CN113109287A - Detection method for obtaining image processing oil quality by additionally arranging sensor - Google Patents
Detection method for obtaining image processing oil quality by additionally arranging sensor Download PDFInfo
- Publication number
- CN113109287A CN113109287A CN202110287336.0A CN202110287336A CN113109287A CN 113109287 A CN113109287 A CN 113109287A CN 202110287336 A CN202110287336 A CN 202110287336A CN 113109287 A CN113109287 A CN 113109287A
- Authority
- CN
- China
- Prior art keywords
- oil
- image
- quality
- image processing
- sensor
- 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
- 238000001514 detection method Methods 0.000 title claims abstract description 22
- 238000000034 method Methods 0.000 claims abstract description 31
- 238000001228 spectrum Methods 0.000 claims abstract description 13
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 12
- 230000000694 effects Effects 0.000 claims abstract description 11
- 239000007788 liquid Substances 0.000 claims abstract description 10
- 239000012533 medium component Substances 0.000 claims abstract description 4
- 239000003921 oil Substances 0.000 claims description 90
- 230000011218 segmentation Effects 0.000 claims description 14
- 238000003066 decision tree Methods 0.000 claims description 12
- 238000000926 separation method Methods 0.000 claims description 5
- 239000000306 component Substances 0.000 claims description 3
- 230000006872 improvement Effects 0.000 claims description 3
- 238000002329 infrared spectrum Methods 0.000 claims description 3
- 238000012417 linear regression Methods 0.000 claims description 3
- 239000004973 liquid crystal related substance Substances 0.000 claims description 3
- 239000010687 lubricating oil Substances 0.000 claims description 3
- 238000001157 Fourier transform infrared spectrum Methods 0.000 claims description 2
- 230000001737 promoting effect Effects 0.000 claims description 2
- 230000003595 spectral effect Effects 0.000 claims description 2
- 239000000203 mixture Substances 0.000 claims 1
- 239000002994 raw material Substances 0.000 claims 1
- 239000011521 glass Substances 0.000 description 4
- 230000009471 action Effects 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 238000005033 Fourier transform infrared spectroscopy Methods 0.000 description 2
- 230000007613 environmental effect Effects 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 241000894006 Bacteria Species 0.000 description 1
- 241000606161 Chlamydia Species 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 239000010627 cedar oil Substances 0.000 description 1
- 239000004568 cement Substances 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 210000003463 organelle Anatomy 0.000 description 1
- 238000010183 spectrum analysis Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/3577—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing liquids, e.g. polluted water
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/01—Arrangements or apparatus for facilitating the optical investigation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/243—Classification techniques relating to the number of classes
- G06F18/24323—Tree-organised classifiers
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N2021/3595—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using FTIR
Landscapes
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Pathology (AREA)
- Immunology (AREA)
- General Health & Medical Sciences (AREA)
- Biochemistry (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Data Mining & Analysis (AREA)
- Theoretical Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Bioinformatics & Computational Biology (AREA)
- General Engineering & Computer Science (AREA)
- Evolutionary Computation (AREA)
- Evolutionary Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Artificial Intelligence (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
The invention discloses a detection method for obtaining the quality of image processing oil by adding a sensor, which comprises the following steps of S1: obtaining an LD; s2: the transmitting LD is focused and converted into parallel light through a convex lens and then is emitted into an oil medium; s3: collecting transmitted light through a CMOS image sensor and forming an image; s4: obtaining a spectrum result by using Fourier transform on the image, and obtaining an oil level position based on the image and a trained oil level detection model; s5: calculating to obtain the medium components in the current oil mirror according to the spectrum result and the oil level position in the S4, and judging whether all the oil liquid and all the air exist; s6: if the oil is all oil or the oil is more than 50%, calculating the water content, density, viscosity, water activity and dielectric constant of the oil according to the spectrum result, and recording the result; the method acquires the projection light by the CMOS photosensitive element and matching with the light source, obtains the oil quality by image processing on the MCU microprocessor, has simple operation and improves the working efficiency.
Description
Technical Field
The invention belongs to the technical field of image processing, and particularly relates to a detection method for obtaining the quality of image processing oil by additionally arranging a sensor.
Background
The oil lens is one of microscopes commonly used in laboratories, has slightly higher definition than a common optical microscope, and is used for observing relatively fine structures such as chlamydia, bacteria, organelles and the like. When the oil lens is used, cedar oil is required to be dripped on the glass slide.
The existing oil level oil quality is difficult to measure, workers need to contact with external tools to detect, the accuracy is relatively poor, the influences and judgment of the workers can be influenced, the working efficiency is reduced, meanwhile, various parameters of the oil are difficult to calculate and detect, the workers need to calculate for many times after collecting data, the time is wasted, the problem of operation difficulty is increased, and the detection method for obtaining the image processing oil quality by additionally installing the sensor is provided for the purpose.
Disclosure of Invention
The invention aims to provide a detection method for obtaining the quality of image processing oil by additionally arranging a sensor, which aims to solve the problems that the quality of the oil of the existing oil lens is difficult to measure, workers need to contact with external tools for detection, the accuracy is relatively poor, the influences and judgment of the workers are influenced, the working efficiency is reduced and the like.
In order to achieve the purpose, the invention provides the following technical scheme: a detection method for obtaining the quality of image processing oil by additionally arranging a sensor comprises the following steps,
s1: obtaining an LD;
s2: the transmitting LD is focused and converted into parallel light through a convex lens and then is emitted into an oil medium;
s3: collecting transmitted light through a CMOS image sensor and forming an image;
s4: obtaining a spectrum result by using a Fourier transform infrared spectrum method for the image, and obtaining an oil level position based on the image and a trained oil level detection model;
s5: calculating to obtain the medium components in the current oil mirror according to the spectrum result and the oil level position in the S4, and judging whether all the oil liquid and all the air exist;
s6: if the oil is all oil or the oil is more than 50%, calculating the water content, density, viscosity, water activity and dielectric constant of the oil according to the spectrum result, and recording the result;
s7: if all the air exists, recording the result;
s8: if 1-99% of oil level exists, the MCU microprocessor carries out image recognition on the image, analyzes to obtain the height proportion of the current oil liquid level in the oil mirror, and calculates the real height of the oil liquid level in the oil mirror according to the preset oil mirror height;
s9: and sending the result to the server through the wireless module.
Preferably, in S4, fourier transform is performed on the image, the image is converted from rgb space to frequency domain space, effective environmental factors affect the image generation factor, and the visible light band and the far-near infrared band are located in a uniform spatial dimension, and the formula is as follows:
preferably, in S6, a representative lubricating oil sample is collected as a training set.
Preferably, the infrared spectrum of the training set sample is determined, and the quality parameter of the training set sample is determined.
Preferably, in S5, regression tasks for oil components, density, viscosity, water activity, and dielectric constant are respectively established for different detection targets, binning operation is performed on the spectral band range according to a decision tree algorithm, and useless bins are removed according to model accuracy improvement;
and by the principle of least squares regression tree algorithm:
inputting: a training data set D;
and (3) outputting: regression tree f (x);
in an input space where a training data set is located, recursively dividing each region into two sub-regions and determining an output value on each sub-region, and constructing a binary decision tree:
(1) selecting the optimal segmentation variable j and the optimal segmentation point s, and solving
Traversing the variable j, scanning the fixed segmentation variable j for segmentation points s, selecting the pair (j, s) that minimizes the above equation
(2) Dividing the region by the selected pair (j, s) and determining the corresponding output value:
(3) continuing to call the steps (1) and (2) for the two sub-areas until a stopping condition is met;
(4) dividing an input space into M areas and producing a decision tree;
(5) linear regression is carried out on the characteristic space after Fourier transform and box separation operation are completed,
preferably, the decision variables with low decision promoting degree are gradually eliminated through the decision tree in the step (4), the decision regression tree model is repeatedly trained, after the model effect is stable, the image subjected to Fourier transform is subjected to binning operation according to a binning mode on a two-dimensional frequency band, and the operation can effectively ensure the stability of the subsequent prediction model.
Preferably, the LD employs an infrared laser diode with a wavelength of 650nm as a point light source.
Preferably, the image in S3 is displayed by a liquid crystal display.
Preferably, the server may view and modify the result recorded in S6.
Preferably, the server may view and modify the result recorded in S7.
Compared with the prior art, the invention has the beneficial effects that:
according to the method, the sensor is additionally arranged at the position of the original oil lens of the equipment, under the condition that the original mode of observing and inspecting by human eyes is not influenced, projection light is collected by the aid of the CMOS photosensitive element and the light source, the oil quality is obtained by image processing on the MCU microprocessor, the operation is simple, the oil moisture, density, viscosity, water activity and dielectric constant are calculated through spectral analysis, the functionality is further improved, the calculation result is rapid, the accuracy is high, the complicated steps of calculating after a worker obtains parameters are reduced, the time is saved, and the working efficiency is improved.
Drawings
FIG. 1 is a schematic structural view of the present invention;
FIG. 2 is a schematic diagram of a wireless module and server connection according to the present invention;
FIG. 3 is a schematic flow chart of the present invention;
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1-3, the present invention provides a technical solution: a detection method for obtaining the quality of image processing oil by additionally arranging a sensor comprises the following steps,
s1: obtaining an LD;
s2: the transmitting LD is focused and converted into parallel light through a convex lens and then is emitted into an oil medium;
s3: collecting transmitted light through a CMOS image sensor and forming an image;
s4: obtaining a spectrum result by using Fourier transform infrared spectroscopy on the image, which specifically comprises the following steps: the method comprises the following steps of performing Fourier transform on an image by using a Fourier transform infrared spectroscopy, performing Fourier transform on the image, converting the image from an rgb space to a frequency domain space, wherein effective environmental factors influence an imaging factor, and a visible light wave band and a far-near infrared wave band are arranged in a unified space dimension, and the formula is as follows:
and obtaining an oil level position based on the image and the trained oil level detection model, wherein the method comprises the following steps:
acquiring original oil level images of different devices on a cement production line;
s4-1: marking out the oil observation mirror in the original image by a marking tool, wherein a marking frame is a minimum external rectangle of the oil observation mirror;
s4-2: horizontally turning over the marked image data, blurring the marked image data, adding different noises, performing homographic transformation and gray data enhancement, and mixing the image data with the original image to prepare oil sight glass detection training data;
s4-3: training a detection model of the oil observation mirror by adopting a YOLO-V3 target detection network to obtain a detection result of the oil observation mirror;
s4-4: intercepting and storing the oil level mirror image marked in the step S4-2, marking the coordinate value of an oil level page by taking the upper left corner of the picture as an original point, performing data enhancement such as horizontal turning, blurring, adding different noises and graying on the stored image, and mixing the data with the original image to manufacture oil level state detection training data;
s4-5: modifying the last layer network structure of the SegNet network based on the data prepared in the step S4-4, training a segmentation network model, and projecting a segmentation result to obtain an oil level position;
the method can further comprise the following steps:
s4-6: and calculating the position and the size of the oil level sight glass and the height percentage of the oil level in the oil level sight glass by combining the results obtained in the step S4-3 and the step S4-5.
S5: calculating to obtain the medium components in the current oil mirror according to the spectrum result and the oil level position in the S4, and judging whether all the oil liquid and all the air exist;
s6: if the oil is all oil or the oil is over 50 percent, calculating the water content, density, viscosity, water activity and dielectric constant of the oil according to the spectrum result, recording the result,
a representative lubricating oil sample was collected as a training set.
And (4) measuring the infrared spectrum of the training set sample, and measuring the quality parameters of the training set sample.
In S5, regression tasks for oil components, density, viscosity, water activity and dielectric constant are respectively built for different detection targets, box separation operation is carried out on a spectrum wave band range according to a decision tree algorithm, and useless box sections are removed according to model precision improvement degree;
and by the principle of least squares regression tree algorithm:
inputting: a training data set D;
and (3) outputting: regression tree f (x);
in an input space where a training data set is located, recursively dividing each region into two sub-regions and determining an output value on each sub-region, and constructing a binary decision tree:
(1) selecting the optimal segmentation variable j and the optimal segmentation point s, and solving
Traversing the variable j, scanning the fixed segmentation variable j for segmentation points s, selecting the pair (j, s) that minimizes the above equation
(2) Dividing the region by the selected pair (j, s) and determining the corresponding output value:
(3) continuing to call the steps (1) and (2) for the two sub-areas until a stopping condition is met;
(4) dividing an input space into M areas, producing a decision tree, gradually eliminating decision variables with low decision lifting degree through the decision tree, repeatedly training a decision regression tree model, performing binning operation on images subjected to Fourier transform in a binning mode on a two-dimensional frequency band when the model effect is stable, wherein the binning operation can effectively ensure the stability of a subsequent prediction model
(5) Linear regression is carried out on the characteristic space after Fourier transform and box separation operation are completed,
s7: if all the air exists, recording the result;
s8: if 1-99% of oil level exists, the MCU microprocessor carries out image recognition on the image, analyzes to obtain the height proportion of the current oil liquid level in the oil mirror, and calculates the real height of the oil liquid level in the oil mirror according to the preset oil mirror height;
s9: and sending the result to the server through the wireless module.
In this embodiment, it is preferable that the LD employs an infrared laser diode with a wavelength of 650nm as a point light source.
In this embodiment, it is preferable that the image in S3 is displayed by a liquid crystal display.
In this embodiment, the server may preferably view and modify the result recorded in S6.
In this embodiment, the server may preferably view and modify the result recorded in S7.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The above description is only for the purpose of illustrating the technical solutions of the present invention and not for the purpose of limiting the same, and other modifications or equivalent substitutions made by those skilled in the art to the technical solutions of the present invention should be covered within the scope of the claims of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (10)
1. A detection method for obtaining the quality of image processing oil by additionally arranging a sensor is characterized by comprising the following steps: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
s1: obtaining an LD;
s2: the transmitting LD is focused and converted into parallel light through a convex lens and then is emitted into an oil medium;
s3: collecting transmitted light through a CMOS image sensor and forming an image;
s4: obtaining a spectrum result by using a Fourier transform infrared spectrum method for the image, and obtaining an oil level position based on the image and a trained oil level detection model;
s5: calculating to obtain the medium components in the current oil mirror according to the spectrum result and the oil level position in the S4, and judging whether all the oil liquid and all the air exist;
s6: if the oil is all oil or the oil is more than 50%, calculating the water content, density, viscosity, water activity and dielectric constant of the oil according to the spectrum result, and recording the result;
s7: if all the air exists, recording the result;
s8: if 1-99% of oil level exists, the MCU microprocessor carries out image recognition on the image, analyzes to obtain the height proportion of the current oil liquid level in the oil mirror, and calculates the real height of the oil liquid level in the oil mirror according to the preset oil mirror height;
s9: and sending the result to the server through the wireless module.
2. The method for detecting the quality of the image processing oil obtained by the sensor, as claimed in claim 1, wherein the method comprises the following steps: in S4, fourier transform is performed on the image, the image is converted from rgb space to frequency domain space, and the visible light band and the far-near infrared band are placed in a uniform spatial dimension, using the formula:
3. the method for detecting the quality of the image processing oil obtained by the sensor, as claimed in claim 1, wherein the method comprises the following steps: in S6, a representative lubricating oil sample is collected as a training set.
4. The method for detecting the quality of the image processing oil obtained by the sensor as claimed in claim 3, wherein the method comprises the following steps: and measuring the infrared spectrum of the training set sample, and measuring the quality parameters of the training set sample.
5. The method for detecting the quality of the image processing oil obtained by the sensor, as claimed in claim 1, wherein the method comprises the following steps: in the S5, regression tasks for oil components, density, viscosity, water activity and dielectric constant are respectively built for different detection targets, the spectral band range is subjected to box separation operation according to a decision tree algorithm, and useless box sections are removed according to the accuracy improvement degree of the model;
and by the principle of least squares regression tree algorithm:
inputting: a training data set D;
and (3) outputting: regression tree f (x);
in an input space where a training data set is located, recursively dividing each region into two sub-regions and determining an output value on each sub-region, and constructing a binary decision tree:
(1) selecting the optimal segmentation variable j and the optimal segmentation point s, and solving
Traversing the variable j, scanning the fixed segmentation variable j for segmentation points s, selecting the pair (j, s) that minimizes the above equation
(2) Dividing the region by the selected pair (j, s) and determining the corresponding output value:
(3) continuing to call the steps (1) and (2) for the two sub-areas until a stopping condition is met;
(4) dividing an input space into M areas and producing a decision tree;
(5) linear regression is carried out on the characteristic space after Fourier transform and box separation operation are completed,
6. the method for detecting the quality of the image processing oil obtained by the sensor as claimed in claim 5, wherein the method comprises the following steps: and (4) gradually eliminating decision variables with low decision promoting degree through the decision tree in the step (4), repeatedly training a decision regression tree model, and performing binning operation on the images subjected to Fourier transform according to a binning mode on a two-dimensional frequency band when the model effect is stable, wherein the operation can effectively ensure the stability of a subsequent prediction model.
7. The method for detecting the quality of the image processing oil obtained by the sensor, as claimed in claim 1, wherein the method comprises the following steps: the LD in S1 adopts 650nm wavelength infrared laser diode as point light source.
8. The method for detecting the quality of the image processing oil obtained by the sensor, as claimed in claim 1, wherein the method comprises the following steps: the image in S3 is displayed by the liquid crystal display.
9. The method for detecting the quality of the image processing oil obtained by the sensor, as claimed in claim 1, wherein the method comprises the following steps: the server may view and modify the results recorded in S6.
10. The method for detecting the quality of the image processing oil obtained by the sensor, as claimed in claim 1, wherein the method comprises the following steps: the server may view and modify the results recorded in S7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110287336.0A CN113109287A (en) | 2021-03-17 | 2021-03-17 | Detection method for obtaining image processing oil quality by additionally arranging sensor |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110287336.0A CN113109287A (en) | 2021-03-17 | 2021-03-17 | Detection method for obtaining image processing oil quality by additionally arranging sensor |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113109287A true CN113109287A (en) | 2021-07-13 |
Family
ID=76711890
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110287336.0A Pending CN113109287A (en) | 2021-03-17 | 2021-03-17 | Detection method for obtaining image processing oil quality by additionally arranging sensor |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113109287A (en) |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101576485A (en) * | 2009-06-04 | 2009-11-11 | 浙江大学 | Analytical method of multi-source spectrum fusion water quality |
| CN103314293A (en) * | 2010-11-16 | 2013-09-18 | 霍夫曼-拉罗奇有限公司 | Method and apparatus for detecting foam on a liquid surface in a vessel |
| CN103808911A (en) * | 2013-12-09 | 2014-05-21 | 神华集团有限责任公司 | Lubricating oil detection device |
| CN105606696A (en) * | 2015-12-17 | 2016-05-25 | 北京至感传感器技术研究院有限公司 | Oil liquid quality parameter detection method, sensor and on-line detection device |
| CN107064023A (en) * | 2017-05-09 | 2017-08-18 | 北京邮电大学 | A kind of grease color detecting system and method |
| CN108362661A (en) * | 2017-12-22 | 2018-08-03 | 无锡中科恒源信息科技有限公司 | Frying oil on-line detecting system and its method based on spectrum sensing technology |
| CN110428416A (en) * | 2019-08-06 | 2019-11-08 | 广东工业大学 | A kind of liquid level visible detection method and device |
| CN111077093A (en) * | 2020-01-10 | 2020-04-28 | 安徽理工大学 | Method and device for quickly detecting coal gangue based on multispectral technology |
| CN111160629A (en) * | 2019-12-13 | 2020-05-15 | 广东电网有限责任公司 | Transformer oil temperature prediction method combining k-means and random forest |
| CN111487213A (en) * | 2020-04-29 | 2020-08-04 | 武汉新烽光电股份有限公司 | Multispectral fusion chemical oxygen demand testing method and device |
| CN112036409A (en) * | 2020-08-13 | 2020-12-04 | 浙江大华技术股份有限公司 | Reading identification method and device of liquid level meter |
| CN112132234A (en) * | 2020-10-28 | 2020-12-25 | 重庆斯铂电气自动化设备有限公司 | Oil level monitoring system and method based on image recognition |
-
2021
- 2021-03-17 CN CN202110287336.0A patent/CN113109287A/en active Pending
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101576485A (en) * | 2009-06-04 | 2009-11-11 | 浙江大学 | Analytical method of multi-source spectrum fusion water quality |
| CN103314293A (en) * | 2010-11-16 | 2013-09-18 | 霍夫曼-拉罗奇有限公司 | Method and apparatus for detecting foam on a liquid surface in a vessel |
| CN103808911A (en) * | 2013-12-09 | 2014-05-21 | 神华集团有限责任公司 | Lubricating oil detection device |
| CN105606696A (en) * | 2015-12-17 | 2016-05-25 | 北京至感传感器技术研究院有限公司 | Oil liquid quality parameter detection method, sensor and on-line detection device |
| CN107064023A (en) * | 2017-05-09 | 2017-08-18 | 北京邮电大学 | A kind of grease color detecting system and method |
| CN108362661A (en) * | 2017-12-22 | 2018-08-03 | 无锡中科恒源信息科技有限公司 | Frying oil on-line detecting system and its method based on spectrum sensing technology |
| CN110428416A (en) * | 2019-08-06 | 2019-11-08 | 广东工业大学 | A kind of liquid level visible detection method and device |
| CN111160629A (en) * | 2019-12-13 | 2020-05-15 | 广东电网有限责任公司 | Transformer oil temperature prediction method combining k-means and random forest |
| CN111077093A (en) * | 2020-01-10 | 2020-04-28 | 安徽理工大学 | Method and device for quickly detecting coal gangue based on multispectral technology |
| CN111487213A (en) * | 2020-04-29 | 2020-08-04 | 武汉新烽光电股份有限公司 | Multispectral fusion chemical oxygen demand testing method and device |
| CN112036409A (en) * | 2020-08-13 | 2020-12-04 | 浙江大华技术股份有限公司 | Reading identification method and device of liquid level meter |
| CN112132234A (en) * | 2020-10-28 | 2020-12-25 | 重庆斯铂电气自动化设备有限公司 | Oil level monitoring system and method based on image recognition |
Non-Patent Citations (4)
| Title |
|---|
| 吴娱: "数字图像处理", 31 October 2017, 北京邮电大学出版社, pages: 156 - 158 * |
| 姚海根 等: "数字印刷质量检测与评价", 30 April 2012, 印刷工业出版社, pages: 205 - 207 * |
| 李宇: "数学物理方法", 31 August 2020, 大连海事大学出版社, pages: 173 - 175 * |
| 穆海洋: "多源光谱融合水质分析的多模型组合建模方法", 中国优秀硕士学位论文全文数据库 工程科技Ⅰ辑, 15 July 2011 (2011-07-15) * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Huang et al. | Automatic inspection of pavement cracking distress | |
| US10846325B2 (en) | Information search system and information search program | |
| CN113240626A (en) | Neural network-based method for detecting and classifying concave-convex flaws of glass cover plate | |
| CN1800820A (en) | Pollutant gas concentration field measurement method in environmental wind tunnel | |
| CN105227802A (en) | A kind of self-adaptation control method towards best quality image rapid scanning and system | |
| CN113177909A (en) | Multi-mode visual detection method and system for recycled aggregate with mortar on surface | |
| CN109085178A (en) | A kind of accurate on-line monitoring method of defect fingerprint and feedback strategy for increasing material manufacturing | |
| CN109182081A (en) | A kind of unicellular separation system based on image processing model | |
| CN113483682B (en) | Gap measurement method and system based on machine vision | |
| CN102663340A (en) | Device and method for classifying plates | |
| CN111257329A (en) | Smartphone camera defect detection method and detection system | |
| Boffety et al. | Color image simulation for underwater optics | |
| CN113655002A (en) | Recycled aggregate quality detection system with mortar on surface based on hyperspectral technology | |
| CA3026633C (en) | Information search system, information search method, and information search program | |
| CN113109287A (en) | Detection method for obtaining image processing oil quality by additionally arranging sensor | |
| Potapov et al. | Smart-camera–based linear sizing | |
| CN109978824B (en) | Transparent film defect morphology measurement method and system | |
| Luzi et al. | Automatic evaluation of vertex structural defects on the anode surface of a low-light-level image intensifier based on proposed individual image processing strategies | |
| Luo et al. | High-precision four-dimensional hyperspectral imager integrating fluorescence spectral detection and 3D surface shape measurement | |
| CN113702328B (en) | Method, device, equipment and storage medium for analyzing properties of finished oil | |
| CN109035390A (en) | Modeling method and device based on laser radar | |
| Martišek et al. | High-quality three-dimensional reconstruction and noise reduction of multifocal images from oversized samples | |
| CN114441450A (en) | Multi-metal nodule typical characteristic simulation analysis device and method | |
| Masoudifar et al. | Depth estimation and deblurring from a single image using an optimized-throughput coded aperture | |
| RU2413179C2 (en) | Method of monitoring article surface roughness |
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 |