associates the coin image with its corresponding XM L file to obtain a plurality of sets of sample data, and the plurality of sets of sample data constitute a sample set for training the model. .
  • the terminal device can randomly extract 50% of the sample data from the sample set as the training sample data, and the training sample data constitutes the training sample set.
  • the RCNN model is trained to determine the trained Faster RCNN model as a coin year identification model.
  • the terminal device uses the training sample set to train the pre-built region-based convolutional neural network (Faster RCNN) model.
  • Faster RCNN pre-built region-based convolutional neural network
  • the terminal device uses each coin image as an input of a Faster RCNN model, and the name and coordinates of the four digits on the coin in each coin image are output of the Faster RCNN model to the pre-built Faster RCNN model. Train. After training the Faster RCNN model, the terminal device determines the trained Faster RCNN model as the coin year recognition model.
  • coin year identification model is used to identify the year of the coin.
  • FIG. 5 is a schematic structural diagram of a Faster RCNN model according to an embodiment of the present invention.
  • the Faster RCNN model specifically includes a feature extraction network and a region extraction network (Region proposal).
  • RPN Random Network Network
  • target identification network The input end of the RPN and the input end of the target identification network are both connected to the output of the feature extraction network, and the input end of the target identification network is also connected to the output of the PRN.
  • the feature extraction network is configured to perform a convolution operation on the pixel values of all the pixel points in the input coin image by the convolution kernel to obtain a feature map, and output the feature map to the RPN and the target recognition network.
  • the dimensions of the feature map are much smaller than the dimensions of the coin image.
  • the RPN is configured to determine, according to the feature map, a display area in the coin image that may be a year number, and The display area where the year number is located is output as a candidate area to the target recognition network.
  • the target recognition network is configured to select a target region corresponding to the four-digit year number on the coin in the coin image from the candidate region according to the feature map output by the feature extraction network, and identify the year number in the region where the year number is located. And output the name of the four-digit year number and the coordinates of the corresponding target area.
  • the feature extraction network may adopt a VGG16 network.
  • the target recognition network may specifically be a Fast Region-based Convolutional Neural Network (Fast RCNN).
  • the target area is a rectangular area
  • the coordinates of the target area may be represented by the coordinates of the two end points of the preset diagonal of the target area, for example, the coordinates of the end point of the lower left corner of the target area may be The coordinate representation of the endpoint in the upper right corner.
  • the RPN is used to gradually slide on the feature map by using a preset sliding window.
  • the sliding step size can be set according to actual needs, and there is no limit here.
  • Each position where the preset sliding window slides on the feature map maps k original image areas of different sizes or areas. For example, assuming that the preset sliding window corresponds to 300 sliding positions on the feature map, the entire feature map corresponds to 300k original image regions.
  • the coordinates corresponding to each of the original image regions are known. Since the original image region is a rectangular region, the coordinates of the original image region can be represented by the coordinates of the endpoint positions at which the four corners are located.
  • the RPN calculates the probability value that the original image area is an unrelated area or the area where the year number is located, that is, each original image area corresponds to two probability values. Based on the two probability values corresponding to each original image region, the RPN selects n original image regions with a high probability of the region where the year number is located as the candidate region from all the original image regions, and outputs the coordinates of the candidate region to the target recognition.
  • the internet The internet.
  • the target recognition network is configured to calculate a probability value of each of the numbers 0 to 9 for each candidate region, that is, each candidate region corresponds to 10 probability values, which are any numbers of numbers 0-9. Probability value. That is to say, if the number of candidate regions is n, then 10n probability values are finally obtained, and each of the numbers 0-9 corresponds to n probability values. Considering that the year number on the coin is four digits, the target recognition network selects four probability values with higher probability values from the n probability values corresponding to each number, that is, each number in 0 ⁇ 9 is separately screened out. Four candidate regions, and finally 40 candidate regions were selected.
  • the target recognition network further determines four candidate regions with the highest probability value from the selected 40 candidate regions, and identifies the four candidate regions with the highest probability value as The four-digit year number on the coin corresponds to the target area. Finally, the target recognition network outputs the coordinates of the four target areas and the names of the corresponding year numbers.
  • S02 can be implemented by S021 ⁇ S025 as shown in FIG. 6, which is as follows:
  • S021 Initialize parameter values of the feature extraction network according to parameter values of the feature extraction model obtained by pre-training, randomly initialize parameter values of the region extraction network, and adopt the feature extraction after initialization a network and the region extraction network extract candidate regions from each of the coin images
  • the terminal acquires the feature extraction model obtained by the pre-training, and initializes the parameters of the feature extraction network according to the parameter values of the feature extraction model obtained by the pre-training.
  • the feature extraction model is a feature extraction model based on ImageNet training.
  • ImageNet is the largest image database used for image recognition in the world.
  • the feature extraction network may adopt a VGG16 network. Please refer to Table 2 together. Table 2 shows the parameters of a VGG16 network provided by this embodiment.
  • the VGG16 network includes 13 convolution layers and 4 pooling layers. The size and number of convolution kernels in each layer are shown in Table 2.
  • the terminal device may obtain the parameter value of the pre-trained 0016 model from the network, and initialize the parameter value of 0016 by using the parameter value of the pre-trained 0016 model.
  • the parameter values of 0016 include, but are not limited to, the number of convolution kernels, the size of the convolution kernel, and the values corresponding to the respective elements in the convolution kernel.
  • the terminal device randomly initializes the parameter values of the RPN.
  • Initializing the parameter value of the RPN means initializing the value of each element in each convolution kernel included in the RPN.
  • the terminal device may extract the candidate area from each coin image by using the initialized feature extraction network and the RPN, and The coordinates of the extracted candidate regions are output to the target recognition network.
  • S022 Perform the first training on the target recognition network according to the candidate region extracted from each of the coin images and the year digital information of the coins in each of the coin images.
  • the terminal device performs the first training on the target recognition network based on the candidate region extracted from each coin image and the year digital information on the coin in each coin image.
  • the terminal device uses the coordinates of the candidate region extracted from each coin image as the input of the target recognition network, and uses the year digital information on the coin in each coin image as the output of the target recognition network, The first training is performed on the target recognition network.
  • an initial parameter value of the target recognition network is obtained.
  • the initial parameter value of the target recognition network refers to the initial parameter value of each element in the convolution kernel in the target recognition network.
  • S023 Update parameter values of the area extraction network according to initial parameter values of the target identification network after the first training, and adopt the initialized feature extraction network and the updated area.
  • the extraction network again extracts candidate regions from each of the coin images.
  • the terminal device updates the parameter value of the RPN according to the initial parameter value of the target network after the first training, and extracts the candidate region from each coin image again by using the updated region extraction network, and extracts the candidate region again.
  • the coordinates of the candidate regions are output to the target recognition network.
  • S024 Perform a second training on the target recognition network according to the candidate region extracted from each of the coin images and the year digital information of the coins in each of the coin images.
  • the terminal device performs the second training on the target recognition network after the first training based on the candidate region extracted from each coin image and the year digital information on the coin in each coin image. Specifically, the terminal device will again use the coordinates of the candidate region extracted from each coin image as the input of the target recognition network, and use the year digital information on the coin in each coin image as the output of the target recognition network to target the target. Identify the network for a second training session.
  • the final parameter value of the target recognition network refers to the final parameter of each element in the convolution kernel in the target recognition network. ⁇ 0 2019/127075 ⁇ (:17 ⁇ 2017/118891 value.
  • the 8 ⁇ 3 ⁇ RCNN model composed of the updated area extraction network of the terminal device and the target recognition network after the second training is determined as a coin year recognition model, and the coin year identification model is stored.
  • the terminal calls the coin year identification model.
  • the Fa S t er RCNN model detects and locates the target faster and has higher accuracy
  • the coin year model obtained by training the Fa S er RCNN model is used to identify the year of the coin, which not only improves the coin.
  • the efficiency of the year identification, and the accuracy of the coin year identification since the positions and postures of the coins in the plurality of coin images included in the training sample set are random, the coin year recognition model trained based on the training sample set can be effective for the years of the coins in the coin images of different specifications. Identification.
  • FIG. 7 is a schematic structural diagram of a terminal device according to an embodiment of the present invention.
  • the terminal device 700 can be a mobile terminal device such as a smart phone or a tablet computer, and can also be other terminal devices, and is not limited herein.
  • Each unit included in the terminal device 700 of this embodiment is used to perform the steps in the embodiment corresponding to FIG. 1.
  • the terminal device 700 of this embodiment includes a first obtaining unit 71, a model calling unit 72, and a year determining unit 73.
  • the first acquisition unit 71 is configured to acquire a target coin image to be identified.
  • the model invoking unit 72 is configured to input the target coin image to the trained coin year recognition model.
  • the year determining unit 73 is configured to determine a year of the coin in the target coin image according to the year number information output by the coin year identification model; wherein the year number information is used to represent the year of the coin ⁇ 0 2019/127075 ⁇ (:17 ⁇ 2017/118891
  • the year digital information includes a name and a coordinate of a four-digit year number on the coin in the target coin image; the year determining unit 73 is specifically configured to: The coordinates of the four-digit year number on the coin in the target coin image and the preset sorting strategy sort the four-digit year number to obtain the year of the coin.
  • the year determining unit 73 is specifically configured to:
  • the four digits are located in the lower half of the circular display area, the four digits are sorted according to the first preset order to obtain the year of the coin;
  • the four digits are located in the upper half of the circular display area, the four digits are sorted according to a second preset order to obtain a year of the coin;
  • the four digits are located in the right half of the circular display area, the four digits are sorted according to a third preset order to obtain the year of the coin;
  • the four digits are located in the left half of the circular display area, the four digits are sorted according to a fourth preset order to obtain the year of the coin.
  • the terminal device obtained by the embodiment obtains the target coin image to be identified; the target coin image is input to the trained coin year recognition model; and the model is output according to the coin year identification model.
  • the year number information determines the year of the coin in the target coin image. Since the year of the coin to be identified is recognized by the pre-trained coin year recognition model, it is not necessary to manually classify the coin according to the year of the coin, which not only improves the efficiency of coin recovery but also saves labor costs.
  • FIG. 8 is a schematic structural diagram of a terminal device according to another embodiment of the present invention.
  • Each unit included in the terminal device 700 of this embodiment is used to perform the steps in the embodiment corresponding to FIG. 3.
  • the terminal device 700 in this embodiment further includes a second acquiring unit 74 and a model, with respect to the embodiment corresponding to FIG. ⁇ 0 2019/127075 ⁇ (:17 ⁇ 2017/118891 Training unit 75.
  • the second obtaining unit 74 is configured to acquire a training sample set; wherein each set of sample data in the training sample set is composed of a coin image and year digital information of coins in the coin image, the year of the coin
  • the digital information includes the name and coordinates of the four digits of the year on the coin.
  • the model training unit 75 is configured to adopt the training sample set pair to construct a pre-constructed region-based fast convolutional neural network RCNN model is trained, will be trained
  • the RCNN model is determined to be a coin year identification model; wherein the coin year identification model is used to identify the year of the coin.
  • the second obtaining unit 74 specifically includes an image obtaining unit 74 1 , a coordinate calibration unit 742 , a digital coordinate determining unit 743 , a digital name determining unit 744 , and a sample set determining unit. 745.
  • the image acquisition unit 741 is for acquiring a coin image for training the model.
  • the coordinate calibration unit 742 is configured to determine coordinates of four rectangular regions selected by the user on the coin in each of the coin images; wherein the rectangular region is used to represent the year number on the coin Occupied display area.
  • the digital coordinate determining unit 743 is for determining the coordinates of the four rectangular regions in the coin image as the coordinates of the four-digit year number on the coin in the coin image, respectively.
  • the number name determining unit 744 is for determining the name of the four-digit year number on the coin in each of the coin images.
  • the sample set determining unit 745 is configured to store the name and the coordinates of the year number on each of the coin images and the coins in each of the coin images to obtain a training sample set.
  • the coordinate calibration unit 742 specifically includes a region obtaining unit and a region coordinate determining unit.
  • the area obtaining unit is configured to acquire four rectangular areas selected by the user on the coins in each of the coin images.
  • the area coordinate determining unit is configured to determine coordinates of two end points of the preset diagonal of each of the rectangular areas according to a preset coordinate calibration strategy, and preset a diagonal of each of the rectangular areas The coordinates of the two endpoints are determined as the coordinates of the rectangular region.
  • the Faster RCNN model includes a feature extraction network ⁇ 0 2019/127075 ⁇ (:17 ⁇ 2017/118891
  • the model training unit 75 specifically includes: a parameter initialization unit 751, a first training unit 752, a parameter update unit 753, a second training unit 754, and a model determination unit 755.
  • the parameter initialization unit 751 is configured to initialize a parameter value of the feature extraction network according to a parameter value of the feature extraction model obtained by pre-training, randomly initialize a parameter value of the region extraction network, and adopt an initialized The feature extraction network and the region extraction network extract candidate regions from each of the coin images.
  • the first training unit 752 is configured to perform the first training on the target recognition network based on the candidate regions extracted from each of the coin images and the year digital information of the coins in each of the coin images.
  • the parameter updating unit 753 is configured to update the parameter value of the area extraction network according to the initial parameter value of the target identification network after the first training, and adopt the initialized feature extraction network and the updated The region extraction network again extracts candidate regions from each of the coin images.
  • the second training unit 754 is configured to perform the second training on the target recognition network according to the candidate region extracted from each of the coin images and the year digital information of the coins in each of the coin images. .
  • the model determining unit 755 is configured to identify the Fa S t er RCNN model composed of the initialized feature extraction network, the updated region extraction network, and the target training network after the second training. Identify the model for the coin year.
  • a terminal device provided by this embodiment is pre-built RCNN model
  • the RCNN model is trained to obtain a coin year recognition model.
  • the target detection and positioning rate is faster and the accuracy is higher. Therefore, the coin year model obtained by training the Fa S t er RCNN model is used to identify the year of the coin, which not only improves the efficiency of the coin year recognition, but also improves the efficiency.
  • the accuracy of the coin year identification since the positions and postures of the coins in the plurality of coin images included in the training sample set are random, the coin year recognition model trained based on the training sample set can be effective for the years of the coins in the coin images of different specifications.
  • Identification. 9 is a schematic diagram of a terminal device according to still another embodiment of the present invention. As shown in FIG.
  • the terminal device 900 of this embodiment includes: a processor 90, a memory 91, and a computer program 92 stored in the memory 91 and operable on the processor 90.
  • the processor 90 when executing the computer program 92, implements the steps in the various method embodiments described above, such as S11 through S13 shown in FIG.
  • the processor 90 executes the computer program 92, the functions of the units/units in the above-described respective terminal device embodiments are implemented, for example, the functions of the units 71 to 73 shown in FIG.
  • the computer program 92 may be partitioned into one or more units/units, which are stored in the memory 91 and executed by the processor 90.
  • the one or more units/units may be a series of computer program instruction segments capable of performing a particular function, the instruction segments being used to describe the execution of the computer program 92 in the terminal device.
  • the computer program 92 can be divided into a first obtaining unit, a model calling unit, and a year determining unit, and the specific functions of each unit are as follows:
  • the first acquisition unit is configured to acquire a target coin image to be identified.
  • the model calling unit is configured to input the target coin image to the trained coin year recognition model.
  • the year determining unit is configured to determine a year of the coin in the target coin image according to the year number information output by the coin year identification model; wherein the year number information is used to represent the year of the coin.
  • the terminal device 900 may be a computing device such as a desktop computer, a notebook, a palmtop computer, and a cloud server.
  • the terminal device 900 can include, but is not limited to, a processor 90, a memory 91.
  • FIG. 9 is merely an example of a terminal device, and does not constitute a limitation on the terminal device 900, and may include more or less components than those illustrated, or combine some components, or different components.
  • the terminal device may further include an input/output device, a network access device, a bus, and the like.
  • the processor 90 may be a central processing unit (CPU), or may be another general-purpose processor, a digital signal processor (DSP), or an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), ready-to-use programmable gate array
  • CPU central processing unit
  • DSP digital signal processor
  • ASIC Application Specific Integrated Circuit
  • the general purpose processor may be a microprocessor or the processor or any conventional processor or the like.
  • the memory 91 may be an internal storage unit of the terminal device, such as a hard disk or a terminal of the terminal device. Save.
  • the memory 91 may also be an external storage device of the terminal device, for example, a plug-in hard disk provided on the terminal device, and an intelligent memory card (Smart Media)
  • the memory 91 may also include both an internal storage unit of the terminal device and an external storage device.
  • the memory 91 is used to store the computer program and other programs and data required by the terminal device.
  • the memory 91 can also be used to temporarily store data that has been output or is about to be output.
  • each functional unit and unit described above is exemplified. In practical applications, the above functions may be assigned differently according to needs.
  • the functional unit and the unit are completed, that is, the internal structure of the terminal device is divided into different functional units or units to complete all or part of the functions described above.
  • Each functional unit and unit in the embodiment may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit, and the integrated unit may be implemented by hardware.
  • Formal implementation can also be implemented in the form of software functional units.
  • the disclosed terminal devices/systems and methods may be implemented in other manners.
  • the terminal device/system embodiment described above is only illustrative.
  • the division of the unit or unit is only a logical function division.
  • there may be another division manner for example, multiple units.
  • components can be combined or can be integrated into another Systems, or some features can be ignored, or not executed.
  • the mutual coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interface, terminal device or unit, and may be in electrical, mechanical or other form.
  • the units described as separate components may or may not be physically separated, and the components displayed as the unit may or may not be physical units, that is, may be located in one place, or may be distributed to multiple networks. On the unit. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of the embodiment.
  • each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.
  • the above integrated unit can be implemented in the form of hardware or in the form of a software functional unit.
  • the integrated unit/unit if implemented in the form of a software functional unit and sold or used as a standalone product, may be stored in a computer readable storage medium. Based on such understanding, the present invention implements all or part of the processes in the foregoing embodiments, and may also be completed by a computer program to instruct related hardware.
  • the computer program may be stored in a computer readable storage medium. The steps of the various method embodiments described above may be implemented when the program is executed by the processor.
  • the computer program includes computer program code, and the computer program code may be in the form of a source code, an object code, an executable file, or some intermediate form.
  • the computer readable medium may include: any entity or terminal device capable of carrying the computer program code, a recording medium, a USB flash drive, a removable hard disk, a magnetic disk, an optical disk, a computer memory, a read only memory (Read-Only Memory, ROM) ), Random Access Memory (RAM), electrical carrier signals, telecommunications signals, and software distribution media.
  • a recording medium a USB flash drive
  • a removable hard disk a magnetic disk
  • an optical disk a computer memory
  • electrical carrier signals telecommunications signals
  • software distribution media may be appropriately increased or decreased according to the requirements of legislation and patent practice in a jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, computer readable media It does not include electrical carrier signals and telecommunication signals.

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Abstract

La présente invention peut s'appliquer au domaine technique de la reconnaissance d'image et concerne un procédé pour identifier l'année d'une pièce de monnaie, un dispositif de terminal et un support d'enregistrement lisible par ordinateur. Le procédé consiste à : obtenir une image de pièce de monnaie cible à identifier; entrer l'image de pièce de monnaie cible dans un modèle de reconnaissance d'année de pièce de monnaie enregistré; et déterminer l'année d'une pièce de monnaie dans l'image de pièce de monnaie cible selon des informations de numéro d'année délivrées par le modèle de reconnaissance d'année de pièce de monnaie. Étant donné que l'année d'une pièce de monnaie à identifier peut être identifiée à l'aide d'un modèle de reconnaissance d'année de pièce de monnaie pré-enregistré, la classification manuelle des pièces de monnaie à partir des années des pièces de monnaie est évitée, de telle sorte que l'efficacité de récupération des pièces de monnaie soit améliorée et que les coûts manuels soient économisés.
PCT/CN2017/118891 2017-12-27 2017-12-27 Procédé d'identification de l'année d'une pièce de monnaie, dispositif de terminal et support d'enregistrement lisible par ordinateur WO2019127075A1 (fr)

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