CN115100640A - Artificial intelligence-based intelligent supermarket commodity sales big data detection method and system - Google Patents
Artificial intelligence-based intelligent supermarket commodity sales big data detection method and system Download PDFInfo
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Abstract
The invention provides an artificial intelligence based method and system for detecting commodity sales big data of an intelligent supermarket, and relates to the technical field of commodity sales data detection. By obtaining a target merchandise area image at time T1 and a target merchandise area image at time T2, respectively; respectively detecting peak signal-to-noise ratios; then, denoising the target commodity region image at the time of T1 and the target commodity region image at the time of T2 respectively; then respectively carrying out super-resolution reconstruction; detecting the quantity of the commodities by adopting a multi-instance segmentation mutual-inspection technology to generate a first target commodity quantity and a second target commodity quantity; then calculating to obtain the sales quantity of the target commodity; then, acquiring and identifying the target commodity area image at the T2 moment by utilizing a multi-image enhanced mutual-verification OCR technology according to the commodity information of the target commodity area; and finally, generating target commodity sales information according to the target commodity sales number and the target commodity region commodity identification information, so that the commodity sales data can be accurately detected.
Description
Technical Field
The invention relates to the technical field of commodity sales data detection, in particular to an intelligent supermarket commodity sales big data detection method and system based on artificial intelligence.
Background
With the development of the times, more and more shopping supermarkets are built, and great convenience is provided for shopping of the people. In the supermarket operation process, the accurate commodity sales data can be obtained, so that direct data support can be provided for timely replenishment of commodities, timely shelf discharge of overdue commodities and the like, and direct data support can be provided for optimization of the types of the commodities to be sold.
However, the traditional acquisition of the commodity sales data is usually completed by means of employee counting and the like, huge manpower resources are consumed, and although some supermarkets have applied methods such as target detection and target identification to detect the commodity sales data, still larger errors exist. With the continuous update of the technology in the field of artificial intelligence, direct technical support can be provided for commodity sales big data detection. Therefore, the method for detecting the big data of commodity sales in the intelligent supermarket based on the artificial intelligence has important value and significance.
Disclosure of Invention
The invention aims to provide an intelligent supermarket commodity sales big data detection method and system based on artificial intelligence, which are used for solving the problem that large errors exist in the detection of commodity sales data by adopting methods such as target detection and target recognition in the prior art.
In a first aspect, an embodiment of the application provides an artificial intelligence-based method for detecting big data of commodity sales in an intelligent supermarket, which includes the following steps:
respectively acquiring a target commodity area image at time T1 and a target commodity area image at time T2;
respectively carrying out peak signal-to-noise ratio detection on the target commodity region image at the time of T1 and the target commodity region image at the time of T2 to generate a first image peak signal-to-noise ratio and a second image peak signal-to-noise ratio;
denoising the target commodity region image at the time of T1 according to the first image peak signal-to-noise ratio to generate a first target commodity region preprocessed image;
denoising the target commodity region image at the time of T2 according to the second image peak signal-to-noise ratio to generate a second target commodity region preprocessed image;
performing super-resolution reconstruction on the first target commodity area preprocessed image and the second target commodity area preprocessed image respectively to generate a first target commodity area reconstructed image and a second target commodity area reconstructed image;
detecting the quantity of commodities by adopting a multi-instance segmentation mutual-inspection technology to respectively carry out commodity quantity detection on the first target commodity area reconstructed image and the second target commodity area reconstructed image so as to generate a first target commodity quantity and a second target commodity quantity;
calculating to obtain the sales quantity of the target commodities according to the first target commodity quantity and the second target commodity quantity;
acquiring and identifying a target commodity area image at the moment of T2 by using a multi-image enhanced mutually-verified OCR (optical character recognition) technology according to commodity information of the target commodity area to generate commodity identification information of the target commodity area;
and generating target commodity sales information according to the target commodity sales number and the target commodity region commodity identification information.
In the implementation process, a target commodity area image at the time of T1 and a target commodity area image at the time of T2 are respectively obtained; then, respectively carrying out peak signal-to-noise ratio detection on the target commodity region image at the time of T1 and the target commodity region image at the time of T2 to generate a first image peak signal-to-noise ratio and a second image peak signal-to-noise ratio; denoising the target commodity region image at the time of T1 according to the first image peak signal-to-noise ratio to generate a first target commodity region preprocessing image; denoising the target commodity region image at the time of T2 according to the second image peak signal-to-noise ratio to generate a second target commodity region preprocessed image; then, performing super-resolution reconstruction on the first target commodity area preprocessed image and the second target commodity area preprocessed image respectively to generate a first target commodity area reconstructed image and a second target commodity area reconstructed image; detecting the commodity quantity of the first target commodity region reconstructed image and the second target commodity region reconstructed image respectively by adopting a multi-instance segmentation and mutual inspection technology to generate a first target commodity quantity and a second target commodity quantity; calculating to obtain the sales number of the target commodity according to the number of the first target commodity and the number of the second target commodity; then acquiring and identifying the target commodity area image at the time of T2 by using a multi-image enhanced mutual-verification OCR technology according to the commodity information of the target commodity area to generate commodity identification information of the target commodity area; and finally, generating target commodity sales information according to the target commodity sales number and the target commodity region commodity identification information. The method comprises the steps of denoising a target commodity region image with high noise by using an image denoising method based on peak signal-to-noise ratio detection, pointedly optimizing the target commodity region image, detecting the target commodity region image by using a multi-instance segmentation mutual inspection technology based on image super-resolution reconstruction, more accurately detecting the quantity of target commodities, and more accurately identifying information such as production date, quality guarantee period and the like of the target commodities by using a multi-image enhanced mutual inspection OCR technology for slowly selling commodities, so that the commodity sales data can be accurately detected.
Based on the first aspect, in some embodiments of the present invention, the step of denoising the target commodity region image at the time point T1 according to the first image peak signal-to-noise ratio, and generating the first target commodity region preprocessed image includes the following steps:
judging whether the peak signal-to-noise ratio of the first image is greater than a preset signal-to-noise ratio threshold value, if so, taking the target commodity area image at the time of T1 as a first target commodity area preprocessing image; and if not, denoising the target commodity region image at the time of T1 to generate a first target commodity region preprocessing image.
Based on the first aspect, in some embodiments of the present invention, the method further comprises the following steps:
detecting the commodity quantity of the reconstructed image of the first target commodity region by adopting various example segmentation technologies to generate a plurality of commodity detection quantities;
and determining to obtain the first target commodity quantity according to the plurality of commodity detection quantities.
Based on the first aspect, in some embodiments of the present invention, the step of obtaining and identifying the target product area image at time T2 by using a multi-image-enhanced mutually-verified OCR technology according to the product information of the target product area, and generating the product identification information of the target product area comprises the steps of:
acquiring commodity information of a target commodity area;
judging whether the commodity information of the target commodity area is a chronic sale commodity, if so, identifying the target commodity area image at the moment of T2 by using a multi-image enhanced mutual-verification OCR technology to generate target commodity area commodity identification information; if not, the process is ended.
Based on the first aspect, in some embodiments of the present invention, the following steps are further included:
performing image enhancement on the target commodity region image at the time T2 by adopting a plurality of image enhancement methods to generate a plurality of target commodity region enhanced images at the time T2;
respectively identifying the target commodity area enhanced images at the time of T2 by adopting an OCR technology to generate a plurality of identification results;
and determining and obtaining the commodity identification information of the target commodity area according to the identification results.
Based on the first aspect, in some embodiments of the present invention, the following steps are further included:
and generating and sending replenishment reminding information to the merchant according to the sales quantity of the target commodity.
Based on the first aspect, in some embodiments of the present invention, the following steps are further included:
and generating and sending commodity expiration reminding information to the merchant according to the commodity identification information of the target commodity area.
In a second aspect, an embodiment of the present application provides an artificial intelligence-based smart supermarket commodity sales big data detection system, including:
a target merchandise area image acquisition module for respectively acquiring a target merchandise area image at time T1 and a target merchandise area image at time T2;
the peak signal-to-noise ratio detection module is used for respectively carrying out peak signal-to-noise ratio detection on the target commodity region image at the time of T1 and the target commodity region image at the time of T2 to generate a first image peak signal-to-noise ratio and a second image peak signal-to-noise ratio;
the first denoising processing module is used for denoising the target commodity region image at the time of T1 according to the first image peak signal-to-noise ratio to generate a first target commodity region preprocessed image;
the second denoising processing module is used for denoising the target commodity region image at the time of T2 according to the second image peak signal-to-noise ratio to generate a second target commodity region preprocessed image;
the super-resolution reconstruction module is used for respectively performing super-resolution reconstruction on the first target commodity area preprocessed image and the second target commodity area preprocessed image to generate a first target commodity area reconstructed image and a second target commodity area reconstructed image;
the commodity quantity detection module is used for respectively detecting the commodity quantity of the first target commodity region reconstruction image and the second target commodity region reconstruction image by adopting a multi-instance segmentation mutual-inspection technology to generate a first target commodity quantity and a second target commodity quantity;
the commodity sales quantity calculation module is used for calculating the target commodity sales quantity according to the first target commodity quantity and the second target commodity quantity;
the commodity information identification module is used for acquiring and identifying a target commodity area image at the moment T2 by using a multi-image enhanced mutually-verified OCR technology according to the commodity information of the target commodity area to generate commodity identification information of the target commodity area;
and the target commodity sales information generating module is used for generating target commodity sales information according to the target commodity sales quantity and the target commodity region commodity identification information.
In the implementation process, the target commodity region image obtaining module is used for respectively obtaining a target commodity region image at the time of T1 and a target commodity region image at the time of T2; the peak signal-to-noise ratio detection module is used for respectively detecting a peak signal-to-noise ratio of a target commodity region image at the time of T1 and a target commodity region image at the time of T2, and the first denoising processing module is used for denoising the target commodity region image at the time of T1 according to the peak signal-to-noise ratio of the first image; the second denoising processing module denoises the target commodity region image at the time of T2 according to the second image peak signal-to-noise ratio; the super-resolution reconstruction module respectively carries out super-resolution reconstruction on the first target commodity area preprocessed image and the second target commodity area preprocessed image; the commodity quantity detection module is used for respectively detecting the commodity quantity of the first target commodity region reconstruction image and the second target commodity region reconstruction image by adopting a multi-instance segmentation mutual inspection technology; the commodity sales number calculating module calculates to obtain the target commodity sales number; the commodity information identification module acquires and identifies a target commodity area image at the moment T2 by using a multi-image enhanced mutually-verified OCR technology according to the commodity information of the target commodity area; and the target commodity sales information generating module generates target commodity sales information according to the target commodity sales number and the target commodity region commodity identification information. The method comprises the steps of denoising a target commodity region image with high noise by using an image denoising method based on peak signal-to-noise ratio detection, pointedly optimizing the target commodity region image, detecting the target commodity region image by using a multi-instance segmentation mutual inspection technology based on image super-resolution reconstruction, more accurately detecting the quantity of target commodities, and more accurately identifying information such as production date, quality guarantee period and the like of the target commodities by using a multi-image enhanced mutual inspection OCR technology for slowly selling commodities, so that the commodity sales data can be accurately detected.
In a third aspect, an embodiment of the present application provides an electronic device, which includes a memory for storing one or more programs; a processor. The program or programs, when executed by a processor, implement the method of any of the first aspects as described above.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the method of any one of the above first aspects.
The embodiment of the invention at least has the following advantages or beneficial effects:
the embodiment of the invention provides an artificial intelligence-based method and system for detecting commodity sales big data of an intelligent supermarket, wherein a target commodity area image at the time of T1 and a target commodity area image at the time of T2 are respectively obtained; then, respectively carrying out peak signal-to-noise ratio detection on the target commodity region image at the time of T1 and the target commodity region image at the time of T2 to generate a first image peak signal-to-noise ratio and a second image peak signal-to-noise ratio; denoising the target commodity region image at the time of T1 according to the first image peak signal-to-noise ratio to generate a first target commodity region preprocessed image; denoising the target commodity region image at the time of T2 according to the second image peak signal-to-noise ratio to generate a second target commodity region preprocessed image; then, performing super-resolution reconstruction on the first target commodity area preprocessed image and the second target commodity area preprocessed image respectively to generate a first target commodity area reconstructed image and a second target commodity area reconstructed image; detecting the commodity quantity of the first target commodity region reconstructed image and the second target commodity region reconstructed image respectively by adopting a multi-instance segmentation and mutual inspection technology to generate a first target commodity quantity and a second target commodity quantity; calculating to obtain the sales number of the target commodity according to the number of the first target commodity and the number of the second target commodity; then acquiring and identifying the target commodity area image at the time of T2 by using a multi-image enhanced mutual-verification OCR technology according to the commodity information of the target commodity area to generate commodity identification information of the target commodity area; and finally, generating target commodity sales information according to the target commodity sales number and the target commodity region commodity identification information. The method comprises the steps of denoising a target commodity region image with high noise by using an image denoising method based on peak signal-to-noise ratio detection, pointedly optimizing the target commodity region image, detecting the target commodity region image by using a multi-instance segmentation mutual inspection technology based on image super-resolution reconstruction, more accurately detecting the quantity of target commodities, and more accurately identifying information such as production date, quality guarantee period and the like of the target commodities by using a multi-image enhanced mutual inspection OCR technology for slowly selling commodities, so that the commodity sales data can be accurately detected.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
FIG. 1 is a flow chart of a method for detecting big data of commodity sales in an intelligent supermarket, which is based on artificial intelligence and provided by the embodiment of the invention;
fig. 2 is a detailed process diagram of step S130 according to an embodiment of the present invention;
FIG. 3 is a detailed process diagram of merchandise quantity detection provided by an embodiment of the invention;
FIG. 4 is a block diagram of a smart supermarket commodity sales big data detection system based on artificial intelligence according to an embodiment of the present invention;
fig. 5 is a block diagram of an electronic device according to an embodiment of the present invention.
Icon: 110-target commodity area image acquisition module; 120-peak signal-to-noise ratio detection module; 130-a first denoising processing module; 140-a second denoising processing module; 150-a super-resolution reconstruction module; 160-commodity quantity detection module; 170-commodity sales number calculating module; 180-a commodity information identification module; 190-a target commodity sales information generation module; 101-a memory; 102-a processor; 103-a communication interface.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only for distinguishing the description, and are not construed as indicating or implying relative importance.
It should be noted that, in this document, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.
Examples
Some embodiments of the present application will be described in detail below with reference to the accompanying drawings. The embodiments described below and the individual features of the embodiments can be combined with one another without conflict.
Referring to fig. 1, fig. 1 is a flowchart of a method for detecting big data of commodity sales in an intelligent supermarket based on artificial intelligence according to an embodiment of the present invention. The intelligent supermarket commodity sales big data detection method based on artificial intelligence comprises the following steps:
step S110: respectively acquiring a target commodity area image at the time of T1 and a target commodity area image at the time of T2; the time T2 is greater than the time T1, and the time T1 is spaced from the time T2 by a predetermined time, which may be half a day or a day. The target commodity area image can be obtained by utilizing shooting equipment in a supermarket to extract a photo of an area where the target commodity is placed. The target product area image is acquired at time T1, and after a predetermined time interval, the target product area image is acquired at time T2.
Step S120: respectively carrying out peak signal-to-noise ratio detection on the target commodity region image at the time of T1 and the target commodity region image at the time of T2 to generate a first image peak signal-to-noise ratio and a second image peak signal-to-noise ratio; the peak signal-to-noise ratio detection is an objective standard for evaluating images, the peak signal-to-noise ratio detection is used for detecting noise of the images, the peak signal-to-noise ratio detection can be realized by adopting the prior art, and the details are not repeated.
Step S130: denoising the target commodity region image at the time of T1 according to the first image peak signal-to-noise ratio to generate a first target commodity region preprocessing image; specifically, if the first image peak signal-to-noise ratio is high, it indicates that the noise is not significant, and no denoising process may be performed; and if the first image peak signal-to-noise ratio is low, the noise is obvious, and the first image peak signal-to-noise ratio is directly subjected to denoising treatment.
Referring to fig. 2, fig. 2 is a detailed process diagram of step S130 according to an embodiment of the present invention, and a specific denoising process includes the following steps:
judging whether the peak signal-to-noise ratio of the first image is greater than a preset signal-to-noise ratio threshold value, if so, taking the target commodity area image at the time of T1 as a first target commodity area preprocessing image; and if not, denoising the target commodity region image at the time of T1 to generate a first target commodity region preprocessing image. The preset signal-to-noise ratio threshold may be a value preset according to experience.
Step S140: denoising the target commodity region image at the time of T2 according to the second image peak signal-to-noise ratio to generate a second target commodity region preprocessed image; specifically, if the second image peak signal-to-noise ratio is high, the noise is not significant, and denoising processing is not needed; and if the second image peak value signal-to-noise ratio is lower, the noise is obvious, and the denoising processing is directly carried out on the second image peak value. The denoising process is the same as the denoising process in step S130, and is not described herein again.
It should be noted that the image denoising may be performed by a filter-based method, such as a median filter, a model-based method, such as a sparse model, a gradient model, a markov random field model, or the like, a learning-based method, or the like, and the image denoising method belongs to the prior art and is not described herein again.
Step S150: performing super-resolution reconstruction on the first target commodity area preprocessed image and the second target commodity area preprocessed image respectively to generate a first target commodity area reconstructed image and a second target commodity area reconstructed image; and a clearer image can be obtained through super-resolution reconstruction. The super-resolution reconstruction is performed in the prior art, and is not described herein again.
Step S160: detecting the commodity quantity of the first target commodity region reconstructed image and the second target commodity region reconstructed image respectively by adopting a multi-instance segmentation and mutual inspection technology to generate a first target commodity quantity and a second target commodity quantity; the multi-instance segmentation mutual-inspection technology is used for detecting the quantity of commodities by utilizing a plurality of instance segmentation technologies, and the instance segmentation method for detecting the quantity of the commodities is the largest.
Taking the process of generating the first target commodity quantity as an example to specifically describe how to perform commodity quantity detection, the commodity quantity detection of the second target commodity region reconstructed image is the same as the commodity quantity detection process of the first target commodity region reconstructed image, and taking the process of generating the first target commodity quantity as an example to specifically describe how to perform commodity quantity detection, please refer to fig. 3, and fig. 3 is a detailed process diagram of commodity quantity detection provided by the embodiment of the present invention.
Firstly, detecting the commodity quantity of a first target commodity region reconstructed image by adopting various example segmentation technologies to generate a plurality of commodity detection quantities; the example segmentation techniques include semantic example segmentation based on discriminant loss function, video example segmentation VisTR, real-time example segmentation, etc. The above example segmentation techniques belong to the prior art and are not described herein.
Then, the first target commodity quantity is determined according to the plurality of commodity detection quantities. The above determination may be that the largest of the plurality of article detection amounts is the first target article data. For example: three classical example segmentation methods are used for detection, and the first target commodity quantity 22 can be obtained by respectively detecting that the commodity detection quantity is 21, 22 and 19.
Step S170: calculating to obtain the sales number of the target commodity according to the number of the first target commodity and the number of the second target commodity; the target commodity sales number is obtained by subtracting the second target commodity number from the first target commodity number, thereby obtaining the sales numbers of the commodities in the time period from T1 to T2.
Step S180: acquiring and identifying a target commodity area image at the time of T2 by using a multi-image enhanced mutual-verification OCR technology according to commodity information of a target commodity area to generate commodity identification information of the target commodity area;
firstly, acquiring commodity information of a target commodity area; the commodity information includes a commodity name, a commodity category, a commodity attribute, and the like. The acquisition may be obtained by human input or by identifying a shelf type label.
Then, judging whether the commodity information of the target commodity area is a chronic sale commodity, if so, identifying the target commodity area image at the time of T2 by using an OCR (optical character recognition) technology of multi-image enhancement mutual inspection to generate commodity identification information of the target commodity area; if not, the process is ended. The above judgment can be made by comparing the commodity category in the commodity information with preset classification information, for example, coca cola and farmer spring belong to commodities sold faster, and milk powder belongs to commodities sold slower.
Specifically, the process of recognizing the target commodity area image at the time T2 by using the multi-image-enhanced mutual-verification OCR technology includes the following steps:
firstly, performing image enhancement on a target commodity region image at the time of T2 by adopting a plurality of image enhancement methods to generate a plurality of target commodity region enhanced images at the time of T2; the image enhancement can be performed by using different image enhancement methods, including a point operation algorithm, a neighborhood denoising algorithm, a space-domain-based algorithm, and the like, and the image enhancement can be realized by using the prior art, which is not described herein.
Then, respectively identifying the target commodity region enhanced images at the time of T2 by adopting an OCR technology to generate a plurality of identification results; on the basis of different image enhancement methods, characters are recognized by using OCR technology respectively. And identifying the production date and the quality guarantee period of the commodity in the target commodity area image by using the multi-image enhanced mutual-inspection OCR technology. The production date and the quality guarantee period of one commodity can be only identified for the same commodity which is delivered from the same factory. The OCR technology is prior art and will not be described herein.
And finally, determining to obtain the commodity identification information of the target commodity area according to the identification results. The commodity identification information of the target commodity area can be determined by adopting a principle that a minority follows a majority. Such as: after most image enhancement methods enhance an image, a certain character is recognized as "8"; after the image is enhanced by a few image enhancement methods, a certain character is recognized as "0", and finally the character is recognized as "8". By adopting the OCR technology of multi-image enhancement mutual inspection for recognition, the recognition accuracy can be improved.
Step S190: and generating target commodity sales information according to the target commodity sales number and the target commodity region commodity identification information. The target commodity sales information includes a target commodity sales number and target commodity region commodity identification information. The information such as the number of sales, the production date and the quality guarantee period of the current goods on the shelf can be known through the sales information of the target goods. The information of the quality guarantee period can be utilized, the problem that the commodity sales process is not actually sold but automatically off-shelf is considered, so that the information of the production date, the quality guarantee period and the like of the target commodity is identified, and the commodity sales data can be detected more comprehensively and accurately.
And generating and sending replenishment reminding information to the merchant according to the sales quantity of the target commodities. And if the number of the commodities is less than a certain number, reminding the merchant of replenishing the commodities in time.
And generating and sending commodity expiration reminding information to the merchant according to the commodity identification information of the target commodity area. For the commodities which exceed or are about to exceed the quality guarantee period, the merchant needs to be reminded to put down the shelves in time.
In the implementation process, a target commodity area image at the time of T1 and a target commodity area image at the time of T2 are respectively obtained; then, respectively carrying out peak signal-to-noise ratio detection on the target commodity region image at the time of T1 and the target commodity region image at the time of T2 to generate a first image peak signal-to-noise ratio and a second image peak signal-to-noise ratio; denoising the target commodity region image at the time of T1 according to the first image peak signal-to-noise ratio to generate a first target commodity region preprocessing image; denoising the target commodity region image at the time of T2 according to the second image peak signal-to-noise ratio to generate a second target commodity region preprocessed image; then, performing super-resolution reconstruction on the first target commodity area preprocessed image and the second target commodity area preprocessed image respectively to generate a first target commodity area reconstructed image and a second target commodity area reconstructed image; detecting the commodity quantity of the first target commodity region reconstructed image and the second target commodity region reconstructed image respectively by adopting a multi-instance segmentation and mutual inspection technology to generate a first target commodity quantity and a second target commodity quantity; calculating to obtain the sales number of the target commodity according to the number of the first target commodity and the number of the second target commodity; then acquiring and identifying the target commodity area image at the time of T2 by using a multi-image enhanced mutual-verification OCR technology according to the commodity information of the target commodity area to generate commodity identification information of the target commodity area; and finally, generating target commodity sales information according to the target commodity sales number and the target commodity region commodity identification information. The method comprises the steps of denoising a target commodity region image with high noise by using an image denoising method based on peak signal-to-noise ratio detection, pointedly optimizing the target commodity region image, detecting the target commodity region image by using a multi-instance segmentation mutual inspection technology based on image super-resolution reconstruction, more accurately detecting the quantity of target commodities, and more accurately identifying information such as production date, quality guarantee period and the like of the target commodities by using a multi-image enhanced mutual inspection OCR technology for slowly selling commodities, so that the commodity sales data can be accurately detected.
Based on the same inventive concept, the invention further provides an artificial intelligence based intelligent supermarket commodity sales big data detection system, please refer to fig. 4, and fig. 4 is a structural block diagram of the artificial intelligence based intelligent supermarket commodity sales big data detection system provided by the embodiment of the invention. This wisdom supermarket commodity sales big data detecting system based on artificial intelligence includes:
a target merchandise area image acquisition module 110 for acquiring a target merchandise area image at time T1 and a target merchandise area image at time T2, respectively;
a peak signal-to-noise ratio detection module 120, configured to perform peak signal-to-noise ratio detection on the target commodity region image at time T1 and the target commodity region image at time T2, respectively, to generate a first image peak signal-to-noise ratio and a second image peak signal-to-noise ratio;
the first denoising processing module 130 is configured to denoise a target commodity region image at a time T1 according to a first image peak signal-to-noise ratio, and generate a first target commodity region preprocessed image;
the second denoising module 140 is configured to denoise the target commodity region image at the time T2 according to the second image peak signal-to-noise ratio, and generate a second target commodity region preprocessed image;
the super-resolution reconstruction module 150 is configured to perform super-resolution reconstruction on the first target commodity region preprocessed image and the second target commodity region preprocessed image respectively to generate a first target commodity region reconstructed image and a second target commodity region reconstructed image;
the commodity quantity detection module 160 is configured to perform commodity quantity detection on the first target commodity region reconstructed image and the second target commodity region reconstructed image respectively by using a multi-instance segmentation mutual-inspection technology, so as to generate a first target commodity quantity and a second target commodity quantity;
a commodity sales number calculation module 170, configured to calculate a target commodity sales number according to the first target commodity number and the second target commodity number;
the commodity information identification module 180 is used for acquiring and identifying a target commodity area image at the time of T2 by using a multi-image enhanced mutual-verification OCR technology according to the commodity information of the target commodity area to generate commodity identification information of the target commodity area;
and the target commodity sales information generating module 190 is configured to generate target commodity sales information according to the target commodity sales number and the target commodity region commodity identification information.
In the implementation process, the target commodity region image obtaining module 110 obtains a target commodity region image at time T1 and a target commodity region image at time T2, respectively; the peak signal-to-noise ratio detection module 120 respectively performs peak signal-to-noise ratio detection on the target commodity region image at the time of T1 and the target commodity region image at the time of T2, and the first denoising processing module 130 denoises the target commodity region image at the time of T1 according to the peak signal-to-noise ratio of the first image; the second denoising processing module 140 denoises the target commodity region image at the time T2 according to the second image peak signal-to-noise ratio; the super-resolution reconstruction module 150 respectively carries out super-resolution reconstruction on the first target commodity area preprocessed image and the second target commodity area preprocessed image; the commodity quantity detection module 160 detects the quantity of commodities in the first target commodity region reconstructed image and the second target commodity region reconstructed image respectively by adopting a multi-instance segmentation mutual-inspection technology; the commodity sales number calculating module 170 calculates the target commodity sales number; the commodity information identification module 180 acquires and identifies the target commodity area image at the time of T2 by using the multi-image enhanced mutual-verification OCR technology according to the commodity information of the target commodity area; the target commodity sales information generating module 190 generates target commodity sales information based on the target commodity sales amount and the target commodity region commodity identification information. The method comprises the steps of denoising a target commodity region image with high noise by using an image denoising method based on peak signal-to-noise ratio detection, pointedly optimizing the target commodity region image, detecting the target commodity region image by using a multi-instance segmentation mutual inspection technology based on image super-resolution reconstruction, more accurately detecting the quantity of target commodities, and more accurately identifying information such as production date, quality guarantee period and the like of the target commodities by using a multi-image enhanced mutual inspection OCR technology for slowly selling commodities, so that the commodity sales data can be accurately detected.
Referring to fig. 5, fig. 5 is a schematic structural block diagram of an electronic device according to an embodiment of the present disclosure. The electronic device comprises a memory 101, a processor 102 and a communication interface 103, wherein the memory 101, the processor 102 and the communication interface 103 are electrically connected to each other directly or indirectly to realize data transmission or interaction. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The memory 101 may be configured to store software programs and modules, such as program instructions/modules corresponding to the artificial intelligence based smart supermarket commodity sales big data detection system provided in the embodiment of the present application, and the processor 102 executes the software programs and modules stored in the memory 101, so as to execute various functional applications and data processing. The communication interface 103 may be used for communicating signaling or data with other node devices.
The Memory 101 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like.
The processor 102 may be an integrated circuit chip having signal processing capabilities. The Processor 102 may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components.
It will be appreciated that the configuration shown in fig. 5 is merely illustrative and that the electronic device may include more or fewer components than shown in fig. 5 or have a different configuration than shown in fig. 5. The components shown in fig. 5 may be implemented in hardware, software, or a combination thereof.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. 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 and/or flowchart illustration, and combinations of blocks in the block diagrams and/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.
In addition, functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist alone, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made to the present application by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
It will be evident to those skilled in the art that the present application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (10)
1. The method for detecting the big data of commodity sales in the intelligent supermarket based on artificial intelligence is characterized by comprising the following steps of:
respectively acquiring a target commodity area image at time T1 and a target commodity area image at time T2;
respectively carrying out peak signal-to-noise ratio detection on the target commodity region image at the time of T1 and the target commodity region image at the time of T2 to generate a first image peak signal-to-noise ratio and a second image peak signal-to-noise ratio;
denoising the target commodity region image at the time of T1 according to the first image peak signal-to-noise ratio to generate a first target commodity region preprocessing image;
denoising the target commodity region image at the time of T2 according to the second image peak signal-to-noise ratio to generate a second target commodity region preprocessed image;
performing super-resolution reconstruction on the first target commodity area preprocessed image and the second target commodity area preprocessed image respectively to generate a first target commodity area reconstructed image and a second target commodity area reconstructed image;
detecting the commodity quantity of the first target commodity region reconstructed image and the second target commodity region reconstructed image respectively by adopting a multi-instance segmentation and mutual inspection technology to generate a first target commodity quantity and a second target commodity quantity;
calculating to obtain the sales number of the target commodity according to the number of the first target commodity and the number of the second target commodity;
acquiring and identifying a target commodity area image at the time of T2 by using a multi-image enhanced mutual-verification OCR technology according to commodity information of a target commodity area to generate commodity identification information of the target commodity area;
and generating target commodity sales information according to the target commodity sales number and the target commodity region commodity identification information.
2. The artificial intelligence based detection method for big commodity sales data in an intelligent supermarket according to claim 1, wherein the step of denoising the target commodity region image at the moment of T1 according to the first image peak signal-to-noise ratio to generate the first target commodity region preprocessed image comprises the following steps:
judging whether the peak signal-to-noise ratio of the first image is greater than a preset signal-to-noise ratio threshold value, if so, taking the target commodity area image at the time of T1 as a first target commodity area preprocessing image; and if not, denoising the target commodity region image at the time of T1 to generate a first target commodity region preprocessing image.
3. The artificial intelligence based detection method for big data on commodity sales in an intelligent supermarket according to claim 1, further comprising the steps of:
detecting the commodity quantity of the reconstructed image of the first target commodity region by adopting various example segmentation technologies to generate a plurality of commodity detection quantities;
and determining to obtain the first target commodity quantity according to the plurality of commodity detection quantities.
4. The artificial intelligence based intelligent supermarket commodity sales big data detection method according to claim 1, wherein the step of obtaining and identifying the target commodity area image at the time of T2 by using a multi-image enhanced mutual inspection OCR technology according to the commodity information of the target commodity area to generate the commodity identification information of the target commodity area comprises the following steps:
acquiring commodity information of a target commodity area;
judging whether the commodity information of the target commodity area is a chronic sale commodity, if so, identifying the target commodity area image at the time of T2 by using an OCR (optical character recognition) technology of multi-image enhancement mutual inspection to generate commodity identification information of the target commodity area; if not, the process is ended.
5. The artificial intelligence based detection method for the big data of commodity sales in the intelligent supermarket according to claim 1, further comprising the following steps:
performing image enhancement on the target commodity region image at the time of T2 by adopting a plurality of image enhancement methods to generate a plurality of target commodity region enhanced images at the time of T2;
respectively identifying the target commodity area enhanced images at the time of T2 by adopting an OCR technology to generate a plurality of identification results;
and determining and obtaining the commodity identification information of the target commodity area according to the plurality of identification results.
6. The artificial intelligence based detection method for the big data of commodity sales in the intelligent supermarket according to claim 1, further comprising the following steps:
and generating and sending replenishment reminding information to the merchant according to the sales quantity of the target commodity.
7. The artificial intelligence based detection method for the big data of commodity sales in the intelligent supermarket according to claim 1, further comprising the following steps:
and generating and sending commodity expiration reminding information to the merchant according to the commodity identification information of the target commodity area.
8. Big data detecting system is sold to wisdom supermarket commodity based on artificial intelligence, its characterized in that includes:
a target merchandise area image acquisition module for respectively acquiring a target merchandise area image at time T1 and a target merchandise area image at time T2;
the peak signal-to-noise ratio detection module is used for respectively carrying out peak signal-to-noise ratio detection on the target commodity region image at the time of T1 and the target commodity region image at the time of T2 to generate a first image peak signal-to-noise ratio and a second image peak signal-to-noise ratio;
the first denoising processing module is used for denoising the target commodity region image at the time of T1 according to the first image peak signal-to-noise ratio to generate a first target commodity region preprocessed image;
the second denoising processing module is used for denoising the target commodity region image at the time of T2 according to the second image peak signal-to-noise ratio to generate a second target commodity region preprocessed image;
the super-resolution reconstruction module is used for respectively performing super-resolution reconstruction on the first target commodity area preprocessed image and the second target commodity area preprocessed image to generate a first target commodity area reconstructed image and a second target commodity area reconstructed image;
the commodity quantity detection module is used for respectively detecting the commodity quantity of the first target commodity region reconstruction image and the second target commodity region reconstruction image by adopting a multi-instance segmentation mutual-inspection technology to generate a first target commodity quantity and a second target commodity quantity;
the commodity sales quantity calculation module is used for calculating the target commodity sales quantity according to the first target commodity quantity and the second target commodity quantity;
the commodity information identification module is used for acquiring and identifying a target commodity area image at the moment T2 by using a multi-image enhanced mutually-verified OCR technology according to the commodity information of the target commodity area to generate commodity identification information of the target commodity area;
and the target commodity sales information generating module is used for generating target commodity sales information according to the target commodity sales quantity and the target commodity region commodity identification information.
9. An electronic device, comprising:
a memory for storing one or more programs;
a processor;
the one or more programs, when executed by the processor, implement the method of any of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-7.
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