CN117011505B - Identification method, system and related equipment based on spectrum data - Google Patents
Identification method, system and related equipment based on spectrum data Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 53
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- 230000003287 optical effect Effects 0.000 claims abstract description 95
- 238000004458 analytical method Methods 0.000 claims abstract description 9
- 230000003595 spectral effect Effects 0.000 claims description 35
- 238000002834 transmittance Methods 0.000 claims description 20
- 238000006243 chemical reaction Methods 0.000 claims description 14
- 238000007405 data analysis Methods 0.000 claims description 8
- 238000003860 storage Methods 0.000 claims description 8
- 239000004973 liquid crystal related substance Substances 0.000 claims description 5
- 238000010276 construction Methods 0.000 claims description 3
- 238000005259 measurement Methods 0.000 claims description 3
- 239000007787 solid Substances 0.000 claims description 2
- 238000004364 calculation method Methods 0.000 abstract description 8
- 238000001514 detection method Methods 0.000 abstract description 6
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 abstract description 5
- 238000003384 imaging method Methods 0.000 description 11
- 238000010586 diagram Methods 0.000 description 8
- 239000000126 substance Substances 0.000 description 4
- 230000000694 effects Effects 0.000 description 3
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- 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
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/10—Image acquisition
- G06V10/12—Details of acquisition arrangements; Constructional details thereof
- G06V10/14—Optical characteristics of the device performing the acquisition or on the illumination arrangements
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/766—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using regression, e.g. by projecting features on hyperplanes
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V2201/00—Indexing scheme relating to image or video recognition or understanding
- G06V2201/07—Target detection
Abstract
The invention is suitable for the technical field of optical identification, and particularly relates to an identification method, an identification system and related equipment based on spectrum data. The invention provides a recognition method of an optical unit combining sense calculation, which combines an optical regression model to construct an optical unit integrating acquisition and analysis of optical data, can omit the requirement of an additional calculation unit in the optical recognition process, and reduces the realization cost; meanwhile, the object detection is realized through the spectrum data of the identification object, the shape requirement of the identification object is reduced, the optical unit can be applied to the ground and the water surface, and the scene adaptability of the scheme is improved.
Description
Technical Field
The invention is suitable for the technical field of optical identification, and particularly relates to an identification method, an identification system and related equipment based on spectrum data.
Background
With the development of science and technology, optical recognition is applied in more and more scenes, such as scientific research observation, meteorological analysis, pollution monitoring and the like, and in different application scenes, for an object to be monitored, further analysis is often performed by acquiring optical data of the object. During the petroleum production, storage, refining and use, petroleum hydrocarbon overflows and discharges due to accidents, abnormal operation, overhauls and the like, and a great deal of petroleum hydrocarbon overflows and is released into aquatic or land environments to negatively influence animal and plant groups and human health, so that monitoring of the pollutants is a main means for dealing with environmental problems.
The existing optical recognition schemes mainly have two kinds: one scheme is based on the visible light camera as the acquisition equipment of the optical data, the material is identified, however, the scheme of the visible light camera is limited in adaptation environment, the camera can only be used for imaging, and the material detection and analysis needs to be processed by another calculation unit, so that the identification scheme of the visible light camera has time delay and higher implementation cost; another solution is to use a method of obtaining optical data for detection by using laser fluorescence of a substance to be detected, however, the cost of laser identification is higher, and the identified scene has a requirement, and the solution of laser is generally only suitable for water surface detection.
Therefore, there is a need to propose a new optical recognition method to solve the above-mentioned problems.
Disclosure of Invention
The invention provides a spectrum data-based identification method, a spectrum data-based identification system and related equipment, and aims to solve the problems of high cost and large scene limitation of an optical identification scheme in the prior art.
To solve the above technical problem, in a first aspect, the present invention provides an identification method based on spectral data, the identification method comprising the steps of:
acquiring a spectrum curve of a target object;
performing data analysis on the spectrum curve to establish an optical regression equation model;
constructing an optical unit according to the optical regression equation model;
and acquiring an image to be identified through the optical unit, and detecting and outputting an identification result of the target object from the image to be identified.
Further, defining the spectral curve asIt satisfies the following conditions:
；
wherein,indicating that the target objects are respectively atnReflectance value of individual band,/>A classification representing the target object;
the optical regression model satisfies:
；
；
wherein,for the spectral regression vector corresponding to the target object, < >>Is a component of the spectral regression vector.
Further, defining a lens transmittance curve L (λ) and a quantum conversion efficiency curve Q (λ) of the optical unit, and obtaining the lens transmittance curve L (λ) and the quantum conversion efficiency curve Q (λ), where a controllable transmittance curve T (λ) of one controllable subunit of the optical unit satisfies:
。
further, the lens transmittance curve L (λ) and the quantum conversion efficiency curve Q (λ) are obtained by means of actual measurement.
Still further, the controllable subunit is implemented based on one of a liquid crystal tunable filter, a coated broadband filter, and an ultra-surface broadband filter.
In a second aspect, the present invention also provides an identification system based on spectral data, comprising:
the spectrum data acquisition module is used for acquiring a spectrum curve of the target object;
the optical analysis module is used for carrying out data analysis on the spectrum curve so as to establish an optical regression equation model;
the optical construction module is used for constructing an optical unit according to the optical regression equation model;
and the sensing and calculating integrated module is used for acquiring an image to be identified through the optical unit, and detecting and outputting an identification result of the target object from the image to be identified.
In a third aspect, the present invention also provides a computer device comprising: the system comprises a memory, a processor and a spectrum data based identification program stored on the memory and capable of running on the processor, wherein the processor realizes the steps in the spectrum data based identification method according to any one of the embodiments when executing the spectrum data based identification program.
In a fourth aspect, the present invention also provides a computer-readable storage medium, on which a spectrum data based identification program is stored, which when executed by a processor implements the steps in the spectrum data based identification method according to any of the above embodiments.
The invention has the beneficial effects that an identification method of an optical unit combined with sensing calculation is provided, the method combines an optical regression model to construct an optical unit with integrated acquisition and analysis of optical data, the requirement of an additional calculation unit can be omitted in the optical identification process, and the implementation cost is reduced; meanwhile, the object detection is realized through the spectrum data of the identification object, the shape requirement of the identification object is reduced, the optical unit can be applied to the ground and the water surface, and the scene adaptability of the scheme is improved.
Drawings
FIG. 1 is a block flow diagram of steps of a method for spectral data-based identification provided by an embodiment of the present invention;
FIG. 2 is a schematic diagram of the correspondence of the parameters of the optical regression model according to the embodiment of the present invention;
FIG. 3 is a schematic diagram of an imaging process of an optical unit according to an embodiment of the present invention;
FIG. 4 is a schematic structural diagram of an identification system based on spectral data according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a computer device according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Referring to fig. 1, fig. 1 is a block flow diagram of steps of a spectrum data-based identification method according to an embodiment of the present invention, where the identification method includes the following steps:
s101, acquiring a spectrum curve of a target object.
The target object in the embodiment of the present invention may be a solid substance on the ground or a liquid substance on the water surface in a planar form, and the step may be performed by a spectrum acquisition device, or may be performed by using existing spectrum data, which is not limited to the embodiment of the present invention.
S102, carrying out data analysis on the spectrum curve to establish an optical regression equation model.
Defining the spectral curve asIt satisfies the following conditions:
；
wherein,indicating that the target objects are respectively atnReflectance value of individual band,/>A classification representing the target object; specifically, for the spectrum curve->Which demonstrates this physical property of being able to obtain a classification of a substance by its spectrum.
The optical regression model satisfies:
；
；
wherein,for the spectral regression vector corresponding to the target object, < >>Is a component of the spectral regression vector. According to the spectral regression model, it can be known that the spectral curve of the target object has a corresponding relationship with its classification, and in fourteen cases of the present invention, the spectral regression vector is obtained by constructing the optical regression model, that is, in the deriving process of the spectral curve, the spectral regression vector is calculated>Can obtain spectral curve +.>Classification of->As shown in fig. 2, the process can be expressed as:
。
the corresponding optical regression model and the spectral regression vector can be obtained by combining the spectral curve acquired in the step S101 with a data analysis method such as machine learning in the actual implementation process.
S103, constructing an optical unit according to the optical regression equation model.
Specifically, the optical unit constructed in the embodiment of the present invention may be an actual optical imaging unit, which includes a lens and a sensor, and the imaging process conforms to the light propagation rule, that is, the target object is imaged onto the sensor through the lens, so as to obtain the imaging of the target object. In the embodiment of the present invention, parameters of structures such as a lens, a sensor, and the like, including the lens transmittance curve L (λ) and the quantum conversion efficiency curve Q (λ), need to be determined according to the optical regression equation, where the lens transmittance curve L (λ) and the quantum conversion efficiency curve Q (λ) are obtained by actually measuring.
Further, the embodiment of the invention constructs a controllable subunit in the optical unit, wherein the controllable subunit is used as a part of an imaging system to participate in the imaging process of the optical unit, and the controllable transmittance curve T (λ) of the controllable subunit of the optical unit is satisfied by acquiring the lens transmittance curve L (λ) and the quantum conversion efficiency curve Q (λ):
。
preferably, the controllable subunit in the implementation process can be implemented based on a liquid crystal tunable filter, a coated broadband filter, a super-surface broadband filter and the like, and other structures such as a lens, a sensor and the like only need to determine corresponding physical parameters.
S104, acquiring an image to be identified through the optical unit, and detecting and outputting an identification result of the target object from the image to be identified.
As in step S101, step S104 does not treat the recognized image as suchThe shape and the spatial position of the target object are limited, and in the implementation process, the optical effect presented by the target object can be acquired. By adding the controllable sub-unit in the optical unit, the imaging process of the optical unit can be corresponding to the optical regression model, in the step, the additional controllable sub-unit takes part in the imaging of the optical unit, and the transmittance curve can correspondingly make the spectrum regression vectorReflected in the imaging process, as shown in fig. 3, so that the final imaging of the optical unit can directly show the optical properties of the corresponding target object, namely showing the classification>. Through the design, the requirement of a subsequent calculation unit in the optical identification process can be omitted, the cost of the whole imaging system is reduced, and the identification efficiency is improved.
The invention has the beneficial effects that an identification method of an optical unit combined with sensing calculation is provided, the method combines an optical regression model to construct an optical unit with integrated acquisition and analysis of optical data, the requirement of an additional calculation unit can be omitted in the optical identification process, and the implementation cost is reduced; meanwhile, the object detection is realized through the spectrum data of the identification object, the shape requirement of the identification object is reduced, the optical unit can be applied to the ground and the water surface, and the scene adaptability of the scheme is improved.
Referring to fig. 4, fig. 4 is a schematic structural diagram of the spectrum data-based identification system provided in the embodiment of the present invention, where the spectrum data-based identification system 200 includes:
a spectrum data acquisition module 201, configured to acquire a spectrum curve of a target object;
the optical analysis module 202 is configured to perform data analysis on the spectrum curve to establish an optical regression equation model;
an optical construction module 203, configured to construct an optical unit according to the optical regression equation model;
and the sensing and calculating integrated module 204 is used for acquiring an image to be identified through the optical unit, and detecting and outputting an identification result of the target object from the image to be identified.
The spectrum data-based recognition system 200 can implement the steps in the spectrum data-based recognition method in the above embodiment, and can achieve the same technical effects, and is not described in detail herein with reference to the above embodiment.
Referring to fig. 5, fig. 5 is a schematic structural diagram of a computer device according to an embodiment of the present invention, where the computer device 300 includes: a memory 302, a processor 301 and a spectral data based identification program stored on the memory 302 and executable on the processor 301.
The processor 301 invokes the spectrum data-based identification program stored in the memory 302 to execute the steps in the spectrum data-based identification method provided in the embodiment of the present invention, please refer to fig. 1, specifically including the following steps:
s101, acquiring a spectrum curve of a target object.
S102, carrying out data analysis on the spectrum curve to establish an optical regression equation model.
Defining the spectral curve asIt satisfies the following conditions:
；
wherein,indicating that the target objects are respectively atnReflectance value of individual band,/>A classification representing the target object;
the optical regression model satisfies:
；
；
wherein,for the spectral regression vector corresponding to the target object, < >>Is a component of the spectral regression vector.
S103, constructing an optical unit according to the optical regression equation model.
Defining a lens transmittance curve L (lambda) of the optical unit, a quantum conversion efficiency curve Q (lambda) and obtaining the lens transmittance curve L (lambda) and the quantum conversion efficiency curve Q (lambda), wherein a controllable transmittance curve T (lambda) of one controllable subunit of the optical unit meets the following conditions:
。
the lens transmittance curve L (lambda) and the quantum conversion efficiency curve Q (lambda) are obtained through actual measurement.
The controllable subunit is realized based on one of a liquid crystal tunable filter, a film-coated broadband filter and an ultra-surface broadband filter.
S104, acquiring an image to be identified through the optical unit, and detecting and outputting an identification result of the target object from the image to be identified.
The computer device 300 provided in the embodiment of the present invention can implement the steps in the spectrum data-based identification method in the above embodiment, and can implement the same technical effects, and is not described herein again with reference to the description in the above embodiment.
The embodiment of the invention also provides a computer readable storage medium, on which a spectrum data-based identification program is stored, which realizes each process and step in the spectrum data-based identification method provided by the embodiment of the invention when being executed by a processor, and can realize the same technical effects, and in order to avoid repetition, the description is omitted here.
Those skilled in the art will appreciate that implementing all or part of the above-described methods in accordance with the embodiments may be accomplished by instructing the associated hardware by a spectral data-based recognition program, which may be stored on a computer-readable storage medium, which when executed may include the steps of the above-described embodiments. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM) or the like.
It should be noted that, in this document, 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 one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) comprising instructions for causing a terminal (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method according to the embodiments of the present invention.
While the embodiments of the present invention have been illustrated and described in connection with the drawings, what is presently considered to be the most practical and preferred embodiments of the invention, it is to be understood that the invention is not limited to the disclosed embodiments, but on the contrary, is intended to cover various equivalent modifications and equivalent arrangements included within the spirit and scope of the appended claims.
Claims (5)
1. An identification method based on spectrum data, characterized in that the identification method comprises the following steps:
acquiring a spectrum curve of a target object;
performing data analysis on the spectrum curve to establish an optical regression equation model;
constructing an optical unit according to the optical regression equation model, wherein the optical unit is a physical unit;
acquiring an image to be identified through the optical unit, and detecting and outputting an identification result of the target object from the image to be identified;
wherein the spectral curve is defined asIt satisfies the following conditions:
；
representation houseThe target objects are respectively atnReflectance value of individual band,/>A classification representing the target object;
the optical regression equation model satisfies:
；
；
for the spectral regression vector corresponding to the target object, < >>Components of the spectral regression vector;
defining a lens transmittance curve L (lambda) of the optical unit, a quantum conversion efficiency curve Q (lambda) and obtaining the lens transmittance curve L (lambda) and the quantum conversion efficiency curve Q (lambda), wherein a controllable transmittance curve T (lambda) of one controllable subunit of the optical unit meets the following conditions:
；
the controllable subunit is realized based on one of a liquid crystal tunable filter, a film-coated broadband filter and an ultra-surface broadband filter.
2. The method of claim 1, wherein the lens transmittance curve L (λ) and the quantum conversion efficiency curve Q (λ) are obtained by means of actual measurement.
3. An identification system based on spectral data, comprising:
the spectrum data acquisition module is used for acquiring a spectrum curve of the target object;
the optical analysis module is used for carrying out data analysis on the spectrum curve so as to establish an optical regression equation model;
the optical construction module is used for constructing an optical unit according to the optical regression equation model, wherein the optical unit is a solid unit;
the sensing and calculating integrated module is used for acquiring an image to be identified through the optical unit, detecting and outputting an identification result of the target object from the image to be identified;
wherein the spectral curve is defined asIt satisfies the following conditions:
；
indicating that the target objects are respectively atnReflectance value of individual band,/>A classification representing the target object;
the optical regression equation model satisfies:
；
；
for the spectral regression vector corresponding to the target object, < >>Components of the spectral regression vector;
defining a lens transmittance curve L (lambda) of the optical unit, a quantum conversion efficiency curve Q (lambda) and obtaining the lens transmittance curve L (lambda) and the quantum conversion efficiency curve Q (lambda), wherein a controllable transmittance curve T (lambda) of one controllable subunit of the optical unit meets the following conditions:
；
the controllable subunit is realized based on one of a liquid crystal tunable filter, a film-coated broadband filter and an ultra-surface broadband filter.
4. A computer device, comprising: memory, a processor and a spectral data based identification program stored on the memory and executable on the processor, the processor implementing the steps in the spectral data based identification method according to any of claims 1-2 when the spectral data based identification program is executed.
5. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a spectral data based identification program, which, when executed by a processor, implements the steps of the spectral data based identification method according to any of claims 1-2.
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| CN103278464A (en) * | 2013-04-18 | 2013-09-04 | 北京工商大学 | Method and device for fish flesh detection |
| CN106295696A (en) * | 2016-08-09 | 2017-01-04 | 中国科学院遥感与数字地球研究所 | A kind of multi-source Remote Sensing Images radiation normalization method |
| CN111443045A (en) * | 2020-04-26 | 2020-07-24 | 深圳市中达瑞和科技有限公司 | Spectral imaging analysis system and spectral imaging analysis method |
| WO2023040675A1 (en) * | 2021-09-15 | 2023-03-23 | 佛山市博顿光电科技有限公司 | Method and apparatus for optimizing process parameter of film coating process, and real-time film coating monitoring system |
| CN116067911A (en) * | 2023-02-03 | 2023-05-05 | 四川轻化工大学 | Mineral multicomponent grade identification and separation method |
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Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103278464A (en) * | 2013-04-18 | 2013-09-04 | 北京工商大学 | Method and device for fish flesh detection |
| CN106295696A (en) * | 2016-08-09 | 2017-01-04 | 中国科学院遥感与数字地球研究所 | A kind of multi-source Remote Sensing Images radiation normalization method |
| CN111443045A (en) * | 2020-04-26 | 2020-07-24 | 深圳市中达瑞和科技有限公司 | Spectral imaging analysis system and spectral imaging analysis method |
| WO2023040675A1 (en) * | 2021-09-15 | 2023-03-23 | 佛山市博顿光电科技有限公司 | Method and apparatus for optimizing process parameter of film coating process, and real-time film coating monitoring system |
| CN116067911A (en) * | 2023-02-03 | 2023-05-05 | 四川轻化工大学 | Mineral multicomponent grade identification and separation method |
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