CN111222382A - Commodity settlement method, commodity settlement device, commodity settlement medium and electronic equipment based on images - Google Patents
Commodity settlement method, commodity settlement device, commodity settlement medium and electronic equipment based on images Download PDFInfo
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Abstract
The embodiment of the invention provides a commodity settlement method, a commodity settlement device, a commodity settlement medium and electronic equipment based on images, wherein the commodity settlement method comprises the following steps: detecting the acquired image to be settled to determine an area image of the commodity to be settled in the image to be settled; performing 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; identifying price information corresponding to the feature vector of the commodity to be settled in a preset feature vector identification database based on the feature vector of the commodity to be settled; and summing the identified price information and outputting the total settlement price of the image to be settled. According to the technical scheme of the embodiment of the invention, in a retail settlement scene, a settlement clerk does not need to operate, the commodities placed on the settlement table by the user are identified, the categories and the number of the commodities are automatically, quickly and accurately identified, and the settlement amount is calculated, so that the cost is reduced.
Description
Technical Field
The invention relates to the technical field of commodity identification and settlement, in particular to a commodity settlement method based on an image and a commodity settlement device based on the image.
Background
The current commodity settlement modes mainly comprise the following modes:
(1) the commodity identification technology based on bar code identification comprises the following steps: the bar code printed on the commodity package is a unique identification for identifying the commodity and corresponds to a unique serial number of the commodity in a commodity library. The commodity identification technology based on the bar code identification identifies the commodity bar code and determines the serial number of the commodity through the image identification technology, then reads the information such as the name, the category, the price and the like of the commodity in the commodity library, and completes the functions of commodity settlement and the like, so that the manpower and the time for settlement personnel to manually input the commodity information can be reduced, and the settlement efficiency is improved. As the commodity bar code becomes the industry standard, the bar code identification equipment has mature technology and lower cost, and the technology is widely applied to retail scenes such as supermarkets, shopping malls and the like.
(2) The commodity identification technology based on radio frequency identification comprises the following steps: radio frequency identification is a tag identification technology based on wireless communication. The commodity identification technology based on radio frequency identification is that firstly, a radio frequency identification electronic tag is installed on a commodity package. During identification, the radio frequency identification reading equipment is used for reading the radio frequency identification electronic tag information of the commodities, acquiring the commodity numbers, reading the names, the categories, the prices and other information of the commodities in the commodity library, completing commodity settlement and other functions, reducing the manpower and time for settlement personnel to manually input the commodity information and improving the settlement efficiency. However, the technology has not been applied to retail scenes on a large scale due to the high deployment cost of devices such as radio frequency identification tags and readers.
(3) Image recognition technology: at present, a deep neural network model is mainly adopted in a mature method of an image recognition technology, a large-scale image data is used for training a neural network, and the specific technology comprises target detection and target classification in an image.
The prior art scheme has the following disadvantages:
(1) in a retail settlement scene, complex operations of settlement personnel are required, firstly, the positions of bar codes on commodity packages, operations of holding bar code identification equipment, aligning and scanning equipment scanners and the bar codes and the like are required to be searched, and a plurality of commodities are required to be scanned one by one, so that a large amount of manpower is consumed, and the settlement time is prolonged.
(2) Due to the fact that the RFID electronic tags need to be installed for commodities in advance, the RFID electronic tags are high in deployment and maintenance cost, and cannot be widely applied to retail scenes.
(3) At present, aiming at fine-grained object recognition such as commodity recognition, a general deep neural network model still cannot achieve high accuracy, and the adoption of a deeper network structure can increase the calculation time on one hand and lead to difficulty in model training on the other hand. In addition, due to the similarity of the appearance of the commodities, good distinguishing effect is difficult to achieve by only carrying out network training through a general classification loss function.
It is to be noted that the information disclosed in the above background section is only for enhancement of understanding of the background of the present invention and therefore may include information that does not constitute prior art known to a person of ordinary skill in the art.
Disclosure of Invention
An object of an embodiment of the present invention is to provide an image-based commodity settlement method and an image-based commodity settlement apparatus, which overcome one or more problems, such as a long settlement time, a complicated settlement operation, a high cost of an electronic tag, and low practicability of the conventional image recognition technology, due to limitations and defects of the related art, at least to some extent.
Additional features and advantages of embodiments of the invention will be set forth in the detailed description which follows, or in part will be obvious from the description, or may be learned by practice of the invention.
According to a first aspect of embodiments of the present invention, there is provided an image-based commodity settlement method, including:
detecting the acquired image to be settled to determine an area image of the commodity to be settled in the image to be settled;
performing 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;
identifying price information corresponding to the feature vector of the commodity to be settled in a preset feature vector identification database based on the feature vector of the commodity to be settled;
and summing the identified price information and outputting the total settlement price of the image to be settled.
In an embodiment of the present invention, the detecting the acquired image to be settled to determine an area image of the commodity to be settled in the image to be settled includes:
carrying out normalization processing on the image to be settled, determining invariant in the image to be settled, and converting the invariant into a normalized image to be settled which accords with a preset standard form;
carrying out commodity detection on the normalized image to be settled through a pre-trained commodity detection model, determining bounding box coordinate information surrounding the commodity to be settled in the normalized image to be settled, and intercepting an image in the bounding box coordinate information area;
and intercepting the area image of each commodity to be settled from the image in the bounding box coordinate information area.
In an embodiment of the invention, the pre-trained merchandise detection model includes:
inputting the training image, the coordinate information of the commodity bounding box to be settled and the commodity category as training samples of the commodity detection model into the commodity detection model;
summing a preset bounding box regression loss function and a preset commodity classification loss function to determine a total loss function of the commodity detection model;
and training the commodity detection model through a random gradient descent algorithm based on the training sample and the total loss function, and outputting the pre-trained commodity detection model.
In an embodiment of the present invention, the performing low-dimensional mapping on the area image of the to-be-settled commodity to obtain the feature vector corresponding to the to-be-settled commodity includes:
and inputting the regional image of the commodity to be settled into a pre-trained feature embedded neural network model, and mapping the regional image of the commodity to be settled into a low-dimensional feature vector.
In an embodiment of the present invention, the pre-trained feature-embedded neural network model includes:
taking the images and commodity categories after normalization processing of the training images as training samples of the characteristic embedded neural network model;
determining a preset normalized exponential loss function as a loss function of the characteristic embedded neural network model;
and training the feature embedded neural network model through a stochastic gradient descent algorithm based on the training sample and the loss function, and outputting the pre-trained feature embedded neural network model.
In an embodiment of the present invention, the identifying, in a preset feature vector identification database, price information corresponding to the feature vector of the commodity to be settled based on the feature vector of the commodity to be settled includes:
calculating the similarity between the feature vector of the commodity to be settled and the feature vector of the commodity put in storage in a preset feature vector identification database, and determining the feature vector of the commodity put in storage with the highest similarity;
inquiring corresponding price information according to the commodity category corresponding to the characteristic vector of the warehoused commodity with the highest similarity;
and determining the price information as the price information of the commodity to be settled.
In an embodiment of the present invention, the feature vector of the commodity that has been put into the warehouse in the preset feature vector identification database includes:
carrying out normalization processing on the images of the warehoused commodities to obtain the images of the warehoused commodities which accord with a preset standard format;
performing forward propagation operation on the image of the warehoused commodity in the preset standard format through a pre-trained feature embedded neural network model to obtain a low-dimensional feature vector of the image of the warehoused commodity;
and establishing a characteristic vector identification database based on the low-dimensional characteristic vector of the image of the warehoused commodity.
According to a second aspect of the embodiments of the present invention, there is provided an image-based commodity settlement apparatus including:
the determining module is used for detecting the acquired image to be settled and determining the area image of the commodity to be settled in the image to be settled;
the system comprises a feature vector acquisition module, a feature vector calculation module and a feature vector calculation module, wherein the feature vector acquisition module is used for carrying out low-dimensional mapping on an area image of a commodity to be settled to acquire a feature vector corresponding to the commodity to be settled;
the identification module is used for identifying the price information corresponding to the characteristic vector of the commodity to be settled in a preset characteristic vector identification database based on the characteristic vector of the commodity to be settled;
and the output module is used for summing the identified price information and outputting the settlement total price of the image to be settled.
According to a third aspect of embodiments of the present invention, there is provided a computer-readable medium having stored thereon a computer program which, when executed by a processor, implements the image-based commodity settlement method as described above in the first aspect of the embodiments above.
According to a fourth aspect of embodiments of the present invention, there is provided an electronic apparatus, including: one or more processors; a storage device for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement the image-based product settlement method as described above in the first aspect of the embodiments.
The technical scheme provided by the embodiment of the invention has the following beneficial effects:
the embodiment of the invention provides a commodity settlement method, a commodity settlement device, a commodity settlement medium and electronic equipment based on images, wherein the commodity settlement method comprises the following steps: detecting the acquired image to be settled to determine an area image of the commodity to be settled in the image to be settled; performing 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; identifying price information corresponding to the feature vector of the commodity to be settled in a preset feature vector identification database based on the feature vector of the commodity to be settled; and summing the identified price information and outputting the total settlement price of the image to be settled. According to the technical scheme of the embodiment of the invention, in a retail settlement scene, a settlement clerk does not need to operate, the commodities placed on the settlement table by the user are identified, the categories and the number of the commodities are automatically, quickly and accurately identified, and the settlement amount is calculated, so that the cost is reduced.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention. It is obvious that the drawings in the following description are only some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive effort. In the drawings:
fig. 1 schematically shows a flowchart of an image-based commodity settlement method according to an embodiment of the present invention.
FIG. 2 schematically illustrates a flow diagram of a warehouse scan of goods through a self-checkout station, in accordance with one embodiment of the present invention;
FIG. 3 is a flow chart schematically illustrating the determination of an area image for each item to be settled according to one embodiment of the present invention;
FIG. 4 schematically illustrates a training flow diagram of a merchandise detection model, according to one embodiment of the invention;
FIG. 5 schematically illustrates a training diagram of a feature-embedded neural network model, according to one embodiment of the present invention;
FIG. 6 schematically illustrates a diagram of building a feature vector identification database according to one embodiment of the invention;
FIG. 7 schematically illustrates a block diagram of an image-based merchandise settlement system according to one embodiment of the present invention;
fig. 8 schematically shows a block diagram of an image-based commodity settlement apparatus according to an embodiment of the present invention;
FIG. 9 illustrates a schematic structural diagram of a computer system suitable for use with the electronic device to implement an embodiment of the invention.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to provide a thorough understanding of embodiments of the invention. One skilled in the relevant art will recognize, however, that the invention may be practiced without one or more of the specific details, or with other methods, components, devices, steps, and so forth. In other instances, well-known methods, devices, implementations or operations have not been shown or described in detail to avoid obscuring aspects of the invention.
The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.
The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
Fig. 1 schematically shows a flowchart of an image-based commodity settlement method according to an embodiment of the present invention.
Referring to fig. 1, an image-based commodity settlement method according to an embodiment of the present invention includes the steps of:
step S110, detecting the acquired image to be settled, and determining a regional image of the commodity to be settled in the image to be settled;
step S120, carrying out 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, identifying price information corresponding to the feature vector of the commodity to be settled in a preset feature vector identification database based on the feature vector of the commodity to be settled;
step S140, summing the identified price information, and outputting the total settlement price of the image to be settled.
In a retail settlement scene, the technical scheme of the embodiment shown in fig. 1 does not need operation of a settlement clerk, identifies commodities placed on a settlement table by a user, realizes automatic, rapid and accurate identification of commodity types and quantity, calculates settlement amount, and reduces related cost.
Implementation details of the various steps shown in FIG. 1 are set forth below:
in step S110, the acquired image to be settled is detected, and an area image of the commodity to be settled in the image to be settled is determined.
In an embodiment of the present invention, before the step S110, the method further includes: the self-service settlement equipment that erects in advance acquires the image of treating the settlement, and is specific, self-service settlement equipment that erects in advance mainly includes: the intelligent image recognition system comprises a storage table (a storage basket, a surrounding table and the like), lighting equipment (LED lamps and the like), and shooting and recording equipment (a camera, a mobile phone and the like), wherein the shooting and recording equipment can also provide corresponding training images for an intelligent image recognition algorithm and commodity images for commodity warehousing.
In an embodiment of the present invention, based on the foregoing scheme, the erection of the recording device may be adjusted according to actual requirements, and the recording device with corresponding parameters is selected for erection according to reasons such as the image quality, the video quality, and the light ray required for identification.
In one embodiment of the invention, two color cameras for looking down and looking sideways can be used for shooting the appearance information of the commodity, and for each camera, an image is collected once every time the commodity rotates for X degrees, and Y images are collected in total.
In an embodiment of the present invention, based on the foregoing solution, step S110 is to perform validity check on the acquired image to be settled and identify whether the commodity is complete, so that during the process of erecting the camera device before step S110, the animal needs to be photographed from multiple angles to collect information related to the commodity, such as: the information such as the bar code, the price, the name and the like of the commodity provides a data base for accurately identifying the commodity in the later period.
In an embodiment of the invention, based on the foregoing scheme, the recording device provided in the self-service checkout station is an infrastructure for providing images, and videos recorded by the recording device can be stored according to different storage modes, and the recorded videos can be stored in a cloud or in a local external device storage.
Fig. 2 schematically illustrates a flow diagram of a warehouse scan of goods through a self-checkout station, in accordance with one embodiment of the present invention.
Referring to fig. 2, a process of determining an area image of each item to be settled according to an embodiment of the present invention includes the following steps:
step S210, a commodity is obtained;
step S220, inputting information such as bar codes, prices, names and the like of the commodities by scanning;
step S230, shooting the commodity;
step 240, judging whether the number of the shot images is Y, if not, returning to the step 230 to shoot again; if yes, executing the subsequent steps;
and step S250, storing the shot images into a commodity database and a training picture library respectively.
In an embodiment of the present invention, the step S110 specifically includes: carrying out normalization processing on the image to be settled, determining invariant in the image to be settled, and converting the invariant into a normalized image to be settled which accords with a preset standard form; carrying out commodity detection on the normalized image to be settled through a pre-trained commodity detection model, determining bounding box coordinate information surrounding the commodity to be settled in the normalized image to be settled, and intercepting an image in the bounding box coordinate information area; and intercepting the area image of each commodity to be settled from the image in the bounding box coordinate information area.
Fig. 3 schematically shows a flowchart for determining an area image of each item to be settled according to an embodiment of the present invention.
Referring to fig. 3, a process of determining an area image of each item to be settled according to an embodiment of the present invention includes the following steps:
step S310, a user places commodities to be settled on a self-service settlement table, and a shooting device automatically shoots images of all the commodities to be settled;
step S320, normalizing the image to be settled as the input of a pre-trained commodity detection model;
step S330, initializing a pre-trained commodity detection model by using the trained detection model;
step S340, detecting the commodity to be settled in the image to be settled by using a pre-trained commodity detection model to obtain bounding box coordinates of the commodity to be settled in the image to be settled;
in one embodiment of the present invention, 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.
And step S350, intercepting the area image of each commodity to be settled according to the commodity bounding box coordinates.
In an embodiment of the present invention, the determining the area image of each commodity to be settled is an online process.
In an embodiment of the present invention, based on the foregoing scheme, the pre-trained merchandise detection model may be obtained by:
inputting the training image, the coordinate information of the commodity bounding box to be settled and the commodity category as training samples of the commodity detection model into the commodity detection model;
summing a preset bounding box regression loss function and a preset commodity classification loss function to determine a total loss function of the commodity detection model;
and training the commodity detection model through a random gradient descent algorithm based on the training sample and the total loss function, and outputting the pre-trained commodity detection model.
FIG. 4 schematically illustrates a training flow diagram of a merchandise detection model, according to one embodiment of the invention.
Referring to fig. 4, a training process of a commodity detection model according to an embodiment of the present invention includes the following steps:
step S410, preprocessing such as normalization, color balance and the like is carried out on the batch of warehousing commodity images;
step S420, taking the training image, the corresponding coordinates of the commodity bounding box and the commodity category as training samples of the lightweight commodity detection model;
step S3430, initializing a commodity detection model by using a pre-training model;
step S440, summing a bounding box regression loss function and a commodity classification loss function to serve as a total loss function of the commodity detection model, and training the commodity detection model by adopting a random gradient descent algorithm;
and step S450, storing the commodity detection model.
In an embodiment of the present invention, the training process of the commodity detection model is an offline processing process.
In step S120, the area image of the commodity to be settled is subjected to low-dimensional mapping, and a feature vector corresponding to the commodity to be settled is obtained.
In an embodiment of the present invention, the performing low-dimensional mapping on the area image of the to-be-settled commodity to obtain the feature vector corresponding to the to-be-settled commodity includes: and inputting the regional image of the commodity to be settled into a pre-trained feature embedded neural network model, and mapping the regional image of the commodity to be settled into a low-dimensional feature vector.
In one embodiment of the present invention, based on the foregoing scheme, the pre-trained feature-embedded neural network model is trained by: taking the images and commodity categories after normalization processing of the training images as training samples of the characteristic embedded neural network model; determining a preset normalized exponential loss function as a loss function of the characteristic embedded neural network model; and training the feature embedded neural network model through a stochastic gradient descent algorithm based on the training sample and the loss function, and outputting the pre-trained feature embedded neural network model.
FIG. 5 schematically shows a training diagram of a feature-embedded neural network model, according to one embodiment of the present invention.
Referring to fig. 5, a training process of feature-embedded neural network model according to an embodiment of the present invention includes the following steps:
step S510, carrying out preprocessing such as normalization, color balance and the like on the batch of training commodity images;
step S520, embedding the training images and the commodity categories into a training sample of the neural network model as features;
step S530, initializing a feature embedding neural network model by using a pre-training model;
step S540, calculating the network loss of the characteristic embedded neural network model by using a normalized exponential loss function, and training the characteristic embedded neural network model by adopting a random gradient descent method;
and step S550, storing the feature embedded neural network model.
In one embodiment of the present invention, the above-mentioned establishing of the feature vector recognition database is an offline processing procedure.
In step S130, based on the feature vector of the to-be-settled commodity, price information corresponding to the feature vector of the to-be-settled commodity is identified in a preset feature vector identification database.
In an embodiment of the present invention, the identifying, in a preset feature vector identification database, the price information corresponding to the feature vector of the commodity to be settled specifically includes: calculating the similarity between the feature vector of the commodity to be settled and the feature vector of the commodity put in storage in a preset feature vector identification database, and determining the feature vector of the commodity put in storage with the highest similarity; inquiring corresponding price information according to the commodity category corresponding to the characteristic vector of the warehoused commodity with the highest similarity; and determining the price information as the price information of the commodity to be settled.
In an embodiment of the present invention, based on the foregoing scheme, the feature vector of the commodity that has been put into storage in the preset feature vector identification database includes: carrying out normalization processing on the images of the warehoused commodities to obtain the images of the warehoused commodities which accord with a preset standard format; performing forward propagation operation on the image of the warehoused commodity in the preset standard format through a pre-trained feature embedded neural network model to obtain a low-dimensional feature vector of the image of the warehoused commodity; and establishing a characteristic vector identification database based on the low-dimensional characteristic vector of the image of the warehoused commodity.
Fig. 6 schematically shows a schematic diagram of building a feature vector identification database according to an embodiment of the invention.
Referring to fig. 6, building a feature vector identification database according to an embodiment of the present invention includes the following steps:
step S610, normalizing the acquired warehousing commodity pictures and the like;
step S620, using the trained identification model to initialize the characteristic and embed the characteristic into a neural network model;
step S630, using the characteristic embedded neural network model to perform forward propagation operation on the warehoused commodity picture, and extracting the characteristic vector of the warehoused commodity picture;
step S640, establishing a feature vector identification database for feature vectors of all warehousing commodity pictures;
and step S650, storing and establishing a feature vector identification database.
In one embodiment of the present invention, the above-mentioned establishing of the feature vector recognition database is an offline processing procedure.
Step S140, summing the identified price information, and outputting the total settlement price of the image to be settled.
In one embodiment of the invention, after the price information of the identified commodities to be identified is summed, the total settlement amount of the images to be identified can be calculated, so that the categories and the quantities of the commodities can be automatically, quickly and accurately identified, and the total settlement amount can be calculated.
In an embodiment of the present invention, the identified price information is summed, and the total settlement price of the image to be settled is output as an online processing procedure.
It should be noted that the above-mentioned embodiments are only preferred embodiments of the present invention, and are not intended to limit the scope of the present invention.
Embodiments of the apparatus of the present invention will be described below, which may be used to implement the image-based product settlement method of the present invention described above.
Fig. 7 schematically shows a block diagram of an image-based commodity settlement system according to an embodiment of the present invention.
Referring to fig. 7, an image-based commodity settlement system 700 according to an embodiment of the present invention includes: a self-service checkout station 701, a data line 702, and a self-service checkout apparatus 703; wherein the content of the first and second substances,
a self-service checkout station 701 for providing functions of placing goods, lighting, photographing, and the like;
a data line 702 for connecting the self-service checkout station 701 and the self-service checkout apparatus 703 and providing data channel transmission data;
and the self-service settlement device 703 is used for determining 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 invention.
Referring to fig. 8, an image-based product settlement apparatus 800 according to an embodiment of the present invention includes:
a determining module 801, configured to detect the acquired image to be settled, and determine an area image of the commodity to be settled in the image to be settled;
a feature vector obtaining module 802, configured to perform low-dimensional mapping on an area image of a to-be-settled commodity, so as to obtain a feature vector corresponding to the to-be-settled commodity;
the identifying module 803 is configured to identify, based on the feature vector of the to-be-settled commodity, price information corresponding to the feature vector of the to-be-settled commodity in a preset feature vector identification database;
and an output module 804, configured to sum the identified price information and output the total settlement price of the image to be settled.
Since the respective functional modules of the image-based product settlement apparatus according to the exemplary embodiment of the present invention correspond to the steps of the exemplary embodiment of the image-based product settlement method described above, for details that are not disclosed in the embodiment of the apparatus according to the present invention, refer to the above-described embodiment of the image-based product settlement method according to the present invention.
Referring now to FIG. 9, shown is a block diagram of a computer system 900 suitable for use in implementing an electronic device of an embodiment of the present invention. The computer system 900 of the electronic device shown in fig. 9 is only an example, and should not bring any limitations to the function and the scope of the use of the embodiments of the present invention.
As shown in fig. 9, the computer system 900 includes a Central Processing Unit (CPU)901 that can perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)902 or a program loaded from a storage section 908 into a Random Access Memory (RAM) 903. In the RAM 903, various programs and data necessary for system operation are also stored. The CPU901, ROM 902, and RAM 903 are connected to each other via a bus 904. An input/output (I/O) interface 905 is also connected to bus 904.
The following components are connected to the I/O interface 905: an input section 1206 including a keyboard, a mouse, and the like; an output section 907 including components such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage portion 908 including a hard disk and the like; and a communication section 909 including a network interface card such as a LAN card, a modem, or the like. 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 necessary. A removable medium 911 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 910 as necessary, so that a computer program read out therefrom is mounted into the storage section 908 as necessary.
In particular, according to an embodiment of the present invention, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the invention include a computer program product comprising a computer program embodied on a computer-readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 909, and/or installed from the removable medium 911. The above-described functions defined in the system of the present application are executed when the computer program is executed by a Central Processing Unit (CPU) 901.
It should be noted that the computer readable medium shown in the present invention can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present invention may be implemented by software, or may be implemented by hardware, and the described units may also be disposed in a processor. Wherein the names of the elements do not in some way constitute a limitation on the elements themselves.
As another aspect, the present application also provides a computer-readable medium, which may be contained in the electronic device described in the above embodiments; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs, and when the one or more programs are executed by the electronic device, the electronic device is enabled to implement the screen control implementation and display method in the embodiment.
For example, the electronic device described above may implement as shown in fig. 1: step S110, detecting the acquired image to be settled, and determining a regional image of the commodity to be settled in the image to be settled; step S120, carrying out 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, identifying price information corresponding to the feature vector of the commodity to be settled in a preset feature vector identification database based on the feature vector of the commodity to be settled; step S140, summing the identified price information, and outputting the total settlement price of the image to be settled.
As another example, the electronic device described above may implement the steps shown in fig. 2.
As another example, the electronic device described above may implement the steps shown in fig. 3.
As another example, the electronic device described above may implement the steps shown in fig. 4.
As another example, the electronic device described above may implement the steps shown in fig. 5.
As another example, the electronic device described above may implement the steps shown in fig. 6.
It should be noted that although in the above detailed description several modules or units of the device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit, according to embodiments of the invention. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiment of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiment of the present invention.
Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims.
It will be understood that the invention is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the invention is limited only by the appended claims.
Claims (10)
1. An image-based commodity settlement method, comprising:
detecting the acquired image to be settled to determine an area image of the commodity to be settled in the image to be settled;
performing 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;
identifying price information corresponding to the feature vector of the commodity to be settled in a preset feature vector identification database based on the feature vector of the commodity to be settled;
and summing the identified price information and outputting the total settlement price of the image to be settled.
2. The image-based commodity settlement method according to claim 1, wherein the detecting the acquired image to be settled to determine an area image of the commodity to be settled in the image to be settled comprises:
carrying out normalization processing on the image to be settled, determining invariant in the image to be settled, and converting the invariant into a normalized image to be settled which accords with a preset standard form;
carrying out commodity detection on the normalized image to be settled through a pre-trained commodity detection model, determining bounding box coordinate information surrounding the commodity to be settled in the normalized image to be settled, and intercepting an image in a bounding box coordinate information area;
and intercepting the area image of each commodity to be settled from the image in the bounding box coordinate information area.
3. The image-based commodity settlement method according to claim 2, wherein the pre-trained commodity detection model comprises:
inputting the training image, the coordinate information of the commodity bounding box to be settled and the commodity category as training samples of the commodity detection model into the commodity detection model;
summing a preset bounding box regression loss function and a preset commodity classification loss function to determine a total loss function of the commodity detection model;
and training the commodity detection model through a stochastic gradient descent algorithm based on the training sample and the total loss function, and outputting the pre-trained commodity detection model.
4. The image-based commodity settlement method according to claim 1, wherein 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 comprises:
and inputting the regional image of the commodity to be settled into a pre-trained feature embedded neural network model, and mapping the regional image of the commodity to be settled into a low-dimensional feature vector.
5. The image-based commodity settlement method of claim 4, wherein the pre-trained feature-embedded neural network model comprises:
taking the images and commodity categories after normalization processing of the training images as training samples of the characteristic embedded neural network model;
determining a preset normalized exponential loss function as a loss function of the characteristic embedded neural network model;
and training the feature embedded neural network model through a stochastic gradient descent algorithm based on the training sample and the loss function, and outputting the pre-trained feature embedded neural network model.
6. The image-based commodity settlement method according to claim 1, wherein the identifying price information corresponding to the feature vector of the commodity to be settled in a preset feature vector identification database based on the feature vector of the commodity to be settled comprises:
calculating the similarity between the feature vector of the commodity to be settled and the feature vector of the commodity put in storage in a preset feature vector identification database, and determining the feature vector of the commodity put in storage with the highest similarity;
inquiring corresponding price information according to the commodity category corresponding to the characteristic vector of the warehoused commodity with the highest similarity;
and determining the price information as the price information of the commodity to be settled.
7. The image-based commodity settlement method according to claim 6, wherein the feature vector of the commodity put in storage in the preset feature vector recognition database comprises:
carrying out normalization processing on the images of the warehoused commodities to obtain the images of the warehoused commodities which accord with a preset standard format;
performing forward propagation operation on the image of the warehoused commodity in the preset standard format through a pre-trained feature embedded neural network model to obtain a low-dimensional feature vector of the image of the warehoused commodity;
and establishing a characteristic vector identification database based on the low-dimensional characteristic vector of the image of the warehoused commodity.
8. An image-based commodity settlement apparatus, comprising:
the determining module is used for detecting the acquired image to be settled and determining the area image of the commodity to be settled in the image to be settled;
the system comprises a feature vector acquisition module, a feature vector calculation module and a feature vector calculation module, wherein the feature vector acquisition module is used for carrying out low-dimensional mapping on an area image of a commodity to be settled to obtain a feature vector corresponding to the commodity to be settled;
the identification module is used for identifying price information corresponding to the feature vector of the commodity to be settled in a preset feature vector identification database based on the feature vector of the commodity to be settled;
and the output module is used for summing the identified price information and outputting the settlement total price of the image to be settled.
9. A computer-readable medium on which a computer program is stored, wherein the program, when executed by a processor, implements the image-based commodity settlement method of any one of claims 1 to 7.
10. An electronic device, comprising:
one or more processors;
a storage device for storing one or more programs that, when executed by the one or more processors, cause the one or more processors to implement the image-based commodity settlement method of any one of claims 1 to 7.
Priority Applications (2)
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| CN201811426518.6A CN111222382A (en) | 2018-11-27 | 2018-11-27 | Commodity settlement method, commodity settlement device, commodity settlement medium and electronic equipment based on images |
| PCT/CN2019/101113 WO2020107951A1 (en) | 2018-11-27 | 2019-08-16 | Image-based product checkout method and apparatus, medium, and electronic device |
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| CN201811426518.6A CN111222382A (en) | 2018-11-27 | 2018-11-27 | Commodity settlement method, commodity settlement device, commodity settlement medium and electronic equipment based on images |
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