CN111639928A - Bulk commodity metering device and method based on convolutional neural network - Google Patents
Bulk commodity metering device and method based on convolutional neural network Download PDFInfo
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- 238000013527 convolutional neural network Methods 0.000 title claims abstract description 52
- 238000000034 method Methods 0.000 title claims abstract description 19
- 238000005303 weighing Methods 0.000 claims abstract description 15
- 238000011176 pooling Methods 0.000 claims description 12
- 238000012549 training Methods 0.000 claims description 12
- 238000003709 image segmentation Methods 0.000 claims description 6
- 238000012545 processing Methods 0.000 claims description 5
- 238000003860 storage Methods 0.000 claims description 4
- 238000004590 computer program Methods 0.000 claims description 3
- 238000005457 optimization Methods 0.000 claims description 3
- 230000008030 elimination Effects 0.000 claims 1
- 238000003379 elimination reaction Methods 0.000 claims 1
- 230000006870 function Effects 0.000 description 5
- 238000012986 modification Methods 0.000 description 4
- 230000004048 modification Effects 0.000 description 4
- 210000002569 neuron Anatomy 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 3
- 238000007781 pre-processing Methods 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
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- 238000013528 artificial neural network Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q20/00—Payment architectures, schemes or protocols
- G06Q20/08—Payment architectures
- G06Q20/20—Point-of-sale [POS] network systems
- G06Q20/208—Input by product or record sensing, e.g. weighing or scanner processing
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01G—WEIGHING
- G01G19/00—Weighing apparatus or methods adapted for special purposes not provided for in the preceding groups
- G01G19/40—Weighing apparatus or methods adapted for special purposes not provided for in the preceding groups with provisions for indicating, recording, or computing price or other quantities dependent on the weight
- G01G19/413—Weighing apparatus or methods adapted for special purposes not provided for in the preceding groups with provisions for indicating, recording, or computing price or other quantities dependent on the weight using electromechanical or electronic computing means
- G01G19/414—Weighing apparatus or methods adapted for special purposes not provided for in the preceding groups with provisions for indicating, recording, or computing price or other quantities dependent on the weight using electromechanical or electronic computing means using electronic computing means only
- G01G19/4144—Weighing apparatus or methods adapted for special purposes not provided for in the preceding groups with provisions for indicating, recording, or computing price or other quantities dependent on the weight using electromechanical or electronic computing means using electronic computing means only for controlling weight of goods in commercial establishments, e.g. supermarket, P.O.S. systems
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/25—Determination of region of interest [ROI] or a volume of interest [VOI]
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/26—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
- G06V10/267—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion by performing operations on regions, e.g. growing, shrinking or watersheds
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Abstract
The invention provides a bulk commodity metering device based on a convolutional neural network, which comprises a camera module, a weighing module and a control system, wherein the camera module is used for acquiring a picture of a commodity to be weighed, the weighing module is used for acquiring the weight of the commodity, a commodity name list and a commodity price list are prestored in a database, and the control system receives a commodity image to be weighed and a commodity weighing signal and calculates the total commodity price according to the commodity name to be weighed, the commodity price and the weight of the commodity; the convolutional neural network receives the image of the commodity to be weighed, performs image recognition on the commodity to be weighed according to the image of the commodity to be weighed so as to obtain the name of the commodity to be weighed, and feeds the name of the commodity to be weighed back to the control system. The invention can improve the operation efficiency, reduce the operation time and avoid the problem of wrong commodity name selection. Correspondingly, the invention further provides a bulk commodity metering method based on the convolutional neural network.
Description
Technical Field
The invention relates to the technical field of electronic scales, in particular to a bulk commodity metering device and method based on a convolutional neural network.
Background
The electronic scale is an electronic weighing device integrating modern sensor technology, electronic technology and computer technology, can meet and solve the 'quick, accurate, continuous and automatic' weighing requirement in real life, and mainly comprises three parts, namely a bearing system (such as a scale pan and a scale body), a force transmission conversion system (such as a lever force transmission system and a sensor) and a value indicating system (such as a dial and an electronic display instrument).
For the electronic scale used in the current supermarket, a customer needs to go to a designated place after selecting bulk commodities, distinguish the commodities, select the commodity name, and finally the electronic scale calculates the total commodity price according to the commodity price and weight and prints a label. The electronic scale needs a customer to identify and select the commodity name so as to calculate the total price of the commodity, and has the problems of low operation efficiency, long operation time and easy occurrence of wrong commodity name selection.
Disclosure of Invention
In order to overcome the problems that the existing electronic scale is low in operating efficiency and easy to cause wrong commodity name selection, the invention provides a bulk commodity metering device and method based on a convolutional neural network, and the specific technical scheme is as follows:
the utility model provides a commodity metering device in bulk based on convolutional neural network, is including being used for gathering the camera module of waiting to weigh the commodity picture and being used for obtaining the weighing module of commodity weight, and it still includes:
and the control system is connected with the camera module and the weighing module, receives the commodity image to be weighed and the commodity weight, identifies the commodity image by using the convolutional neural network to obtain a commodity name, acquires the corresponding commodity price by inquiring the database after identifying the commodity name, and calculates the total commodity price by using the commodity price and the commodity weight.
Optionally, the convolutional neural network includes a first convolutional layer, a first maximum pooling layer, a second convolutional layer, a second maximum pooling layer, a third convolutional layer, a third maximum pooling layer, a fourth convolutional layer, a fourth maximum pooling layer, a Flatten layer, a first fully-connected layer, a Drop out layer, a second fully-connected layer, a third fully-connected layer, and a Softmax layer, which are connected in sequence.
Optionally, the bulk commodity metering device further includes a server, the server is connected to the control system through a network, commodity image training data obtained by image acquisition of an actual commodity uploaded by the control system is stored in the server, the convolutional neural network parameters are trained on the server by using the commodity image training data, and the convolutional neural network parameters of the control system are updated after training is finished.
Optionally, the to-be-weighed commodity image is obtained by performing image segmentation on the to-be-weighed commodity image and removing the background of the to-be-weighed commodity image.
Optionally, the bulk commodity metering device further comprises a display module connected with the control system, and the display module is used for displaying the commodity name, the commodity price, the commodity weight and the total commodity price.
Optionally, the bulk commodity metering device further comprises a printing module connected with the control system, and the printing module is used for printing the commodity name, the commodity price, the commodity weight and the total commodity price.
Optionally, the loss function of the convolutional neural network is a cross entropy loss function, and the optimization algorithm of the convolutional neural network is an RMSProp algorithm.
Correspondingly, the invention also provides a bulk commodity metering method based on the convolutional neural network, which comprises the following steps:
firstly, acquiring a picture of a commodity to be weighed and acquiring the weight of the commodity;
secondly, processing the picture of the commodity to be weighed to obtain an image of the commodity to be weighed;
thirdly, carrying out image recognition on the goods to be weighed by the convolutional neural network according to the goods to be weighed image to obtain the names of the goods to be weighed;
and fourthly, acquiring the commodity price corresponding to the commodity name of the commodity to be weighed, and calculating the total commodity price according to the commodity price and the commodity weight.
Optionally, the convolutional neural network is trained using commodity image data.
A computer-readable storage medium, which stores a computer program that, when executed by a processor, implements the bulk goods metering method described above.
The beneficial effects obtained by the invention are as follows: the method has the advantages that the convolutional neural network is utilized to carry out image recognition on the commodity, the name of the commodity to be weighed is obtained, then the total price of the commodity is automatically calculated according to the name of the commodity to be weighed, the price of the commodity and the weight of the commodity, a customer does not need to distinguish and select the name of the commodity, operation efficiency can be improved, operation time is shortened, and the problem of wrong name selection of the commodity is avoided.
Drawings
The present invention will be further understood from the following description taken in conjunction with the accompanying drawings, the emphasis instead being placed upon illustrating the principles of the embodiments.
Fig. 1 is a schematic overall structure diagram of a bulk commodity metering method based on a convolutional neural network in an embodiment of the present invention;
FIG. 2 is a schematic diagram of the structure of a convolutional neural network in an embodiment of the present invention;
fig. 3 is a schematic flow chart of a bulk commodity metering method based on a convolutional neural network in an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to embodiments thereof.
The invention relates to a bulk commodity metering device and method based on a convolutional neural network, which explain the following embodiments according to the attached drawings:
as shown in fig. 1, a bulk commodity metering device based on a convolutional neural network comprises a camera module for collecting a picture of a commodity to be weighed, a weighing module for acquiring a commodity weighing signal, and a control system.
The control system is connected with the camera module and the weighing module, receives the to-be-weighed commodity picture and the commodity weight, identifies the commodity image by using the convolutional neural network to obtain a commodity name, acquires a corresponding commodity price by inquiring the database after identifying the commodity name, and calculates a total commodity price by using the commodity price and the commodity weight.
As a preferred technical solution, as shown in fig. 2, the convolutional neural network includes a first convolutional layer, a first maximum pooling layer, a second convolutional layer, a second maximum pooling layer, a third convolutional layer, a third maximum pooling layer, a fourth convolutional layer, a fourth maximum pooling layer, a Flatten layer, a first fully-connected layer, a Drop out layer, a second fully-connected layer, a third fully-connected layer, and a Softmax layer, which are connected in sequence, where the Softmax layer outputs a final result (probability).
The input data dimension of the convolutional neural network is 3 × 100, namely, the three-channel RGB picture of 100 × 100, and the final output dimension is the number n of the commodities to be identified. The Softmax layer outputs the probability that the value is n commodities, and the commodity with the highest probability is a predicted commodity. Wherein, the role of the Flatten layer is to compress the feature data of 3 dimensions into a one-dimensional array. The Drop out layer is that when the forward propagation is carried out, P probability of each neuron is kept, and the rest neurons return to zero and do not propagate. The method has the advantages that different parts of neurons participate in each propagation, so that the special dependence on a certain neuron in the network is prevented, the generalization capability of the model is enhanced, and the risk of overfitting is reduced. The Drop out layer is only used during training and is masked during testing and detection, where P is 0.5, which is commonly used.
The bulk commodity metering device further comprises a server, the server is connected with the processor through a network, and a commodity image training data set uploaded by the control system and used for carrying out image acquisition on actual commodities is stored in the server. The convolutional neural network is trained using commodity image data.
The convolutional neural network loss function selects a cross entropy loss function (cross entropy error), the optimization algorithm selects the RMSProp algorithm, and the learning rate is set to 0.0001. Determining a commodity name list of the metering device to be used, then carrying out image acquisition on the actual commodity according to the commodity name list, then carrying out data enhancement on the acquired picture data, and finally obtaining a sufficient amount of data sets and manufacturing a commodity image training data set. And randomly selecting 70% of the commodity images in the manufactured commodity image training data set as training data, using the rest 30% of the commodity images as test data, and training the convolutional neural network.
In some embodiments, the to-be-weighed commodity image is obtained after preprocessing and image segmentation, and the processing method comprises the following steps: modifying the lightness, contrast and saturation of the commodity picture to be weighed- > image segmentation- > filling white pixels- > normalized pixels- > adjusting the image size of the background of the segmented object.
The lightness and contrast modification is that a certain value is directly added and multiplied on three channels of the picture RGB, the saturation modification needs to convert the color model from RGB to HSV channel, the S channel is multiplied by a certain value, and then the color model is converted back to the RGB color model. The image preprocessing enhances the characteristics of the image and improves the recognition rate of the neural network.
The image segmentation is an algorithm for segmenting objects and backgrounds, and a clustering algorithm K-means is adopted. The color of the scale plate is nearly pure white, the pixel value of the scale plate is higher than that of the commodity, the K value is set to be 2, and the K-means algorithm separates the background pixel value and the commodity pixel value to further achieve the effect of image segmentation.
In some embodiments, image pre-processing and image segmentation are implemented using an OpenCV open source vision library.
In some embodiments, the bulk goods metering device further comprises a display module connected with the processor, and a printing module connected with the processor, wherein the display module is used for displaying the goods name, the goods price, the goods weight, the total goods price, the payment two-dimensional code and the like. The printing module is used for printing commodity names, commodity prices, commodity weights, commodity total prices, payment two-dimensional codes and the like.
Accordingly, as shown in fig. 3, the present invention further provides a bulk goods metering method based on a convolutional neural network, which includes the following steps:
firstly, acquiring a picture of a commodity to be weighed and acquiring the weight of the commodity;
secondly, processing the picture of the commodity to be weighed to obtain an image of the commodity to be weighed;
thirdly, carrying out image recognition on the goods to be weighed by the convolutional neural network according to the goods to be weighed image to obtain the names of the goods to be weighed;
and fourthly, acquiring the commodity price corresponding to the commodity name of the commodity to be weighed, and calculating the total commodity price according to the commodity price and the commodity weight.
Optionally, the convolutional neural network is trained using commodity image data.
In some embodiments, the camera module used for collecting the picture of the to-be-weighed commodity is a high-definition camera, the picture of the to-be-weighed commodity is a color image, and the parameters of the camera are as follows:
(1) CMOS size: 1/4 inches;
(2) aperture (F): 2.8 of;
(3) focal Length (Focal Length): 3.4 mm;
(4) diagonal field angle (FOV): 66 degrees;
(5) a sensor pixel: 1080 p;
(6) resolution of still picture: 2592, 1944;
(7) and (3) supporting: 1080p30 frames, 720p60 frames and (640 × 480)60/90 frames of video and video;
(8) size: 25mm 24mm 9 mm.
In some embodiments, the weighing module for acquiring the weighing signal of the commodity adopts a YZC131 miniature weighing sensor module, inputs the signal acquired by the miniature weighing sensor module into a 24-bit high-precision a/D converter HX711 chip, and then transmits the signal to the processor for processing and calculating the weight of the commodity.
In some embodiments, the server stores information such as commodity price, commodity weight, total commodity price, and commodity name list for supermarket subsequent processing, such as checkout, statistics, and the like.
In some embodiments, when the convolutional neural network identifies the commodity placed on the metering device by a customer, due to certain identification errors, the commodity to be selected can be displayed on the display module from high to low according to the probability, and the name of the commodity is finally confirmed by a user, wherein the probability of the commodity to be selected is determined by the activation function of the output layer softmax of the convolutional neural network. After the customer confirms, the customer confirmation result (the commodity picture to be weighed and the commodity name) is uploaded to the server. And the server performs online learning by using the uploaded commodity picture to be weighed and the commodity name confirmed by the customer, and updates the parameter of the convolutional neural network. In this way, the commodity identification accuracy of the invention is gradually improved along with the use of the user.
Accordingly, the present invention also provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the bulk goods metering method described above.
In summary, the bulk commodity metering device and method based on the convolutional neural network disclosed by the invention have the following beneficial technical effects: the method has the advantages that the convolutional neural network is utilized to carry out image recognition on the commodity, the name of the commodity to be weighed is obtained, then the total price of the commodity is automatically calculated according to the name of the commodity to be weighed, the price of the commodity and the weight of the commodity, a customer does not need to distinguish and select the name of the commodity, operation efficiency can be improved, operation time is shortened, and the problem of wrong name selection of the commodity is avoided.
The above examples are to be construed as merely illustrative and not limitative of the remainder of the disclosure. After reading the description of the invention, the skilled person can make various changes or modifications to the invention, and these equivalent changes and modifications also fall into the scope of the invention defined by the claims.
Claims (10)
1. The bulk commodity metering device is characterized by further comprising a control system, wherein the control system is connected with the camera module and the weighing module, receives a commodity image to be weighed and the commodity weight, identifies the commodity image by using the convolutional neural network to obtain a commodity name, acquires a commodity price corresponding to the commodity name by inquiring a database after identifying the commodity name, and calculates a total commodity price by using the commodity price and the commodity weight.
2. The convolutional neural network-based bulk commodity metering device of claim 1, wherein the convolutional neural network comprises a first convolutional layer, a first max pooling layer, a second convolutional layer, a second max pooling layer, a third convolutional layer, a third max pooling layer, a fourth convolutional layer, a fourth max pooling layer, a Flatten layer, a first fully-connected layer, a Drop out layer, a second fully-connected layer, a third fully-connected layer, and a Softmax layer, which are connected in sequence.
3. The bulk commodity metering device based on the convolutional neural network as claimed in claim 2, further comprising a server, wherein the server is connected with the control system through a network, commodity image training data uploaded by the control system and obtained by image acquisition of an actual commodity is stored in the server, the convolutional neural network parameters are trained on the server by using the commodity image training data, and the convolutional neural network parameters of the control system are updated after training is finished.
4. The convolutional neural network-based bulk commodity metering device as claimed in claim 3, wherein the commodity image to be weighed is obtained by image segmentation of the commodity image to be weighed and elimination of the background of the commodity image to be weighed.
5. The convolutional neural network-based bulk commodity metering device of claim 4, further comprising a display module connected to the control system, wherein the display module is used for displaying a commodity name, a commodity price, a commodity weight and a commodity total price.
6. The convolutional neural network-based bulk commodity metering device of claim 5, further comprising a printing module connected to the control system, wherein the printing module is configured to print a commodity name, a commodity price, a commodity weight, and a commodity total price.
7. The convolutional neural network based bulk commodity metering device of claim 6, wherein the loss function of the convolutional neural network is a cross entropy loss function, and the optimization algorithm of the convolutional neural network is a RMSProp algorithm.
8. A method for metering bulk goods based on a convolutional neural network, which is applied to the bulk goods metering device as claimed in any one of claims 1 to 7, and which comprises the following steps:
firstly, acquiring a picture of a commodity to be weighed and acquiring the weight of the commodity;
secondly, processing the picture of the commodity to be weighed to obtain an image of the commodity to be weighed;
thirdly, carrying out image recognition on the goods to be weighed by the convolutional neural network according to the goods to be weighed image to obtain the names of the goods to be weighed;
and fourthly, acquiring the commodity price corresponding to the commodity name of the commodity to be weighed, and calculating the total commodity price according to the commodity price and the commodity weight.
9. The method of claim 8, wherein the convolutional neural network is trained using commodity image data.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program which, when executed by a processor, implements the bulk goods metering method as claimed in claim 8 or 9.
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