CN110610183A - Grain evaluation method, grain evaluation device, and storage medium - Google Patents
Grain evaluation method, grain evaluation device, and storage medium Download PDFInfo
- Publication number
- CN110610183A CN110610183A CN201810621682.6A CN201810621682A CN110610183A CN 110610183 A CN110610183 A CN 110610183A CN 201810621682 A CN201810621682 A CN 201810621682A CN 110610183 A CN110610183 A CN 110610183A
- Authority
- CN
- China
- Prior art keywords
- image data
- feature
- evaluated
- grain
- enhanced image
- 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
- 238000011156 evaluation Methods 0.000 title claims abstract description 46
- 238000013209 evaluation strategy Methods 0.000 claims abstract description 33
- 238000012545 processing Methods 0.000 claims description 68
- 238000003708 edge detection Methods 0.000 claims description 38
- 238000000034 method Methods 0.000 claims description 34
- 238000004590 computer program Methods 0.000 claims description 22
- 238000012549 training Methods 0.000 claims description 10
- 239000000126 substance Substances 0.000 claims description 4
- 235000013339 cereals Nutrition 0.000 description 119
- 238000010411 cooking Methods 0.000 description 20
- 241000209094 Oryza Species 0.000 description 8
- 235000007164 Oryza sativa Nutrition 0.000 description 8
- 235000009566 rice Nutrition 0.000 description 8
- 238000004891 communication Methods 0.000 description 3
- 238000013473 artificial intelligence Methods 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000013135 deep learning Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 238000007781 pre-processing Methods 0.000 description 2
- 244000062793 Sorghum vulgare Species 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 235000005911 diet Nutrition 0.000 description 1
- 230000037213 diet Effects 0.000 description 1
- 235000019800 disodium phosphate Nutrition 0.000 description 1
- 235000006694 eating habits Nutrition 0.000 description 1
- 238000010353 genetic engineering Methods 0.000 description 1
- 238000010438 heat treatment Methods 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 235000019713 millet Nutrition 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000000877 morphologic effect Effects 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 238000007639 printing Methods 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/764—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/60—Type of objects
Abstract
The invention discloses a grain evaluation method, which comprises the following steps: acquiring first image data including grains to be evaluated; obtaining a first recognition result based on the first image data and a first recognition model, wherein the first recognition result represents the category of the grain to be evaluated; and evaluating the grains to be evaluated according to the first image data and an evaluation strategy corresponding to the categories of the grains to be evaluated to obtain an evaluation result. The invention also discloses a grain evaluation device and a storage medium.
Description
Technical Field
The present invention relates to image recognition technology, and in particular, to a grain assessment method, apparatus, and computer-readable storage medium.
Background
At present, artificial intelligence is a challenging subject of a gate electrode, comprises very extensive science and consists of different fields such as machine learning, computer vision, bioscience, neural network science, energy technology, genetic engineering and the like, and the main purpose of artificial intelligence research is to enable a machine to execute complex work which can be completed only by human intelligence. Along with the continuous improvement of the life quality of people, the requirements on diet daily life are also naturally improved. The rice cooker can cook, and cooking devices with different functions and prices have different feelings of cooking, so that the requirement for correcting the cooking effect of grains is provided for the cooking devices. Before the cooking device is optimized, what kind of rice meets the eating habits of most users needs to be grasped, so that a method capable of evaluating the quality and taste of grains needs to be provided.
Disclosure of Invention
In order to solve the existing technical problems, embodiments of the present invention provide a grain assessment method, apparatus, and computer-readable storage medium.
The technical scheme of the invention is realized as follows:
the embodiment of the invention provides a grain evaluation method, which comprises the following steps:
acquiring first image data including grains to be evaluated;
obtaining a first recognition result based on the first image data and a first recognition model, wherein the first recognition result represents the category of the grain to be evaluated;
and evaluating the grains to be evaluated according to the first image data and an evaluation strategy corresponding to the categories of the grains to be evaluated to obtain an evaluation result.
In the above scheme, the method further comprises: performing feature enhancement processing on the first image data to obtain feature enhanced image data corresponding to the first image data;
correspondingly, the obtaining a first recognition result based on the first image data and the first recognition model includes: and obtaining a first recognition result based on the feature enhanced image data corresponding to the first image data and the first recognition model.
In the foregoing solution, the performing feature enhancement processing on the first image data to obtain feature enhanced image data corresponding to the first image data includes:
converting the first image data into a gray image, and performing contrast enhancement processing on the gray image to obtain contrast enhanced image data of the first image data; performing edge detection on the first image data to obtain edge detection image data;
and obtaining feature enhanced image data corresponding to the first image data based on the contrast enhanced image data of the first image data and the edge detection image data.
In the above scheme, the method further comprises:
obtaining a plurality of second image information; the second image information comprises second image data and corresponding label data; the tag data characterizes the category to which the grain belongs;
performing feature enhancement processing on the second image data to obtain feature enhanced image data corresponding to the second image data;
performing data enhancement processing on the feature enhanced image data corresponding to the second image data to obtain data enhanced image data;
and performing learning training based on the feature enhanced image data and/or the data enhanced image data and corresponding label data to obtain the first recognition model.
In the foregoing solution, the performing data enhancement processing on the feature-enhanced image data corresponding to the second image data to obtain data-enhanced image data includes:
and rotating and/or flipping the feature enhanced image data corresponding to the second image data to obtain flipped image data and/or rotated image data corresponding to the feature enhanced image data, and generating data enhanced image data based on the flipped image data and/or rotated image data.
In the above scheme, the evaluating the grain to be evaluated according to the first image data and the evaluation strategy corresponding to the category of the grain to be evaluated includes:
acquiring a corresponding evaluation strategy according to the category of the grains to be evaluated;
and evaluating the grains to be evaluated according to the evaluation strategy and the feature enhanced image data corresponding to the first image data.
An embodiment of the present invention further provides a grain evaluation device, including: the device comprises an acquisition module and a processing module; wherein the content of the first and second substances,
the acquisition module is used for acquiring first image data including grains to be evaluated;
the processing module is used for obtaining a first identification result based on the first image data and a first identification model, and the first identification result represents the category of the grain to be evaluated;
and evaluating the grains to be evaluated according to the first image data and an evaluation strategy corresponding to the categories of the grains to be evaluated to obtain an evaluation result.
In the foregoing solution, the processing module is further configured to perform feature enhancement processing on the first image data to obtain feature enhanced image data corresponding to the first image data;
correspondingly, the obtaining a first recognition result based on the first image data and the first recognition model includes: and obtaining a first recognition result based on the feature enhanced image data corresponding to the first image data and the first recognition model.
In the foregoing solution, the processing module is specifically configured to convert the first image data into a grayscale image, and perform contrast enhancement processing on the grayscale image to obtain contrast-enhanced image data of the first image data; performing edge detection on the first image data to obtain edge detection image data;
and obtaining feature enhanced image data corresponding to the first image data based on the contrast enhanced image data of the first image data and the edge detection image data.
In the above scheme, the processing module is further configured to obtain a plurality of second image information; the second image information comprises second image data and corresponding label data; the tag data characterizes the category to which the grain belongs;
performing feature enhancement processing on the second image data to obtain feature enhanced image data corresponding to the second image data;
performing data enhancement processing on the feature enhanced image data corresponding to the second image data to obtain data enhanced image data;
and performing learning training based on the feature enhanced image data and/or the data enhanced image data and corresponding label data to obtain the first recognition model.
In the foregoing solution, the processing module is specifically configured to rotate and/or flip feature enhanced image data corresponding to the second image data, obtain flipped image data and/or rotated image data corresponding to the feature enhanced image data, and generate data enhanced image data based on the flipped image data and/or the rotated image data.
In the above scheme, the processing module is specifically configured to obtain a corresponding evaluation strategy according to the category of the grain to be evaluated;
and evaluating the grains to be evaluated according to the evaluation strategy and the feature enhanced image data corresponding to the first image data.
An embodiment of the present invention further provides a grain evaluation device, including: a processor and a memory for storing a computer program capable of running on the processor;
wherein the processor is configured to execute the steps of any of the grain assessment methods described above when the computer program is run.
Embodiments of the present invention further provide a computer-readable storage medium, on which a computer program is stored, the computer program, when executed by a processor, implementing the steps of any of the grain assessment methods described above.
The grain evaluation method, the grain evaluation device and the computer-readable storage medium provided by the embodiment of the invention are used for acquiring first image data of grains to be evaluated; obtaining a first recognition result based on the first image data and a first recognition model, wherein the first recognition result represents the category of the grain to be evaluated; and evaluating the grains to be evaluated according to the first image data and an evaluation strategy corresponding to the categories of the grains to be evaluated to obtain an evaluation result. In the scheme of the embodiment of the invention, the image of the grain is collected and subjected to feature enhancement processing, the grain is accurately identified according to the image subjected to the feature enhancement processing, the evaluation result of the grain is obtained according to the grain identification result and the image subjected to the enhancement processing, and the evaluation result reflects the quality and the taste of the grain, so that the cooking curve of the cooking equipment can be corrected according to the evaluation result, and the grain with better cooking outlet feeling is cooked by a user.
Drawings
FIG. 1 is a schematic flow chart of a grain evaluation method according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of another grain assessment method according to an embodiment of the present invention;
FIG. 3 is a schematic structural diagram of a grain evaluation apparatus according to an embodiment of the present invention;
fig. 4 is a schematic structural view of another grain evaluation apparatus according to an embodiment of the present invention.
Detailed Description
In various embodiments of the invention, first image data comprising grain to be evaluated is acquired; obtaining a first recognition result based on the first image data and a first recognition model, wherein the first recognition result represents the category of the grain to be evaluated; and evaluating the grains to be evaluated according to the first image data and an evaluation strategy corresponding to the categories of the grains to be evaluated to obtain an evaluation result.
The present invention will be described in further detail with reference to examples.
FIG. 1 is a schematic flow chart of a grain evaluation method according to an embodiment of the present invention; as shown in fig. 1, the method includes:
step 101, acquiring first image data including grains to be evaluated;
102, obtaining a first identification result based on the first image data and a first identification model, wherein the first identification result represents the category of the grain to be evaluated;
103, evaluating the grains to be evaluated according to the first image data and an evaluation strategy corresponding to the categories of the grains to be evaluated to obtain an evaluation result.
In this embodiment, the first image data includes grains to be evaluated, such as rice, millet, and the like.
The grain assessment method can be applied to equipment; as a first implementation manner, the device may be a cooking device, the cooking device is provided with an image acquisition component (such as a camera), image data is acquired through the image acquisition component, the acquired image data is analyzed and identified, and the category to which the grain to be evaluated belongs is determined; as a second implementation manner, the device may be a cooking device, the cooking device does not have an image capturing function, the cooking device may communicate with another device having an image capturing component, image data is captured through the image capturing component of the other device, the cooking device obtains the image data captured by the other device through a communication link with the other device, and analyzes and identifies the captured image data to determine the category to which the grain to be evaluated belongs; as a third implementation manner, the device may be an electronic device, and the electronic device may be a mobile device, such as a mobile phone, a tablet computer, and the like, and the electronic device acquires image data, analyzes and identifies the acquired image data, and determines a category to which the grain to be evaluated belongs. In practical application, the cooking equipment can be kitchen heating equipment such as an electric cooker, an electric pressure cooker and the like.
In this embodiment, the grain assessment method further comprises: performing feature enhancement processing on the first image data to obtain feature enhanced image data corresponding to the first image data;
correspondingly, the obtaining a first recognition result based on the first image data and the first recognition model includes: and obtaining a first recognition result based on the feature enhanced image data corresponding to the first image data and the first recognition model.
In this embodiment, different from other object recognition tasks, in consideration of the fact that the color space of the image data of grains is special, the image data obtained through processing has almost no color information, that is: after the first image data is converted into the gray-scale image, morphological features of grains are not obvious, and the classification effect is not good, so in the embodiment of the invention, feature enhancement is performed on the gray-scale image after the first image data is converted, and mainly, enhancement processing is performed on the contrast of the gray-scale image. The contrast represents the measurement of different brightness levels between the brightest pixel point and the darkest pixel point in the image data, the larger the difference range is, the larger the contrast is, and the smaller the difference range is, the smaller the contrast is. Here, a contrast enhancement algorithm may be employed to enhance the contrast of the image data, especially when the contrast of the useful data of the image data is relatively close. The difference between different rice grains is more obvious, and the light transmission degree of different rice grains can be reflected. The contrast enhancement algorithm includes, but is not limited to, at least one of the following algorithms: linear transformation algorithms, exponential change algorithms, logarithmic change algorithms, histogram algorithms, and the like.
Specifically, the performing the feature enhancement processing on the first image data to obtain the feature enhanced image data corresponding to the first image data includes:
converting the first image data into a gray image, and performing contrast enhancement processing on the gray image to obtain contrast enhanced image data of the first image data; performing edge detection on the first image data to obtain edge detection image data;
and obtaining feature enhanced image data corresponding to the first image data based on the contrast enhanced image data of the first image data and the edge detection image data.
Here, through edge detection, the profile information of cereal that can be better obtains makes the difference between the different rice grains more obvious, can reflect the printing opacity degree of different rice grains. The edge detection algorithm employed includes, but is not limited to, at least one of the following: roberts edge detection algorithm, Sobel edge detection algorithm, Prewitt edge detection algorithm, Canny edge detection algorithm, Laplacian edge detection algorithm, Log edge detection algorithm, and second-order directional derivative isooperator detection method.
In this embodiment, the first recognition model is obtained by a manufacturer of the cooking device by using a learning training method in advance and stored in the cooking device.
Here, the grain evaluation method may further include: the method for obtaining the first recognition model through learning training specifically comprises the following steps:
step 001: obtaining a plurality of second image information; the second image information comprises second image data and corresponding label data; the label data characterizes the class to which the grain belongs.
Step 002: and performing feature enhancement processing on the second image data to obtain feature enhanced image data corresponding to the second image data.
Step 003: and performing data enhancement processing on the feature enhanced image data corresponding to the second image data to obtain data enhanced image data.
Step 004: and performing learning training based on the feature enhanced image data and/or the data enhanced image data and corresponding label data to obtain the first recognition model.
Specifically, the performing the feature enhancement processing on the second image data to obtain the feature enhanced image data may include:
converting the second image data into a gray image, and performing contrast enhancement processing on the gray image to obtain contrast enhanced image data of the second image data; performing edge detection on the second image data to obtain edge detection image data; and obtaining feature enhanced image data corresponding to the second image data based on the contrast enhanced image data and the edge detection image data of the second image data.
Specifically, the performing data enhancement processing on the feature enhanced image data corresponding to the second image data to obtain data enhanced image data may include:
and turning and/or rotating the feature enhanced image data corresponding to the second image data to obtain turned image data and/or rotated image data corresponding to the feature enhanced image data, and generating data enhanced image data based on the turned image data and/or the rotated image data.
The feature enhancement image data is rotated, the angle of rotation may be a first preset angle, and the first preset angle is one of the following angles: 90 degrees, 180 degrees, 270 degrees; turning the feature enhanced image data, further rotating the turned feature enhanced image data, wherein the rotation angle can be a second preset angle, and the second preset angle is one of the following angles: 90 degrees, 180 degrees, 270 degrees.
In this embodiment, the device pre-stores an evaluation policy corresponding to at least one grain category;
the evaluating the grain to be evaluated according to the first image data and the evaluation strategy corresponding to the category of the grain to be evaluated to obtain an evaluation result, and the evaluating method comprises the following steps:
acquiring a corresponding evaluation strategy according to the category of the grains to be evaluated;
and evaluating the grains to be evaluated according to the evaluation strategy and the feature enhancement image data corresponding to the first image data, and determining the evaluation result of the grains to be evaluated.
Here, the evaluation strategy, which may be evaluated for quality, taste, score, etc. of different types of grains, is preset by the manufacturer of the cooking appliance and stored in the appliance.
FIG. 2 is a schematic flow chart of another grain assessment method according to an embodiment of the present invention; as shown in fig. 2, the method includes:
step 201, acquiring first image data including grains to be evaluated;
here, the grain to be evaluated means grain after cooking, such as boiled rice.
Step 202, preprocessing the first image data;
here, the preprocessing the first image data includes:
converting the first image data into a gray image, and performing contrast enhancement processing on the gray image to obtain contrast enhanced image data of the first image data; performing edge detection on the first image data to obtain edge detection image data;
and obtaining feature enhanced image data corresponding to the first image data based on the contrast enhanced image data of the first image data and the edge detection image data.
Step 203, identifying grain categories by using a deep learning image classifier;
here, the identifying the grain class by using the deep learning image classifier includes:
and obtaining a first identification result based on the feature enhanced image data corresponding to the first image data and a first identification model, wherein the first identification result represents the category of the grain to be evaluated.
Step 204, evaluating the grains according to the grain types and the preprocessed images;
here, the grain evaluation based on the grain category and the pre-processed image includes:
acquiring a corresponding evaluation strategy according to the category of the grains to be evaluated;
and evaluating the discreteness, the compactness among particles, the glossiness and the like of the grains to be evaluated according to the evaluation strategy and the feature enhanced image data corresponding to the first image data, so as to obtain evaluation results aiming at the quality, the mouthfeel, the evaluation and the like of the grains to be evaluated.
And step 205, obtaining an evaluation result of the grains.
FIG. 3 is a schematic structural diagram of a grain evaluation apparatus according to an embodiment of the present invention; as shown in fig. 3, the apparatus includes: an acquisition module 301 and a processing module 302; wherein the content of the first and second substances,
the acquisition module 301 is configured to acquire first image data including grains to be evaluated;
the processing module 302 is configured to obtain a first recognition result based on the first image data and a first recognition model, where the first recognition result represents a category of the grain to be evaluated;
and evaluating the grains to be evaluated according to the first image data and an evaluation strategy corresponding to the categories of the grains to be evaluated to obtain an evaluation result.
Specifically, the processing module 302 is further configured to perform feature enhancement processing on the first image data to obtain feature enhanced image data corresponding to the first image data;
correspondingly, the obtaining a first recognition result based on the first image data and the first recognition model includes: and obtaining a first recognition result based on the feature enhanced image data corresponding to the first image data and the first recognition model.
Specifically, the processing module 302 is specifically configured to convert the first image data into a grayscale image, and perform contrast enhancement processing on the grayscale image to obtain contrast-enhanced image data of the first image data; performing edge detection on the first image data to obtain edge detection image data;
and obtaining feature enhanced image data corresponding to the first image data based on the contrast enhanced image data of the first image data and the edge detection image data.
Specifically, the processing module 302 is further configured to obtain a plurality of second image information; the second image information comprises second image data and corresponding label data; the tag data characterizes the category to which the grain belongs;
performing feature enhancement processing on the second image data to obtain feature enhanced image data corresponding to the second image data;
performing data enhancement processing on the feature enhanced image data corresponding to the second image data to obtain data enhanced image data;
and performing learning training based on the feature enhanced image data and/or the data enhanced image data and the corresponding label data to obtain a first recognition model.
Specifically, the processing module 302 is specifically configured to rotate and/or flip feature enhanced image data corresponding to the second image data, obtain flipped image data and/or rotated image data corresponding to the feature enhanced image data, and generate data enhanced image data based on the flipped image data and/or the rotated image data.
Here, the feature enhanced image data is rotated by a first preset angle, and the first preset angle is one of the following angles: 90 degrees, 180 degrees, 270 degrees; turning the feature enhanced image data, further rotating the turned feature enhanced image data, wherein the rotation angle can be a second preset angle, and the second preset angle is one of the following angles: 90 degrees, 180 degrees, 270 degrees.
Specifically, the processing module 302 is specifically configured to obtain a corresponding evaluation strategy according to the category of the grain to be evaluated;
and evaluating the grains to be evaluated according to the evaluation strategy and the feature enhancement image data corresponding to the first image data, and determining the evaluation result of the grains to be evaluated.
To implement the method of the embodiment of the present invention, another grain evaluation device is provided in a cooking apparatus or a mobile terminal, and specifically, as shown in fig. 4, the device 40 includes:
a processor 401 and a memory 402 for storing computer programs executable on said processor; wherein the content of the first and second substances,
the processor 401 is configured to, when running the computer program, perform:
acquiring first image data including grains to be evaluated;
obtaining a first recognition result based on the first image data and a first recognition model, wherein the first recognition result represents the category of the grain to be evaluated;
and evaluating the grains to be evaluated according to the first image data and an evaluation strategy corresponding to the categories of the grains to be evaluated to obtain an evaluation result.
In an embodiment, the processor 401, when running the computer program, is configured to perform:
performing feature enhancement processing on the first image data to obtain feature enhanced image data corresponding to the first image data;
correspondingly, the obtaining a first recognition result based on the first image data and the first recognition model includes: and obtaining a first recognition result based on the feature enhanced image data corresponding to the first image data and the first recognition model.
In an embodiment, the processor 401, when running the computer program, is configured to perform:
converting the first image data into a gray image, and performing contrast enhancement processing on the gray image to obtain contrast enhanced image data of the first image data; performing edge detection on the first image data to obtain edge detection image data;
and obtaining feature enhanced image data corresponding to the first image data based on the contrast enhanced image data of the first image data and the edge detection image data.
In an embodiment, the processor 401, when running the computer program, is configured to perform:
obtaining a plurality of second image information; the second image information comprises second image data and corresponding label data; the tag data characterizes the category to which the grain belongs;
performing feature enhancement processing on the second image data to obtain feature enhanced image data corresponding to the second image data;
performing data enhancement processing on the feature enhanced image data corresponding to the second image data to obtain data enhanced image data;
and performing learning training based on the feature enhanced image data and/or the data enhanced image data and the corresponding label data to obtain a first recognition model.
In an embodiment, the processor 401, when running the computer program, is configured to perform:
and rotating and/or flipping the feature enhanced image data corresponding to the second image data to obtain flipped image data and/or rotated image data corresponding to the feature enhanced image data, and generating data enhanced image data based on the flipped image data and/or rotated image data.
In an embodiment, the processor 401, when running the computer program, is configured to perform:
acquiring a corresponding evaluation strategy according to the category of the grains to be evaluated;
and evaluating the grains to be evaluated according to the evaluation strategy and the feature enhancement image data corresponding to the first image data, and determining the evaluation result of the grains to be evaluated.
It should be noted that: the grain evaluation device provided by the above embodiment and the grain evaluation method embodiment belong to the same concept, and the specific implementation process thereof is described in the method embodiment, which is not described herein again.
Of course, in practical applications, as shown in fig. 4, the apparatus 40 may further include: at least one network interface 403. The various components of the grain evaluation device 40 are coupled together by a bus system 404. It is understood that the bus system 404 is used to enable communications among the components. The bus system 404 includes a power bus, a control bus, and a status signal bus in addition to a data bus. For clarity of illustration, however, the various buses are labeled as bus system 404 in FIG. 4. The number of the processors 404 may be at least one. The network interface 403 is used for wired or wireless communication between the grain evaluation device 40 and other apparatuses. Memory 402 in embodiments of the present invention is used to store various types of data to support the operation of device 40.
The method disclosed in the above embodiments of the present invention may be applied to the processor 401, or implemented by the processor 401. The processor 401 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 401. The Processor 401 described above may be a general purpose Processor, a Digital Signal Processor (DSP), or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like. Processor 401 may implement or perform the methods, steps, and logic blocks disclosed in embodiments of the present invention. A general purpose processor may be a microprocessor or any conventional processor or the like. The steps of the method disclosed by the embodiment of the invention can be directly implemented by a hardware decoding processor, or can be implemented by combining hardware and software modules in the decoding processor. The software modules may be located in a storage medium located in the memory 402, and the processor 401 reads the information in the memory 402 and performs the steps of the aforementioned methods in conjunction with its hardware.
In an exemplary embodiment, the grain-evaluating Device 40 may be implemented by one or more Application Specific Integrated Circuits (ASICs), DSPs, Programmable Logic Devices (PLDs), Complex Programmable Logic Devices (CPLDs), Field Programmable Gate Arrays (FPGAs), general purpose processors, controllers, Micro Controllers (MCUs), microprocessors (microprocessors), or other electronic components for performing the aforementioned methods.
Specifically, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the computer program performs:
acquiring first image data including grains to be evaluated;
obtaining a first recognition result based on the first image data and a first recognition model, wherein the first recognition result represents the category of the grain to be evaluated;
and evaluating the grains to be evaluated according to the evaluation strategy corresponding to the categories of the grains to be evaluated to obtain an evaluation result.
In one embodiment, the computer program, when executed by the processor, performs:
performing feature enhancement processing on the first image data to obtain feature enhanced image data corresponding to the first image data;
correspondingly, the obtaining a first recognition result based on the first image data and the first recognition model includes: and obtaining a first recognition result based on the feature enhanced image data corresponding to the first image data and the first recognition model.
In one embodiment, the computer program, when executed by the processor, performs:
converting the first image data into a gray image, and performing contrast enhancement processing on the gray image to obtain contrast enhanced image data of the first image data; performing edge detection on the first image data to obtain edge detection image data;
and obtaining feature enhanced image data corresponding to the first image data based on the contrast enhanced image data of the first image data and the edge detection image data.
In one embodiment, the computer program, when executed by the processor, performs:
obtaining a plurality of second image information; the second image information comprises second image data and corresponding label data; the tag data characterizes the category to which the grain belongs;
performing feature enhancement processing on the second image data to obtain feature enhanced image data corresponding to the second image data;
performing data enhancement processing on the feature enhanced image data corresponding to the second image data to obtain data enhanced image data;
and performing learning training based on the feature enhanced image data and/or the data enhanced image data and the corresponding label data to obtain a first recognition model.
In one embodiment, the computer program, when executed by the processor, performs:
and rotating and/or flipping the feature enhanced image data corresponding to the second image data to obtain flipped image data and/or rotated image data corresponding to the feature enhanced image data, and generating data enhanced image data based on the flipped image data and/or rotated image data.
In one embodiment, the computer program, when executed by the processor, performs:
acquiring a corresponding evaluation strategy according to the category of the grains to be evaluated;
and evaluating the grains to be evaluated according to the evaluation strategy and the feature enhancement image data corresponding to the first image data, and determining the evaluation result of the grains to be evaluated.
The above description is only exemplary of the present invention and should not be taken as limiting the scope of the present invention, and any modifications, equivalents, improvements, etc. that are within the spirit and principle of the present invention should be included in the present invention.
Claims (14)
1. A method of grain assessment, the method comprising:
acquiring first image data including grains to be evaluated;
obtaining a first recognition result based on the first image data and a first recognition model, wherein the first recognition result represents the category of the grain to be evaluated;
and evaluating the grains to be evaluated according to the first image data and an evaluation strategy corresponding to the categories of the grains to be evaluated to obtain an evaluation result.
2. The method of claim 1, further comprising: performing feature enhancement processing on the first image data to obtain feature enhanced image data corresponding to the first image data;
correspondingly, the obtaining a first recognition result based on the first image data and the first recognition model includes: and obtaining a first recognition result based on the feature enhanced image data corresponding to the first image data and the first recognition model.
3. The method according to claim 2, wherein the performing the feature enhancement processing on the first image data to obtain feature enhanced image data corresponding to the first image data includes:
converting the first image data into a gray image, and performing contrast enhancement processing on the gray image to obtain contrast enhanced image data of the first image data; performing edge detection on the first image data to obtain edge detection image data;
and obtaining feature enhanced image data corresponding to the first image data based on the contrast enhanced image data of the first image data and the edge detection image data.
4. The method of claim 1, further comprising:
obtaining a plurality of second image information; the second image information comprises second image data and corresponding label data; the tag data characterizes the category to which the grain belongs;
performing feature enhancement processing on the second image data to obtain feature enhanced image data corresponding to the second image data;
performing data enhancement processing on the feature enhanced image data corresponding to the second image data to obtain data enhanced image data;
and performing learning training based on the feature enhanced image data and/or the data enhanced image data and corresponding label data to obtain the first recognition model.
5. The method according to claim 4, wherein performing data enhancement processing on the feature enhanced image data corresponding to the second image data to obtain data enhanced image data comprises:
and rotating and/or flipping the feature enhanced image data corresponding to the second image data to obtain flipped image data and/or rotated image data corresponding to the feature enhanced image data, and generating data enhanced image data based on the flipped image data and/or rotated image data.
6. The method of claim 2, wherein said evaluating the grain to be evaluated according to the evaluation strategy corresponding to the first image data and the category of the grain to be evaluated comprises:
acquiring a corresponding evaluation strategy according to the category of the grains to be evaluated;
and evaluating the grains to be evaluated according to the evaluation strategy and the feature enhanced image data corresponding to the first image data.
7. A grain assessment apparatus, said apparatus comprising: the device comprises an acquisition module and a processing module; wherein the content of the first and second substances,
the acquisition module is used for acquiring first image data including grains to be evaluated;
the processing module is used for obtaining a first identification result based on the first image data and a first identification model, and the first identification result represents the category of the grain to be evaluated;
and evaluating the grains to be evaluated according to the first image data and an evaluation strategy corresponding to the categories of the grains to be evaluated to obtain an evaluation result.
8. The apparatus according to claim 7, wherein the processing module is further configured to perform feature enhancement processing on the first image data to obtain feature enhanced image data corresponding to the first image data;
correspondingly, the obtaining a first recognition result based on the first image data and the first recognition model includes: and obtaining a first recognition result based on the feature enhanced image data corresponding to the first image data and the first recognition model.
9. The apparatus according to claim 8, wherein the processing module is specifically configured to convert the first image data into a grayscale image, and perform contrast enhancement processing on the grayscale image to obtain contrast-enhanced image data of the first image data; performing edge detection on the first image data to obtain edge detection image data;
and obtaining feature enhanced image data corresponding to the first image data based on the contrast enhanced image data of the first image data and the edge detection image data.
10. The apparatus of claim 7, wherein the processing module is further configured to obtain a plurality of second image information; the second image information comprises second image data and corresponding label data; the tag data characterizes the category to which the grain belongs;
performing feature enhancement processing on the second image data to obtain feature enhanced image data corresponding to the second image data;
performing data enhancement processing on the feature enhanced image data corresponding to the second image data to obtain data enhanced image data;
and performing learning training based on the feature enhanced image data and/or the data enhanced image data and corresponding label data to obtain the first recognition model.
11. The apparatus according to claim 10, wherein the processing module is specifically configured to rotate and/or flip feature-enhanced image data corresponding to the second image data, obtain flipped image data and/or rotated image data corresponding to the feature-enhanced image data, and generate data-enhanced image data based on the flipped image data and/or rotated image data.
12. The device according to claim 8, wherein the processing module is specifically configured to obtain a corresponding evaluation strategy according to the category of the grain to be evaluated;
and evaluating the grains to be evaluated according to the evaluation strategy and the feature enhanced image data corresponding to the first image data.
13. A grain assessment apparatus, said apparatus comprising: a processor and a memory for storing a computer program capable of running on the processor;
wherein the processor is adapted to perform the steps of the method of any one of claims 1 to 6 when running the computer program.
14. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 6.
Priority Applications (4)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810621682.6A CN110610183A (en) | 2018-06-15 | 2018-06-15 | Grain evaluation method, grain evaluation device, and storage medium |
| JP2020569744A JP2021526646A (en) | 2018-06-15 | 2019-06-14 | Grain evaluation method, equipment and storage medium |
| PCT/CN2019/091399 WO2019238130A1 (en) | 2018-06-15 | 2019-06-14 | Grain evaluation method and device and storage medium |
| KR1020217000926A KR102453207B1 (en) | 2018-06-15 | 2019-06-14 | Grain evaluation methods, devices and storage media |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810621682.6A CN110610183A (en) | 2018-06-15 | 2018-06-15 | Grain evaluation method, grain evaluation device, and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110610183A true CN110610183A (en) | 2019-12-24 |
Family
ID=68841744
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810621682.6A Pending CN110610183A (en) | 2018-06-15 | 2018-06-15 | Grain evaluation method, grain evaluation device, and storage medium |
Country Status (4)
| Country | Link |
|---|---|
| JP (1) | JP2021526646A (en) |
| KR (1) | KR102453207B1 (en) |
| CN (1) | CN110610183A (en) |
| WO (1) | WO2019238130A1 (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111579724A (en) * | 2020-06-01 | 2020-08-25 | 中国标准化研究院 | Rapid classification method and device for sensory sensitivity of tingling and peppery suprathreshold and application |
| CN114486877B (en) * | 2020-10-23 | 2024-01-19 | 合肥美亚光电技术股份有限公司 | Rice quality detection method, rice quality detection device and storage medium |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111879735B (en) * | 2020-07-22 | 2021-11-09 | 武汉大学 | Rice appearance quality detection method based on image |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101234381A (en) * | 2008-03-07 | 2008-08-06 | 天津市华核科技有限公司 | Granular material sorting classifying method based on visual sense recognition |
| CN101271522A (en) * | 2008-05-21 | 2008-09-24 | 中国农业大学 | Automatic recognition method for yellow-colored rice in rice |
| JP2012137458A (en) * | 2010-12-28 | 2012-07-19 | National Agriculture & Food Research Organization | Harvest quality forecasting system and harvest quality forecasting method using picked-up image of cross section of rice to be harvested |
| JP2016062524A (en) * | 2014-09-22 | 2016-04-25 | 日本電気株式会社 | Data processing system, data processing method and data processing program |
| CN106198442A (en) * | 2016-09-14 | 2016-12-07 | 南京财经大学 | A kind of spectroscopic analysis methods differentiating Panjin rice |
| CN107951485A (en) * | 2017-11-27 | 2018-04-24 | 乐普（北京）医疗器械股份有限公司 | Ambulatory ECG analysis method and apparatus based on artificial intelligence self study |
| WO2018084612A1 (en) * | 2016-11-02 | 2018-05-11 | 한국식품연구원 | System for measuring quality of rice, method for evaluating palatability of rice, system for predicting germination rate of grain and method for predicting germination rate |
| CN108108767A (en) * | 2017-12-29 | 2018-06-01 | 美的集团股份有限公司 | A kind of cereal recognition methods, device and computer storage media |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP4402915B2 (en) * | 2003-08-01 | 2010-01-20 | セイレイ工業株式会社 | Grain sorting device |
| CN101398886B (en) * | 2008-03-17 | 2010-11-10 | 杭州大清智能技术开发有限公司 | Rapid three-dimensional face identification method based on bi-eye passiveness stereo vision |
| CN101701916B (en) * | 2009-12-01 | 2011-05-18 | 中国农业大学 | Method for quickly identifying and distinguishing variety of corn |
| CN102436589B (en) * | 2010-09-29 | 2015-05-06 | 中国科学院电子学研究所 | Complex object automatic recognition method based on multi-category primitive self-learning |
-
2018
- 2018-06-15 CN CN201810621682.6A patent/CN110610183A/en active Pending
-
2019
- 2019-06-14 WO PCT/CN2019/091399 patent/WO2019238130A1/en active Application Filing
- 2019-06-14 KR KR1020217000926A patent/KR102453207B1/en active IP Right Grant
- 2019-06-14 JP JP2020569744A patent/JP2021526646A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101234381A (en) * | 2008-03-07 | 2008-08-06 | 天津市华核科技有限公司 | Granular material sorting classifying method based on visual sense recognition |
| CN101271522A (en) * | 2008-05-21 | 2008-09-24 | 中国农业大学 | Automatic recognition method for yellow-colored rice in rice |
| JP2012137458A (en) * | 2010-12-28 | 2012-07-19 | National Agriculture & Food Research Organization | Harvest quality forecasting system and harvest quality forecasting method using picked-up image of cross section of rice to be harvested |
| JP2016062524A (en) * | 2014-09-22 | 2016-04-25 | 日本電気株式会社 | Data processing system, data processing method and data processing program |
| CN106198442A (en) * | 2016-09-14 | 2016-12-07 | 南京财经大学 | A kind of spectroscopic analysis methods differentiating Panjin rice |
| WO2018084612A1 (en) * | 2016-11-02 | 2018-05-11 | 한국식품연구원 | System for measuring quality of rice, method for evaluating palatability of rice, system for predicting germination rate of grain and method for predicting germination rate |
| CN107951485A (en) * | 2017-11-27 | 2018-04-24 | 乐普（北京）医疗器械股份有限公司 | Ambulatory ECG analysis method and apparatus based on artificial intelligence self study |
| CN108108767A (en) * | 2017-12-29 | 2018-06-01 | 美的集团股份有限公司 | A kind of cereal recognition methods, device and computer storage media |
Non-Patent Citations (1)
| Title |
|---|
| MEGHA R. SIDDAGANGAPPA 等: "Classification and Quality Analysis of Food Grains", 《IOSR JOURNAL OF COMPUTER ENGINEERING(IOSR-JCE)》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111579724A (en) * | 2020-06-01 | 2020-08-25 | 中国标准化研究院 | Rapid classification method and device for sensory sensitivity of tingling and peppery suprathreshold and application |
| CN114486877B (en) * | 2020-10-23 | 2024-01-19 | 合肥美亚光电技术股份有限公司 | Rice quality detection method, rice quality detection device and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| JP2021526646A (en) | 2021-10-07 |
| KR20210019089A (en) | 2021-02-19 |
| KR102453207B1 (en) | 2022-10-07 |
| WO2019238130A1 (en) | 2019-12-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108108767B (en) | Grain identification method and device and computer storage medium | |
| Radman et al. | Automated segmentation of iris images acquired in an unconstrained environment using HOG-SVM and GrowCut | |
| KR20210149848A (en) | Skin quality detection method, skin quality classification method, skin quality detection device, electronic device and storage medium | |
| CN108197546B (en) | Illumination processing method and device in face recognition, computer equipment and storage medium | |
| CN110032271B (en) | Contrast adjusting device and method, virtual reality equipment and storage medium | |
| CN110610183A (en) | Grain evaluation method, grain evaluation device, and storage medium | |
| EP3158503B1 (en) | Biometric identification | |
| CN105528757B (en) | A kind of image aesthetic quality method for improving based on content | |
| CN107465809B (en) | Verification method and terminal | |
| Alkassar et al. | Sclera recognition: on the quality measure and segmentation of degraded images captured under relaxed imaging conditions | |
| CN108090518A (en) | A kind of cereal recognition methods, device and computer storage media | |
| JP5793493B2 (en) | Gesture recognition using chroma key | |
| CN108090517A (en) | A kind of cereal recognition methods, device and computer storage media | |
| Guo et al. | Appearance-based gaze estimation under slight head motion | |
| Garea-Llano et al. | Framework for biometric iris recognition in video, by deep learning and quality assessment of the iris-pupil region | |
| Tsai et al. | Decision tree-based contrast enhancement for various color images | |
| KR102333500B1 (en) | Grain recognition method, apparatus and computer readable storage medium | |
| CN110222571B (en) | Intelligent judgment method and device for black eye and computer readable storage medium | |
| CN107038705B (en) | Retinal image bleeding area segmentation method and device and computing equipment | |
| CN111568151B (en) | Fruit and vegetable weight calculation method and device, juicer and storage medium | |
| US10984219B2 (en) | Fingerprint processing with liveness detection | |
| Grigorescu et al. | Closed-loop control in image processing for improvement of object recognition | |
| Chen et al. | Adaptive illumination normalization via adaptive illumination preprocessing and modified weber-face | |
| CN117237939B (en) | Image data-based detection method and device for food maturity of young cooker | |
| Vera et al. | Iris recognition algorithm on BeagleBone Black |
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 |