WO2020107951A1 - Procédé et appareil de facturation de produits à base d'images, support et dispositif électronique - Google Patents

Procédé et appareil de facturation de produits à base d'images, support et dispositif électronique Download PDF

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WO2020107951A1
WO2020107951A1 PCT/CN2019/101113 CN2019101113W WO2020107951A1 WO 2020107951 A1 WO2020107951 A1 WO 2020107951A1 CN 2019101113 W CN2019101113 W CN 2019101113W WO 2020107951 A1 WO2020107951 A1 WO 2020107951A1
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commodity
image
settled
feature vector
present disclosure
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PCT/CN2019/101113
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English (en)
Chinese (zh)
Inventor
梅涛
吴楠
赵何
刘武
徐迎庆
张雷
周伯文
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北京京东尚科信息技术有限公司
北京京东世纪贸易有限公司
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Publication of WO2020107951A1 publication Critical patent/WO2020107951A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/10—Terrestrial scenes
    • 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
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0283—Price estimation or determination

Definitions

  • the present disclosure relates to the technical field of commodity identification and settlement, in particular, to an image-based commodity settlement method and an image-based commodity settlement device.
  • the current commodity settlement methods mainly include the following methods: commodity identification technology based on barcode recognition, commodity identification technology based on radio frequency identification and image recognition technology.
  • These technologies have the following shortcomings: in the retail settlement scenario based on the barcode recognition method, complex operations of the settlement personnel are required, first of all, it is necessary to find the position of the barcode on the product packaging, handheld barcode recognition equipment, device scanner and barcode alignment scanning, etc., and It needs to scan multiple products one by one, which consumes a lot of manpower and prolongs the settlement time; because the identification method based on radio frequency needs to install RFID electronic tags for the products in advance, the high deployment and maintenance costs of RFID electronic tags are very high, and it is difficult to be widely used in retail Scene; image-based recognition method.
  • the general deep neural network model still has difficulty in achieving high accuracy.
  • the use of a deeper network structure will increase computing time on the one hand, and on the other hand Model training is difficult.
  • the purpose of the embodiments of the present disclosure is to provide an image-based commodity settlement method and an image-based commodity settlement device, and to overcome, at least to a certain extent, the long settlement time and complicated settlement operation due to the limitations and defects of related technologies.
  • One or more problems such as high cost of electronic tags and low practicality of existing image recognition technologies.
  • an image-based commodity settlement method including:
  • Detect the acquired image to be settled to determine the area image of the commodity to be settled in the image to be settled;
  • the price information corresponding to the feature vector of the commodity to be settled is identified in a preset feature vector recognition database
  • the identified price information is summed to output the total settlement price of the image to be settled.
  • the above detection of the acquired image to be settled to determine the area image of the commodity to be settled in the image to be settled includes:
  • Commodity detection is performed on the normalized image to be settled through the pre-trained product detection model to determine the coordinate information of the bounding box that surrounds the commodity to be settled in the normalized image to be settled, and to intercept the image in the coordinate information area of the bounding box ;
  • An area image of each commodity to be settled is cut out from the image in the bounding box coordinate information area.
  • the above pre-trained commodity detection model includes:
  • the commodity detection model is trained by a stochastic gradient descent algorithm, and the pre-trained commodity detection model is output.
  • the above-mentioned low-dimensional mapping of the area image of the commodity to be settled to obtain a feature vector corresponding to the commodity to be settled includes:
  • the region image of the commodity to be settled is mapped to a low-dimensional feature vector.
  • the pre-trained feature embedding neural network model includes:
  • the normalized image and product category of the training image are used as the training samples of the feature embedded neural network model
  • the above feature embedded neural network model is trained by a stochastic gradient descent algorithm, and the above pre-trained feature embedded neural network model is output.
  • the price information corresponding to the feature vector of the commodity to be settled in the preset feature vector recognition database based on the feature vector of the commodity to be settled includes:
  • the above price information is determined as the price information of the above-mentioned commodity to be settled.
  • the feature vectors of the preset feature vector recognition database that have been included in the database include:
  • the forward-propagation operation is performed on the image of the warehoused commodity in the preset standard format to obtain a low-dimensional feature vector of the image of the warehoused commodity;
  • a feature vector recognition database is established based on the low-dimensional feature vectors of the images of the commodities that have been put in storage.
  • an image-based commodity settlement device including:
  • a determining module configured to detect the acquired image to be settled, and determine the area image of the commodity to be settled in the image to be settled;
  • the feature vector acquisition module is used to perform a low-dimensional mapping on the area image of the commodity to be settled to obtain a feature vector corresponding to the commodity to be settled;
  • An identification module used to identify the price information corresponding to the feature vector of the commodity to be settled in the preset feature vector recognition database based on the feature vector of the commodity to be settled;
  • the output module is used to sum the identified price information and output the total settlement price of the image to be settled.
  • a computer-readable medium on which a computer program is stored, and when the program is executed by a processor, the image-based commodity settlement method as described in the first aspect of the above embodiments is implemented .
  • an electronic device including: one or more processors; a storage device for storing one or more programs, when the one or more programs are When executed by each processor, the above one or more processors implement the image-based commodity settlement method as described in the first aspect of the above embodiment.
  • Embodiments of the present disclosure provide an image-based commodity settlement method, device, medium, and electronic equipment, including: detecting the acquired image to be settled, and determining an area image of the commodity to be settled in the image to be settled; Perform a low-dimensional mapping on the area image of the commodity to be settled to obtain the feature vector corresponding to the commodity to be settled; based on the feature vector of the commodity to be settled, identify the feature vector of the commodity to be settled in the preset feature vector recognition database Corresponding price information; sum the identified price information and output the total settlement price of the image to be settled.
  • the technical solution of the embodiment of the present disclosure does not require the operation of a clearer to identify the goods placed by the user on the clearing desk, and realizes automatic, rapid and accurate identification of the type and quantity of goods to calculate the settlement amount and reduce costs .
  • FIG. 1 schematically shows a flowchart of an image-based commodity settlement method according to an embodiment of the present disclosure.
  • FIG. 2 schematically shows a flow chart of warehousing and scanning through a self-checkout counter according to an embodiment of the present disclosure
  • FIG. 3 schematically shows a flowchart of determining an area image of each commodity to be settled according to an embodiment of the present disclosure
  • FIG. 4 schematically shows a training flowchart of a commodity detection model according to an embodiment of the present disclosure
  • FIG. 5 schematically shows a training schematic diagram of a feature embedding neural network model according to an embodiment of the present disclosure
  • FIG. 6 schematically shows a schematic diagram of establishing a feature vector recognition database according to an embodiment of the present disclosure
  • FIG. 7 schematically shows a block diagram of an image-based commodity settlement system according to an embodiment of the present disclosure
  • FIG. 8 schematically shows a block diagram of an image-based commodity settlement device according to an embodiment of the present disclosure
  • FIG. 9 shows a schematic structural diagram of a computer system suitable for implementing an electronic device according to an embodiment of the present disclosure.
  • Example embodiments will now be described more fully with reference to the drawings.
  • the example embodiments can be implemented in various forms, and should not be construed as being limited to the examples set forth herein; on the contrary, providing these embodiments makes the present disclosure more comprehensive and complete, and fully conveys the idea of the example embodiments For those skilled in the art.
  • FIG. 1 schematically shows a flowchart of an image-based commodity settlement method according to an embodiment of the present disclosure.
  • an image-based commodity settlement method includes the following steps:
  • Step S110 Detect the acquired image to be settled to determine the area image of the commodity to be settled in the image to be settled;
  • Step S120 Perform a low-dimensional mapping on the area image of the commodity to be settled to obtain a feature vector corresponding to the commodity to be settled;
  • Step S130 based on the feature vector of the commodity to be settled, the price information corresponding to the feature vector of the commodity to be settled is identified in a preset feature vector recognition database;
  • step S140 the identified price information is summed to output the total settlement price of the image to be settled.
  • the technical solution of the embodiment shown in FIG. 1 does not require the operation of a clearer in the retail settlement scenario, and recognizes the goods placed on the checkout counter by the user, which realizes automatic, fast and accurate identification of the type and quantity of goods. Related costs.
  • step S110 the acquired image to be settled is detected to determine the area image of the commodity to be settled in the image to be settled.
  • the method further includes: acquiring images to be settled through a self-settlement self-settlement device pre-erected.
  • the self-settlement self-settlement device pre-erected mainly includes: a storage table (a storage basket, a surrounding table, etc.) ), lighting equipment (LED lights, etc.), and video recording equipment (cameras, cameras, mobile phones, etc.), through which the video recording equipment can also provide corresponding training images for intelligent image recognition algorithms and provide product images for product storage.
  • the erection of the above-mentioned recording device can be adjusted according to actual needs, and the corresponding parameters of the recording device are selected and erected according to the reasons for identifying the required image quality, video quality, and light .
  • the appearance information of the product can be photographed by using two color cameras, a top view and a side view. For each camera, the product collects an image every X degrees of rotation, and Y images are collected in total.
  • step S110 is to perform validity detection on the acquired image to be settled and identify whether the product is complete. Therefore, during the process of setting up the recording device before step S110, multiple Take pictures of animals from an angle to collect information about the product, such as the bar code, price, and name of the product, to provide a data basis for later accurate identification of the product.
  • the recording device provided in the self-checkout station is an infrastructure for providing images, and the video recorded by the recording device can be saved according to different storage methods, and the captured video can be Stored in the cloud, or stored in local external device storage.
  • FIG. 2 schematically shows a flow chart of goods entering and scanning through the self-checkout station according to an embodiment of the present disclosure.
  • the process of determining the area image of each commodity to be settled includes the following steps:
  • Step S210 acquiring a product
  • Step S220 input the barcode, price, name and other information of the commodity by scanning
  • Step S230 photographing the product
  • step S240 it is determined whether the number of captured images is the preset Y sheets. If not, return to step S230 to take another shot; if yes, perform the subsequent steps;
  • step S250 the above-mentioned captured images are stored in the commodity database and the training picture database, respectively.
  • the above step S110 specifically includes: normalizing the image to be settled, determining the invariant in the image to be settled, and converting it to a normalized Settlement image; perform commodity detection on the normalized image to be settled through a pre-trained product detection model, determine the coordinate information of the bounding box surrounding the product to be settled in the normalized image to be settled, and intercept the area of the coordinate information The image in the area; the area image of each commodity to be settled is cut out from the image in the coordinate information area of the bounding box.
  • FIG. 3 schematically shows a flowchart of determining an area image of each commodity to be settled according to an embodiment of the present disclosure.
  • the process of determining the area image of each product to be settled includes the following steps:
  • Step S310 the user places the commodity to be settled on the self-checkout counter, and the camera automatically shoots all the images of the commodity to be settled;
  • Step S320 the normalized image to be settled is used as the input of the pre-trained commodity detection model
  • Step S330 initialize the pre-trained commodity detection model with the trained detection model
  • Step S340 using the pre-trained commodity detection model to detect the commodity to be settled in the image to be settled, to obtain the bounding box coordinates of the commodity to be settled in the image to be settled;
  • the bounding box coordinates include at least the upper left corner coordinates (x1, y1) and the lower right corner coordinates (x2, y2) of the bounding box.
  • step S350 the area images of the commodities to be settled are intercepted according to the coordinates of the commodity bounding box.
  • the above determination of the area images of each commodity to be settled is an online processing process.
  • the above pre-trained commodity detection model may be obtained in the following manner:
  • the commodity detection model is trained by a stochastic gradient descent algorithm, and the pre-trained commodity detection model is output.
  • FIG. 4 schematically shows a training flowchart of a commodity detection model according to an embodiment of the present disclosure.
  • the training process of a commodity detection model includes the following steps:
  • Step S410 Perform pre-processing such as normalization and color balance on the batch of in-stock commodity images
  • Step S420 using the training image and the corresponding product bounding box coordinates and product category as the training samples of the lightweight product detection model;
  • Step S3430 use the pre-trained model to initialize the commodity detection model
  • Step S440 Use the sum of the bounding box regression loss function and the commodity classification loss function as the total loss function of the commodity detection model, and use the stochastic gradient descent algorithm to train the commodity detection model;
  • Step S450 save the commodity detection model.
  • the training process of the aforementioned commodity detection model is an offline process.
  • step S120 perform a low-dimensional mapping on the area image of the commodity to be settled to obtain a feature vector corresponding to the commodity to be settled.
  • the low-dimensional mapping of the area image of the commodity to be settled to obtain the feature vector corresponding to the commodity to be settled includes: inputting the area image of the commodity to be settled into a pre-trained feature embedding After the neural network model, the area image of the commodity to be settled is mapped to a low-dimensional feature vector.
  • the pre-trained feature embedding neural network model is trained in the following manner: the training image is subjected to the normalized image and the commodity category as the feature embedding neural network model training sample ;
  • the preset normalized exponential loss function is determined as the loss function of the above feature embedded neural network model; based on the training sample and the above loss function, the above feature embedded neural network model is trained by a random gradient descent algorithm, and the above pre The trained features are embedded in the neural network model.
  • FIG. 5 schematically shows a training schematic diagram of a feature embedding neural network model according to an embodiment of the present disclosure.
  • the training process of feature embedding neural network model includes the following steps:
  • Step S510 Perform normalization, color balance and other preprocessing on batches of training commodity images
  • Step S520 embedding the training images and commodity categories as features in the training samples of the neural network model
  • Step S530 using the pre-trained model to initialize the feature embedding neural network model
  • Step S540 Use the normalized exponential loss function to calculate the network loss of the feature embedded neural network model, and use the stochastic gradient descent method to train the feature embedded neural network model;
  • Step S550 save the feature embedded neural network model.
  • the establishment of the feature vector recognition database described above is an offline process.
  • step S130 based on the feature vector of the commodity to be settled, the price information corresponding to the feature vector of the commodity to be settled is identified in a preset feature vector recognition database.
  • the price information corresponding to the feature vector of the commodity to be settled identified in the preset feature vector recognition database specifically includes: calculating the feature vector of the commodity to be settled and the preset feature Recognize the similarity between the feature vectors of the warehousing products in the database to determine the feature vectors of the warehousing products with the highest similarity; According to the product category corresponding to the feature vectors of the warehousing products with the highest similarity, Find out the corresponding price information; determine the above price information as the price information of the goods to be settled.
  • the feature vector of the preset feature vector recognition database in the warehoused commodity includes: normalizing the image of the warehoused commodity to obtain a format conforming to the preset standard The image of the stocked goods; the pre-trained feature embedding neural network model performs the forward propagation operation on the image of the stocked goods in the above standard format to obtain the low-dimensional features of the image of the stocked goods Vector; based on the low-dimensional feature vectors of the images of the commodities that have been included in the database, a feature vector recognition database is established.
  • FIG. 6 schematically shows a schematic diagram of establishing a feature vector recognition database according to an embodiment of the present disclosure.
  • establishing a feature vector recognition database includes the following steps:
  • Step S610 Perform normalization and other processing on the acquired merchandise pictures that have been stored in the warehouse;
  • Step S620 using the trained recognition model to initialize the feature embedding neural network model
  • Step S630 using the feature embedding neural network model to perform a forward propagation operation on the stored commodity pictures to extract the feature vector of the stored commodity pictures;
  • step S640 a feature vector recognition database is established for the feature vectors of all the stored commodity pictures
  • Step S650 saving and establishing a feature vector recognition database.
  • the establishment of the feature vector recognition database described above is an offline process.
  • step S140 the identified price information is summed to output the total settlement price of the image to be settled.
  • the total settlement amount of the image to be identified can be calculated, which realizes automatic, fast and accurate identification of the product category and quantity and Calculate the total settlement amount.
  • summing the price information identified above and outputting the total settlement price of the image to be settled is an online process.
  • FIG. 7 schematically shows a block diagram of an image-based commodity settlement system according to one embodiment of the present disclosure.
  • an image-based commodity settlement system 700 includes: a self-service settlement station 701, a data line 702, and a self-service settlement device 703; wherein,
  • Self-service checkout station 701 used to provide functions such as placing goods, lighting, shooting, etc.;
  • the data line 702 is used to connect the self-checkout station 701 and the self-checkout device 703 to provide a data channel to transmit data;
  • the self-service settlement device 703 is used to determine the total settlement amount by acquiring and analyzing the image of the commodity to be settled from the data line 702.
  • FIG. 8 schematically shows a block diagram of an image-based commodity settlement apparatus according to an embodiment of the present disclosure.
  • an image-based commodity settlement apparatus 800 includes:
  • the determining module 801 is configured to detect the acquired image to be settled and determine the area image of the commodity to be settled in the image to be settled;
  • the feature vector acquisition module 802 is used to perform a low-dimensional mapping on the area image of the commodity to be settled to obtain a feature vector corresponding to the commodity to be settled;
  • the identification module 803 is configured to identify the price information corresponding to the feature vector of the commodity to be settled in a preset feature vector recognition database based on the feature vector of the commodity to be settled;
  • the output module 804 is configured to sum the identified price information and output the total settlement price of the image to be settled.
  • each function module of the image-based commodity settlement apparatus of the exemplary embodiment of the present disclosure corresponds to the steps of the above-described exemplary embodiment of the image-based commodity settlement method, for details not disclosed in the embodiment of the disclosed apparatus, please refer to this An embodiment of the aforementioned image-based commodity settlement method is disclosed.
  • FIG. 9 shows a schematic structural diagram of a computer system 900 suitable for implementing an electronic device of an embodiment of the present disclosure.
  • the computer system 900 of the electronic device shown in FIG. 9 is only an example, and should not bring any limitation to the functions and use scope of the embodiments of the present disclosure.
  • the computer system 900 includes a central processing unit (CPU) 901 that can be loaded into a random access memory (RAM) 903 from a program stored in a read-only memory (ROM) 902 or from the storage section 908 Instead, perform various appropriate actions and processing.
  • RAM 903 random access memory
  • ROM 902 read-only memory
  • RAM 903 various programs and data necessary for system operation are also stored.
  • the CPU 901, ROM 902, and RAM 903 are connected to each other through a bus 904.
  • An input/output (I/O) interface 905 is also connected to the bus 904.
  • the following components are connected to the I/O interface 905: an input section 1206 including a keyboard, a mouse, etc.; an output section 907 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker; a storage section 908 including a hard disk, etc. ; And a communication section 909 including a network interface card such as a LAN card, a modem, etc.
  • the communication section 909 performs communication processing via a network such as the Internet.
  • the drive 910 is also connected to the I/O interface 905 as needed.
  • a removable medium 911 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 910 as necessary, so that the computer program read out therefrom is installed into the storage section 908 as necessary.
  • the process described above with reference to the flowchart may be implemented as a computer software program.
  • embodiments of the present disclosure include a computer program product that includes a computer program carried on a computer-readable medium, the computer program containing program code for performing the method shown in the flowchart.
  • the computer program may be downloaded and installed from the network through the communication section 909, and/or installed from the removable medium 911.
  • CPU central processing unit
  • the computer-readable medium shown in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two.
  • the computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination of the above. More specific examples of computer readable storage media may include, but are not limited to: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read only memory (ROM), erasable removable Programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the foregoing.
  • the computer-readable storage medium may be any tangible medium containing or storing a program, and the program may be used by or in combination with an instruction execution system, apparatus, or device.
  • a computer-readable signal medium may include a data signal that is propagated in baseband or as part of a carrier wave, in which computer-readable program code is carried. This propagated data signal can take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing.
  • the computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, and the computer-readable medium may send, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device. .
  • the program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the foregoing.
  • each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the above-mentioned module, program segment, or part of code contains one or more for implementing a prescribed logical function Executable instructions.
  • the functions noted in the block may occur out of the order noted in the figures. For example, two blocks represented in succession may actually be executed in parallel, and they may sometimes be executed in reverse order, depending on the functions involved.
  • each block in the block diagram or flowchart, and a combination of blocks in the block diagram or flowchart can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be used It is realized by a combination of dedicated hardware and computer instructions.
  • the units described in the embodiments of the present disclosure may be implemented in software or hardware, and the described units may also be provided in a processor. Among them, the names of these units do not constitute a limitation on the unit itself under certain circumstances.
  • the present application also provides a computer-readable medium, which may be included in the electronic device described in the foregoing embodiment; or may exist alone without being assembled into the electronic device in.
  • the computer-readable medium carries one or more programs.
  • the electronic device is enabled to implement the screen control implementation and display method as in the foregoing embodiments.
  • Step S110 the acquired image to be settled is detected to determine the area image of the commodity to be settled in the image to be settled;
  • Step S120 to the settled Perform a low-dimensional mapping on the regional image of the commodity to obtain a feature vector corresponding to the commodity to be settled;
  • Step S130 based on the feature vector of the commodity to be settled, identify the feature vector of the commodity to be settled in a preset feature vector recognition database Corresponding price information;
  • Step S140 sum the identified price information, and output the total settlement price of the image to be settled.
  • the above-described electronic device can implement various steps shown in FIG. 2.
  • the electronic device described above can implement various steps shown in FIG. 3.
  • the electronic device described above can implement various steps shown in FIG. 4.
  • the electronic device described above can implement various steps shown in FIG. 5.
  • the electronic device described above can implement various steps shown in FIG. 6.
  • the example embodiments described here can be implemented by software, or can be implemented by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, U disk, mobile hard disk, etc.) or on a network , Including several instructions to enable a computing device (which may be a personal computer, server, touch terminal, or network device, etc.) to perform the method according to the embodiments of the present disclosure.
  • a non-volatile storage medium which may be a CD-ROM, U disk, mobile hard disk, etc.
  • a computing device which may be a personal computer, server, touch terminal, or network device, etc.

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Abstract

Des modes de réalisation de l'invention concernent un procédé et un appareil de facturation de produits à base d'images, un support et un dispositif électronique. Le procédé comprend : la réalisation d'une détection relative à une image acquise à facturer et la détermination, à partir de ladite image, d'une image régionale de produits à facturer; la réalisation d'une mise en correspondance de petite dimension sur l'image régionale des produits et l'obtention de vecteurs de caractéristiques correspondant aux produits respectifs; l'identification, en fonction des vecteurs de caractéristiques des produits respectifs, d'informations de tarification correspondant aux vecteurs de caractéristiques des produits respectifs à partir d'une base prédéfinie de données d'identification de vecteurs de caractéristiques; et la réalisation d'une sommation relative aux informations identifiées de tarification et l'établissement d'un montant total de facturation pour l'image à facturer. La solution technique proposée dans les modes de réalisation de la présente invention peut servir, dans un scénario de facturation de vente au détail, à identifier des produits placés sur un comptoir de facturation par un utilisateur sans demander à un opérateur d'effectuer de facturation, à identifier la quantité et les catégories des produits et à calculer automatiquement, rapidement et précisément un montant total de facturation, ce qui permet de réduire les coûts.
PCT/CN2019/101113 2018-11-27 2019-08-16 Procédé et appareil de facturation de produits à base d'images, support et dispositif électronique WO2020107951A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201811426518.6 2018-11-27
CN201811426518.6A CN111222382A (zh) 2018-11-27 2018-11-27 一种基于图像的商品结算方法、装置、介质及电子设备

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