CN109141234A - A kind of intelligent article recognition methods and device - Google Patents
A kind of intelligent article recognition methods and device Download PDFInfo
- Publication number
- CN109141234A CN109141234A CN201810901335.9A CN201810901335A CN109141234A CN 109141234 A CN109141234 A CN 109141234A CN 201810901335 A CN201810901335 A CN 201810901335A CN 109141234 A CN109141234 A CN 109141234A
- Authority
- CN
- China
- Prior art keywords
- article
- detected
- anticipation
- classification
- physical features
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01G—WEIGHING
- G01G19/00—Weighing apparatus or methods adapted for special purposes not provided for in the preceding groups
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N9/00—Investigating density or specific gravity of materials; Analysing materials by determining density or specific gravity
- G01N9/02—Investigating density or specific gravity of materials; Analysing materials by determining density or specific gravity by measuring weight of a known volume
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N9/00—Investigating density or specific gravity of materials; Analysing materials by determining density or specific gravity
- G01N9/02—Investigating density or specific gravity of materials; Analysing materials by determining density or specific gravity by measuring weight of a known volume
- G01N2009/022—Investigating density or specific gravity of materials; Analysing materials by determining density or specific gravity by measuring weight of a known volume of solids
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Image Analysis (AREA)
Abstract
The invention discloses a kind of intelligent article recognition methods and devices.It is related to graphics technology, solves the problems, such as that automatic identification fruit is easy to happen erroneous judgement.This method comprises: pre-identification article to be detected, obtains the anticipation classification of the article to be detected；Detect the physical features of the article to be detected；Judge whether anticipation classification and the physical features of the article to be detected are consistent；Under the anticipation classification and physical features unanimous circumstances of the article to be detected, determine that the article to be detected is the article of the anticipation classification.Technical solution provided by the invention is suitable for intelligence sale scene, realizes fruit of high-accuracy and other items identification.
Description
Technical field
The present invention relates to graphics technology, espespecially a kind of intelligent article recognition methods and device.
Background technique
Currently, cloud computing big data is gradually approved by industry, the intelligent epoch at hand, intelligent robot, intelligent recognition,
Recognition of face is applicable in by all trades and professions, is provided convenience to people.With intelligentized development, present intelligent function,
Unmanned factory all has been put into operation and the closely bound up daily life, such as unmanned supermarket of people etc. and also lands in various regions, but
It is the identification for object really without good mode, is identified now or by the way of pasting two-dimension code label, this mode
It cannot be intelligence, general picture recognition can not really identify fruit, and image recognition can only identify respectively from shape
Kind fruit, but cannot recognize that and come true fruit and artificial fruit, because their shape is the same.Therefore, automatically
Identify that the accuracy rate of fruit is lower.
Summary of the invention
In order to solve the above-mentioned technical problems, the present invention provides a kind of intelligent article recognition methods and devices.Pass through physics
Signature verification image recognition result improves the accuracy rate of automatic fruit identification, solves automatic identification fruit and be easy to happen mistake
The problem of sentencing.
In order to reach the object of the invention, the present invention provides a kind of intelligent article recognition methods, comprising:
Pre-identification article to be detected obtains the anticipation classification of the article to be detected；
Detect the physical features of the article to be detected；
Judge whether anticipation classification and the physical features of the article to be detected are consistent；
Under the anticipation classification and physical features unanimous circumstances of the article to be detected, determine that the article to be detected is
The article of the anticipation classification.
Preferably, the physical features include at least volume, weight and density, and the physics for detecting the article to be detected is special
The step of sign includes:
The volume of the article to be detected is calculated by image recognition；
Weigh the quality of the article to be detected；
According to the volume and quality of the article to be detected, the density of the article to be detected is calculated.
Preferably, judge that the anticipation classification of the article to be detected step whether consistent with physical features includes:
According to the anticipation classification of the article to be detected, preset density range library is inquired, is found and the anticipation classification
Matched entry, the density range library include multiple entries, and each entry has recorded a kind of type of goods and corresponding density
Range；
Judge the physical features of the article to be detected whether in the density range of the anticipation categorical match；
Determine when the physical features of the article to be detected are in the density range of the anticipation categorical match it is described to
The anticipation classification for detecting article is consistent with physical features, otherwise determines inconsistent.
Preferably, it before the step of pre-identification article to be detected, the anticipation classification of the acquisition article to be detected, also wraps
It includes:
The density range data of various articles are collected, density range library, each entry in the density range library are generated
The physical features of record include at least the density range data of respective articles classification.
Preferably, judge also to wrap after the anticipation classification of the article to be detected step whether consistent with physical features
It includes:
In the case where the anticipation classification of the article to be detected and physical features are inconsistent, alarm prompt is issued.
Another aspect of the present invention additionally provides a kind of intelligent article identification device, comprising:
Pre-identification module is used for pre-identification article to be detected, obtains the anticipation classification of the article to be detected；
Feature detection module, for detecting the physical features of the article to be detected；
Whether as a result matching module, the anticipation classification and physical features for judging the article to be detected are consistent；
Result judgement module, for sentencing under the anticipation classification and physical features unanimous circumstances in the article to be detected
The fixed article to be detected is the article of the anticipation classification.
Preferably, the feature detection module includes:
Volume sensing unit, for calculating the volume of the article to be detected by image recognition；
Quality testing unit, for weighing the quality of the article to be detected；
Density computing unit calculates the article to be detected for the volume and quality according to the article to be detected
Density.
Preferably, the result matching module includes:
Density range query unit inquires preset density range for the anticipation classification according to the article to be detected
The entry with the anticipation categorical match is found in library, and the density range library includes multiple entries, and each entry has recorded one kind
Type of goods and corresponding density range；
Matching degree judging unit, for judging the physical features of the article to be detected whether in the anticipation categorical match
Density range in；
Consistency checking unit, for the physical features in the article to be detected in the density for prejudging categorical match
Determine that the anticipation classification of the article to be detected is consistent with physical features when in range, otherwise determines inconsistent.
Preferably, the device further include:
Density range library maintenance module, for collecting the density range data of various articles, generation density range library is described
The physical features of each of density range library program recording include at least the density range data of respective articles classification.
Preferably, the result judgement module, be also used to the article to be detected anticipation classification and physical features not
Under unanimous circumstances, alarm prompt is issued.
The present invention provides a kind of intelligent article recognition methods and device, first pre-identification article to be detected, described in acquisition
The anticipation classification of article to be detected, then detects the physical features of the article to be detected, and judges the article to be detected
It prejudges classification and whether physical features is consistent, under the anticipation classification and physical features unanimous circumstances of the article to be detected,
Determine that the article to be detected is the article of the anticipation classification.Solve the problems, such as that automatic identification fruit is easy to happen erroneous judgement,
Improve the accuracy rate of automatic fruit identification.
Other features and advantages of the present invention will be illustrated in the following description, also, partly becomes from specification
It obtains it is clear that understand through the implementation of the invention.The objectives and other advantages of the invention can be by specification, right
Specifically noted structure is achieved and obtained in claim and attached drawing.
Detailed description of the invention
Attached drawing is used to provide to further understand technical solution of the present invention, and constitutes part of specification, with this
The embodiment of application technical solution for explaining the present invention together, does not constitute the limitation to technical solution of the present invention.
Fig. 1 is a kind of flow diagram for intelligent article recognition methods that one embodiment of the invention provides；
Fig. 2 is the idiographic flow schematic diagram of step 103 in Fig. 1；
Fig. 3 is the idiographic flow schematic diagram of step 104 in Fig. 1；
Fig. 4 is a kind of structural schematic diagram for intelligent article identification device that one embodiment of the invention provides；
Fig. 5 is the structural schematic diagram of feature detection module 402 in Fig. 4；
Fig. 6 is the structural schematic diagram of result matching module 403 in Fig. 4.
Specific embodiment
To make the objectives, technical solutions, and advantages of the present invention clearer, below in conjunction with attached drawing to the present invention
Embodiment be described in detail.It should be noted that in the absence of conflict, in the embodiment and embodiment in the application
Feature can mutual any combination.
Step shown in the flowchart of the accompanying drawings can be in a computer system such as a set of computer executable instructions
It executes.Also, although logical order is shown in flow charts, and it in some cases, can be to be different from herein suitable
Sequence executes shown or described step.
Image recognition technology can distinguish the kind of different fruit substantially, but simple picture recognition technology is not
The true discernable artificail fruits for being real fruit or other imitations of energy or craftwork are because their shape is
The same, it be easy to cause erroneous judgement.
To solve the above-mentioned problems, the embodiment provides a kind of intelligent article recognition methods and device, pass through
Physical features authentication image recognition result improves the accuracy rate of automatic fruit identification, solves automatic identification fruit and is easy hair
The problem of raw erroneous judgement.
One embodiment of the invention provides a kind of intelligent article recognition methods, is identified not based on picture recognition technology
With kind fruit and calculate the fruit volume currently identified simultaneously, while weighing calculating, this method are carried out to current fruit
According to the real density range of the various fruit of big data analysis, according to the fruit variety of identification, volume, density calculate it is close
Whether degree, see result in rational density range.Final intelligent recognition fruit.The process of article identification is completed as schemed using this method
Shown in 1, comprising:
Step 101, the density range data for collecting various articles generate density range library.
The physical features of each of density range library program recording include at least the density model of respective articles classification
Enclose data.
Step 102, pre-identification article to be detected obtain the anticipation classification of the article to be detected.
In this step, by image recognition, the classification of article to be detected (such as fruit) is judged, obtain anticipation classification.
The physical features of step 103, the detection article to be detected.
This step is specifically as shown in Figure 2, comprising:
Step 1031, the volume that the article to be detected is calculated by image recognition.
In this step, image capturing system calculates the volume of current object according to collection result.
Step 1032, the quality for weighing the article to be detected.
In this step, weighing system multi-angle weighs the quality of current object.
Step 1033, according to the volume and quality of the article to be detected, calculate the density of the article to be detected.
Step 104 judges whether the anticipation classification of the article to be detected and physical features are consistent.
This step is specifically as shown in Figure 3, comprising:
Step 1041, according to the anticipation classification of the article to be detected, inquire preset density range library, find with it is described
Prejudge the entry of categorical match, the density range library includes multiple entries, each entry have recorded a kind of type of goods with it is right
The density range answered.
Whether step 1042 judges the physical features of the article to be detected in the density range for prejudging categorical match
It is interior.
Step 1043 is sentenced when the physical features of the article to be detected are in the density range of the anticipation categorical match
The anticipation classification of the fixed article to be detected is consistent with physical features, otherwise determines inconsistent.
Step 105, under the anticipation classification and physical features unanimous circumstances of the article to be detected, determine it is described to be checked
Survey the article that article is the anticipation classification.
Step 106, in the case where the anticipation classification of the article to be detected and inconsistent physical features, issue alarm mention
Show information.Such as prompt non-genuine fruit.
One embodiment of the invention additionally provides a kind of intelligent article recognition methods, including following process:
1, image identification system identification current item is P.
2, it is V that picture system, which calculates the volume of current item,.
3, it is M that weighing system, which weighs present physical quality,.
4, the density for calculating current object is ρ=M/V.
5, according to density p whether density library zone of reasonableness ρ p1- ρ p2.
6, if being identified by if zone of reasonableness, non-genuine article is otherwise prompted.
One embodiment of the invention additionally provides a kind of intelligent article identification device, and structure is as shown in Figure 4, comprising:
Pre-identification module 401 is used for pre-identification article to be detected, obtains the anticipation classification of the article to be detected；
Feature detection module 402, for detecting the physical features of the article to be detected；
Whether as a result matching module 403, the anticipation classification and physical features for judging the article to be detected are consistent；
Result judgement module 404, in the article to be detected anticipation classification and physical features unanimous circumstances under,
Determine that the article to be detected is the article of the anticipation classification.
Preferably, the structure of the feature detection module 402 is as shown in Figure 5, comprising:
Volume sensing unit 4021, for calculating the volume of the article to be detected by image recognition；
Quality testing unit 4022, for weighing the quality of the article to be detected；
Density computing unit 4023 calculates the object to be detected for the volume and quality according to the article to be detected
The density of product.
Preferably, the structure of the result matching module 403 is as shown in Figure 6, comprising:
Density range query unit 4031 inquires preset density for the anticipation classification according to the article to be detected
The entry with the anticipation categorical match is found in range library, and the density range library includes multiple entries, and each entry has recorded
A kind of type of goods and corresponding density range；
Matching degree judging unit 4032, for judging the physical features of the article to be detected whether in the anticipation classification
In matched density range；
Consistency checking unit 4033, for the physical features in the article to be detected in the anticipation categorical match
Determine that the anticipation classification of the article to be detected is consistent with physical features when in density range, otherwise determines inconsistent.
Preferably, the device further include:
Density range library maintenance module 405 generates density range library for collecting the density range data of various articles,
The physical features of each of density range library program recording include at least the density range data of respective articles classification.
Preferably, the result judgement module 404 is also used to anticipation classification and physical features in the article to be detected
In the case where inconsistent, alarm prompt is issued.
The embodiment provides a kind of intelligent article recognition methods and device, first pre-identification article to be detected,
The anticipation classification for obtaining the article to be detected, then detects the physical features of the article to be detected, and judges described to be checked
Whether anticipation classification and the physical features for surveying article are consistent, consistent in the anticipation classification and physical features of the article to be detected
In the case of, determine that the article to be detected is the article of the anticipation classification.It solves automatic identification fruit and is easy to happen erroneous judgement
The problem of, improve the accuracy rate of automatic fruit identification.
The density range of related fruit is obtained according to big data analysis, it is whether reasonable by weighing, volumetric analysis density, greatly
The accuracy rate of intelligent fruits identification is improved greatly, is calculated wherein weighing calculating can be used directly in unmanned supermarket's intelligent recognition weighing,
The construction for accelerating unmanned supermarket allows image recognition to be preferably community service.
It will appreciated by the skilled person that whole or certain steps, system, dress in method disclosed hereinabove
Functional module/unit in setting may be implemented as software, firmware, hardware and its combination appropriate.In hardware embodiment,
Division between the functional module/unit referred in the above description not necessarily corresponds to the division of physical assemblies；For example, one
Physical assemblies can have multiple functions or a function or step and can be executed by several physical assemblies cooperations.Certain groups
Part or all components may be implemented as by processor, such as the software that digital signal processor or microprocessor execute, or by
It is embodied as hardware, or is implemented as integrated circuit, such as specific integrated circuit.Such software can be distributed in computer-readable
On medium, computer-readable medium may include computer storage medium (or non-transitory medium) and communication media (or temporarily
Property medium).As known to a person of ordinary skill in the art, term computer storage medium is included in for storing information (such as
Computer readable instructions, data structure, program module or other data) any method or technique in the volatibility implemented and non-
Volatibility, removable and nonremovable medium.Computer storage medium include but is not limited to RAM, ROM, EEPROM, flash memory or its
His memory technology, CD-ROM, digital versatile disc (DVD) or other optical disc storages, magnetic holder, tape, disk storage or other
Magnetic memory apparatus or any other medium that can be used for storing desired information and can be accessed by a computer.This
Outside, known to a person of ordinary skill in the art to be, communication media generally comprises computer readable instructions, data structure, program mould
Other data in the modulated data signal of block or such as carrier wave or other transmission mechanisms etc, and may include any information
Delivery media.
Claims (10)
1. a kind of intelligent article recognition methods characterized by comprising
Pre-identification article to be detected obtains the anticipation classification of the article to be detected；
Detect the physical features of the article to be detected；
Judge whether anticipation classification and the physical features of the article to be detected are consistent；
Under the anticipation classification and physical features unanimous circumstances of the article to be detected, determine that the article to be detected is described
Prejudge the article of classification.
2. intelligent article recognition methods according to claim 1, which is characterized in that the physical features include at least body
Product, weight and density, the step of detecting the physical features of the article to be detected include:
The volume of the article to be detected is calculated by image recognition；
Weigh the quality of the article to be detected；
According to the volume and quality of the article to be detected, the density of the article to be detected is calculated.
3. intelligent article recognition methods according to claim 1, which is characterized in that judge the anticipation of the article to be detected
The classification step whether consistent with physical features include:
According to the anticipation classification of the article to be detected, preset density range library is inquired, is found and the anticipation categorical match
Entry, the density range library includes multiple entries, and each entry has recorded a kind of type of goods and corresponding density range；
Judge the physical features of the article to be detected whether in the density range of the anticipation categorical match；
Determine when the physical features of the article to be detected are in the density range of the anticipation categorical match described to be detected
The anticipation classification of article is consistent with physical features, otherwise determines inconsistent.
4. intelligent article recognition methods according to claim 3, which is characterized in that pre-identification article to be detected obtains institute
Before the step of stating the anticipation classification of article to be detected, further includes:
The density range data of various articles are collected, density range library, each of described density range library program recording are generated
Physical features include at least respective articles classification density range data.
5. intelligent article recognition methods according to claim 3, which is characterized in that judge the anticipation of the article to be detected
After classification and the whether consistent step of physical features further include:
In the case where the anticipation classification of the article to be detected and physical features are inconsistent, alarm prompt is issued.
6. a kind of intelligent article identification device characterized by comprising
Pre-identification module is used for pre-identification article to be detected, obtains the anticipation classification of the article to be detected；
Feature detection module, for detecting the physical features of the article to be detected；
Whether as a result matching module, the anticipation classification and physical features for judging the article to be detected are consistent；
Result judgement module, for determining institute under the anticipation classification and physical features unanimous circumstances in the article to be detected
State the article that article to be detected is the anticipation classification.
7. intelligent article identification device according to claim 6, which is characterized in that the feature detection module includes:
Volume sensing unit, for calculating the volume of the article to be detected by image recognition；
Quality testing unit, for weighing the quality of the article to be detected；
Density computing unit calculates the density of the article to be detected for the volume and quality according to the article to be detected.
8. intelligent article identification device according to claim 6, which is characterized in that the result matching module includes:
Density range query unit is inquired preset density range library, is sought for the anticipation classification according to the article to be detected
The entry with the anticipation categorical match is looked for, the density range library includes multiple entries, and each entry has recorded a kind of article
Type and corresponding density range；
Matching degree judging unit, for judging the physical features of the article to be detected whether in the close of the anticipation categorical match
It spends in range；
Consistency checking unit, for the physical features in the article to be detected in the density range for prejudging categorical match
Determine that the anticipation classification of the article to be detected is consistent with physical features when interior, otherwise determines inconsistent.
9. intelligent article identification device according to claim 8, which is characterized in that the device further include:
Density range library maintenance module generates density range library, the density for collecting the density range data of various articles
The physical features of each of range library program recording include at least the density range data of respective articles classification.
10. intelligent article identification device according to claim 8, which is characterized in that
The result judgement module is also used to anticipation classification and the inconsistent situation of physical features in the article to be detected
Under, issue alarm prompt.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810901335.9A CN109141234A (en) | 2018-08-09 | 2018-08-09 | A kind of intelligent article recognition methods and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810901335.9A CN109141234A (en) | 2018-08-09 | 2018-08-09 | A kind of intelligent article recognition methods and device |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109141234A true CN109141234A (en) | 2019-01-04 |
Family
ID=64792493
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810901335.9A Pending CN109141234A (en) | 2018-08-09 | 2018-08-09 | A kind of intelligent article recognition methods and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109141234A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111735735A (en) * | 2020-06-11 | 2020-10-02 | 山东滨州烟草有限公司 | Intelligent cigarette package nondestructive identification method and identifier |
| CN112665698A (en) * | 2020-12-15 | 2021-04-16 | 重庆电子工程职业学院 | Intelligent electronic scale |
| CN113640177A (en) * | 2021-06-29 | 2021-11-12 | 阿里巴巴新加坡控股有限公司 | Cargo density measuring method and system and electronic equipment |
| CN114608692A (en) * | 2022-01-22 | 2022-06-10 | 贵州省检测技术研究应用中心 | Method and system for automatically weighing inspection and detection samples |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102175569A (en) * | 2011-03-01 | 2011-09-07 | 武汉理工大学 | Underground iron ore dynamic weighing and real-time grate analyzing method |
| CN102654463A (en) * | 2012-04-13 | 2012-09-05 | 北京农业信息技术研究中心 | Watermelon quality NDT (non-destructive testing) method and device |
| CN105067480A (en) * | 2015-07-31 | 2015-11-18 | 陕西四维衡器科技有限公司 | Green channel cargo type detection method and detection equipment thereof |
| CN105203778A (en) * | 2014-06-12 | 2015-12-30 | 北京干山科技有限公司 | Three-dimensional reconstruction type ore grade analyzing device and method |
| CN105806743A (en) * | 2016-04-28 | 2016-07-27 | 西北农林科技大学 | Multi-view apple moldy core detection device and method |
| CN107084780A (en) * | 2017-05-12 | 2017-08-22 | 智锐达仪器科技南通有限公司 | A kind of intelligent electronic-scale and corresponding Weighing method |
| CN108097603A (en) * | 2017-12-06 | 2018-06-01 | 中国计量大学 | The shaddock quality method for separating of view-based access control model technology |
-
2018
- 2018-08-09 CN CN201810901335.9A patent/CN109141234A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102175569A (en) * | 2011-03-01 | 2011-09-07 | 武汉理工大学 | Underground iron ore dynamic weighing and real-time grate analyzing method |
| CN102654463A (en) * | 2012-04-13 | 2012-09-05 | 北京农业信息技术研究中心 | Watermelon quality NDT (non-destructive testing) method and device |
| CN105203778A (en) * | 2014-06-12 | 2015-12-30 | 北京干山科技有限公司 | Three-dimensional reconstruction type ore grade analyzing device and method |
| CN105067480A (en) * | 2015-07-31 | 2015-11-18 | 陕西四维衡器科技有限公司 | Green channel cargo type detection method and detection equipment thereof |
| CN105806743A (en) * | 2016-04-28 | 2016-07-27 | 西北农林科技大学 | Multi-view apple moldy core detection device and method |
| CN107084780A (en) * | 2017-05-12 | 2017-08-22 | 智锐达仪器科技南通有限公司 | A kind of intelligent electronic-scale and corresponding Weighing method |
| CN108097603A (en) * | 2017-12-06 | 2018-06-01 | 中国计量大学 | The shaddock quality method for separating of view-based access control model technology |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111735735A (en) * | 2020-06-11 | 2020-10-02 | 山东滨州烟草有限公司 | Intelligent cigarette package nondestructive identification method and identifier |
| CN111735735B (en) * | 2020-06-11 | 2022-05-13 | 山东滨州烟草有限公司 | Intelligent cigarette package nondestructive identification method and identifier |
| CN112665698A (en) * | 2020-12-15 | 2021-04-16 | 重庆电子工程职业学院 | Intelligent electronic scale |
| CN113640177A (en) * | 2021-06-29 | 2021-11-12 | 阿里巴巴新加坡控股有限公司 | Cargo density measuring method and system and electronic equipment |
| CN114608692A (en) * | 2022-01-22 | 2022-06-10 | 贵州省检测技术研究应用中心 | Method and system for automatically weighing inspection and detection samples |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109141234A (en) | A kind of intelligent article recognition methods and device | |
| CN108491799B (en) | Intelligent sales counter commodity management method and system based on image recognition | |
| US11638490B2 (en) | Method and device for identifying product purchased by user and intelligent shelf system | |
| WO2021012644A1 (en) | Shelf commodity detection method and system | |
| CN102567502B (en) | Biometric identification is supplemented with device identification | |
| WO2019057168A1 (en) | Goods order processing method and apparatus, server, shopping terminal, and system | |
| CN109166007A (en) | A kind of Method of Commodity Recommendation and its device based on automatic vending machine | |
| US20190147614A1 (en) | Classification and identification systems and methods | |
| CN108460908A (en) | Automatic vending method and system and automatic vending device and automatic vending machine | |
| WO2021042698A1 (en) | Vision- and gravity sensing-based product identification method, device, and system | |
| CN111709687A (en) | Article warehousing system and article warehousing method | |
| CN110363626B (en) | Behavior mode monitoring method for unmanned container, electronic equipment and unmanned container | |
| WO2018002864A2 (en) | Shopping cart-integrated system and method for automatic identification of products | |
| CN108922029A (en) | Vending equipment and its control method | |
| Megherbi et al. | A classifier based approach for the detection of potential threats in CT based baggage screening | |
| Megherbi et al. | A comparison of classification approaches for threat detection in CT based baggage screening | |
| CN111382762A (en) | Empty box identification method and system | |
| CN108724178A (en) | The autonomous follower method of particular person and device, robot, equipment and storage medium | |
| CN108831073A (en) | unmanned supermarket system | |
| Gothai et al. | Design features of grocery product recognition using deep learning | |
| CN109712324A (en) | A kind of automatic vending machine image-recognizing method, good selling method and vending equipment | |
| CN110826481A (en) | Data processing method, commodity identification method, server and storage medium | |
| CN109300265A (en) | Unmanned Supermarket Management System | |
| JP2023504871A (en) | Fraud detection system and method | |
| CN110765815B (en) | Display rack shielding detection method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190104 |