Device, system and method for detecting substance

Abstract

A substance detection device, a substance detection system, and a substance detection method capable of identifying a substance contained in a sample based on incident light incident from a sample are disclosed. The substance detection device according to the present invention comprises: a beam splitter that separates incident light incident from a region containing a sample into a first beam having a first wavelength spectrum and a second beam having a second wavelength spectrum different from the first wavelength spectrum; a first sensor configured to receive the first beam and output a first characteristic value based on the first wavelength spectrum; a second sensor configured to receive the second beam and output a second characteristic value based on the second wavelength spectrum; and a processor that derives an eigenvalue based on the first characteristic value and the second characteristic value, and identifies at least one substance contained in the sample based on the eigenvalue.

Description

Technology Field

[0001] The present invention relates to a substance detection device, a substance detection system, and a substance detection method, and more specifically, to a substance detection device, a substance detection system, and a substance detection method capable of identifying a substance contained in a sample based on incident light incident from a sample. Background Technology

[0002] Conventionally, fingerprints at the molecular level were detected using spectroscopic methods such as hyperspectral and / or hyperspectral imaging (HSI), and the type of substance was detected and identified using the detected fingerprints.

[0003] Spectroscopy is a technique for collecting and interpreting detailed spectral information from image data. Image data contains hundreds of narrow, adjacent spectral bands, each possessing a continuous spectrum. The spectral range of these bands includes infrared, visible, and ultraviolet light. Detailed spectral analysis (resolution) is utilized not only for target tracking but also as a powerful tool for high-spectral imaging technology to detect substances such as chemicals.

[0004] Meanwhile, spectroscopy utilizes hundreds of bands to classify or detect targets. Computational complexity is proportional to the amount of data to be processed. Consequently, the volume of data processed by spectroscopy is greater than that of general image processing. For example, hyperspectral cubes containing high spatial and spectral information are massive, ranging from tens to hundreds of megabytes. Many factors make hyperspectral data analysis difficult, including physical factors such as atmospheric and geometric distortion, as well as mixed pixels, high data redundancy due to high correlation in adjacent bands, variability in hyperspectral signatures, and the curse of dimensionality.

[0005] In other words, spectroscopy has the problem of taking a long time for measurement and having a complex analysis method.

[0006] In addition, conventional spectroscopy has the problem that it requires an expensive image sensor (detector) to detect and identify the type of substance. Prior art literature

[0007] Republic of Korea Published Patent Application No. 10-2009-0092433 (Published September 1, 2009) The problem to be solved

[0008] The objective of the present invention to solve the aforementioned problems is to provide a substance detection device, a substance detection system, and a substance detection method capable of detecting a substance through a general infrared sensor.

[0009] Another objective of the present invention is to provide a substance detection device, a substance detection system, and a substance detection method capable of detecting a substance by analyzing infrared images of a small number of bands.

[0010] Another objective of the present invention is to provide a substance detection device, a substance detection system, and a substance detection method capable of generating and providing an image including a color image based on infrared image analysis results. means of solving the problem

[0011] To achieve the above-mentioned objective, a substance detection device according to one embodiment of the present invention comprises: a beam splitter that separates incident light incident from a region containing a sample into a first beam having a first wavelength spectrum and a second beam having a second wavelength spectrum different from the first wavelength spectrum; a first sensor configured to receive the first beam and output a first characteristic value based on the first wavelength spectrum; a second sensor configured to receive the second beam and output a second characteristic value based on the second wavelength spectrum; and a processor that derives an intrinsic value based on the first characteristic value and the second characteristic value, and identifies at least one substance contained in the sample based on the intrinsic value.

[0012] The first sensor receives a beam reflected by the beam splitter among the incident light incident from the area containing the sample, and the second sensor receives a beam transmitted through the beam splitter among the incident light incident from the area containing the sample.

[0013] The first wavelength spectrum band and the second wavelength spectrum band are included in the long-wave infrared (LWIR) region.

[0014] The first wavelength spectrum band and the second wavelength spectrum band are characterized by being 8 μm or more and 14 μm or less.

[0015] The first wavelength spectrum and the second wavelength spectrum are each determined by the intrinsic spectrum of at least one substance contained in the sample and the wavelength separation characteristics of the beam splitter.

[0016] The above eigenvalue is determined as the weighted mean of the first characteristic value and the second characteristic value, which reflect the wavelength separation characteristics of the beam splitter.

[0017] The above eigenvalue is derived by the following relationship between the above first characteristic value and the above second characteristic value.

[0018]

[0019] (Here, constants a, b, c, and d are determined by the wavelength separation characteristics of the beam splitter)

[0020] The processor obtains an HSV value by mapping the total energy value of incident light incident from the region containing the sample and the eigenvalue onto the HSV (Hue-Saturation-Value) space, and obtains a color image by converting the HSV value into an RGB value.

[0021] It further includes a display that displays the color image obtained above.

[0022] It further includes a lens that concentrates incident light incident from an area containing a sample and guides it to the beam splitter.

[0023] To achieve the above-mentioned objective, a method for detecting a substance according to one embodiment of the present invention comprises the steps of: separating incident light incident from a region containing a sample into a first beam having a first wavelength spectrum and a second beam having a second wavelength spectrum different from the first wavelength spectrum; receiving the first beam to detect a first characteristic value and receiving the second beam to detect a second characteristic value; deriving an intrinsic value based on the first characteristic value and the second characteristic value, and identifying at least one substance contained in the sample based on the intrinsic value.

[0024] It further includes the step of storing the above-mentioned unique value and the above-mentioned specified substance corresponding to the above-mentioned unique value.

[0025] The step of identifying at least one substance included in the sample based on the above eigenvalue involves identifying the substance corresponding to the eigenvalue based on a previously stored substance-eigenvalue table.

[0026] The method further includes the step of obtaining an HSV value by correlating the total energy value of incident light incident from the region containing the sample and the eigenvalue in the HSV (Hue-Saturation-Value) space, and obtaining a color image corresponding to the specified substance by converting the HSV value into an RGB value.

[0027] It further includes the step of displaying the color image obtained above.

[0028] To achieve the above-mentioned objective, a material detection system according to one embodiment of the present invention comprises: a dichroic beam splitter that separates incident light incident from a region containing a sample into a plurality of beams having different infrared (IR) wavelength bands; a plurality of IR cameras that each receive the plurality of beams and each generate a plurality of output signals; a processor that receives the plurality of output signals generated from the plurality of IR cameras, combines the plurality of output signals according to preset conditions to obtain a combination signal, identifies at least one material based on the combination signal, and matches a color corresponding to the at least one identified material; and a display that displays the at least one identified material as a color-assigned image.

[0029] The above-mentioned pre-set conditions are determined according to the wavelength separation characteristics of the dichroic beam splitter.

[0030] Matching a color corresponding to at least one specific substance involves matching the total energy value of incident light incident from the area containing the sample and the eigenvalue to the HSV (Hue-Saturation-Value) space to obtain an HSV value, and converting the HSV value into an RGB value to obtain a color image. Effects of the invention

[0031] According to the substance detection device, substance detection system, and substance detection method of the present invention, substance detection is possible through a general infrared sensor, thereby enabling the implementation of equivalent functionality while replacing the expensive image sensor used in spectroscopy.

[0032] According to the material detection device, material detection system, and material detection method of the present invention, by detecting a material by analyzing infrared images of a small number of bands, the measurement time and analysis time can be shortened compared to conventional spectroscopy.

[0033] According to the substance detection device, substance detection system, and substance detection method of the present invention, by generating and providing an image including a color image based on infrared image analysis results, visual convenience for the user can be enhanced. Brief explanation of the drawing

[0034] FIG. 1 is a simplified diagram illustrating a material detection device and a material detection system according to the present invention. Figure 2 is a diagram showing the infrared wavelength spectrum. Figure 3 is a diagram showing examples of the first wavelength spectrum and the second wavelength spectrum. FIG. 4 is a diagram exemplarily showing the intrinsic spectrum of a certain material as an example. FIG. 5 is a diagram illustrating, as an example, the intrinsic spectrum of a certain substance being separated into a first wavelength spectrum and a second wavelength spectrum. Figure 6 is a diagram showing the reflected light separation characteristics of a beam splitter. Figure 7 is a diagram showing the transmitted light separation characteristics of a beam splitter. FIG. 8 is a diagram showing the first and second wavelength spectra of a material that is reflected and transmitted by a beam splitter and reaches the first sensor and the second sensor. FIG. 9 is a diagram showing the flow of the material detection process of the material detection device and material detection system according to the present invention. FIGS. 10 and FIGS. 11 are drawings showing a material detection device and a material detection system according to another embodiment of the present invention. FIGS. 12 to 16 are flowcharts showing the flow of a substance detection method according to the present invention. Specific details for implementing the invention

[0035] Some embodiments of the present invention will be described in detail below with reference to the exemplary drawings. It should be noted that in assigning reference numerals to the components of each drawing, the same components are given the same reference numeral whenever possible, even if they are shown in different drawings.

[0036] Furthermore, in describing the embodiments of the present invention, if it is determined that a detailed description of related known configurations or functions would hinder understanding of the embodiments of the present invention, such detailed description is omitted.

[0037] In addition, terms such as first, second, A, B, (a), (b), etc., may be used when describing the components of the embodiments of the present invention. These terms are used merely to distinguish the components from other components, and the essence, order, or sequence of the components is not limited by the terms used.

[0038] In this specification, the singular form includes the plural form unless specifically stated otherwise in the text. As used in this specification, “comprising” and / or “comprising” does not exclude the presence or addition of one or more other components in addition to the mentioned components.

[0039] Hereinafter, the present invention will be described in more detail with reference to the attached drawings.

[0041] FIG. 1 is a simplified diagram illustrating a material detection device and a material detection system according to the present invention.

[0042] A material detection device and material detection system according to the present invention, with reference to FIG. 1, comprises a beam splitter (100), a first sensor (200), a second sensor (300), a processor (400), and a display (500). The beam splitter (100) separates incident light (A) incident from an area containing a sample (1) into a first beam (B) having a first wavelength spectrum and a second beam (C) having a second wavelength spectrum different from the first wavelength spectrum. The first sensor (200) receives the first beam (B) and outputs a first characteristic value (V1) based on the first wavelength spectrum. The second sensor (300) receives the second beam (C) and outputs a second characteristic value (V2) based on the second wavelength spectrum. The processor (400) derives an intrinsic value (<λ>) based on a first characteristic value (V1) and a second characteristic value (V2), and identifies (specifies) at least one substance contained in the sample (1) based on the derived intrinsic value (<λ>).

[0044] The sample (1) contains at least one Point of Interest (POI) material. Between the sample (1) and the beam splitter (100), a lens (10) may be further included to concentrate and amplify incident light (A) incident from the area containing the sample (1). The lens (10) concentrates the incident light (A) and guides the concentrated incident light (A') to the beam splitter (100).

[0046] A beam splitter (100) separates incident light (A) incident from an area containing a sample (1) into a first beam (B) and a second beam (C). The first beam (B) and the second beam (C) separated by the beam splitter (100) are transmitted to a first sensor (200) and a second sensor (300), respectively.

[0047] The beam splitter (100) reflects a portion of the incident light (A) and transmits another portion. The beam splitter (100) splits the incident light (A) incident from the area containing the sample (1) into multiple beams (B, C) having different infrared (IR) wavelength bands. The beam splitter (100) has filter characteristics that ensure the first sensor (200) and the second sensor (300) receive different signals for at least a portion of the entire wavelength range of the incident light (A). Depending on the filter characteristics of the beam splitter (100), a portion of the infrared wavelength band of the incident light (A) is reflected and transmitted to the first sensor (200) as reflected light (B), and another portion of the infrared wavelength band of the incident light (A) is transmitted and transmitted to the second sensor (300) as transmitted light (C).

[0048] Each material generally has different wavelengths depending on the material's inherent properties. The different properties of various materials affect the absorption spectrum received by the first sensor (200) and the second sensor (300). Incident light (A) from each material can be used to determine the properties of the object. For example, if the first sensor (200) and the second sensor (300) are positioned to detect different parts of the wavelength spectrum according to the incident light (A), the first sensor (200) and the second sensor (300) can each determine the total energy of different ranges of the wavelength spectrum. The processor (400) can determine the material properties for various wavelength ranges based on the total energy determined by the first sensor (200) and the second sensor (300).

[0050] The first sensor (200) receives a first beam (B) separated by a beam splitter (100). That is, the first sensor (200) receives a beam (B) reflected by the beam splitter (100) from the incident light (A) incident from the area containing the sample (1). The first sensor (200) detects and generates a first wavelength spectrum of the received first beam (B). The first sensor (200) analyzes the generated first wavelength spectrum and outputs a first characteristic value (V1). The first sensor (200) transmits the first characteristic value (V1) to a processor (400).

[0051] The second sensor (300) receives the second beam (C) separated by the beam splitter (100). That is, the second sensor (300) receives the beam (B) that has passed through the beam splitter (100) among the incident light (A) incident from the area containing the sample (1). The second sensor (300) detects and generates the second wavelength spectrum of the received second beam (C). The second sensor (300) analyzes the generated second wavelength spectrum and outputs a second characteristic value (V2). The second sensor (300) transmits the second characteristic value (V2) to the processor (400).

[0052] The first wavelength spectrum band and the second wavelength spectrum band are included in the long-wave infrared (LWIR) region.

[0053] The first sensor (200) and the second sensor (300) each generate a wavelength spectrum including the infrared wavelength bands of the first beam (B) and the second beam (C) separated by the beam splitter (100). The first sensor (200) and the second sensor (300) may be an infrared sensor (IR sensor) and / or an infrared camera (IR camera) capable of detecting infrared wavelength bands.

[0055] The infrared wavelength band is located between visible light and microwaves and is generally divided into four regions: the NIR band, SWIR band, MWIR band, and LWIR band.

[0056] The NIR and SWIR bands are photodetector infrared bands. The NIR band is a region from 0.7 µm to 1 µm that is distinct from visible light and can be detected by silicon-based image sensors; it is currently the most actively applied region in industry, including the medical field, LiDAR sensor systems, and night vision goggles. The SWIR band is a region from 1 µm to 2.5 µm, and while various applications are expected as a visual safety wavelength, its application fields are extremely limited to space exploration and military use due to the high cost of InGaAs, which was almost the only high-efficiency photodetector.

[0057] The MWIR (midwavelength infrared) and LWIR (long-wavelength infrared) bands are thermal detection infrared bands. MWIR (midwavelength infrared) is divided into a band from 3 µm to 5 µm, and LWIR (long-wavelength infrared) is divided into a band from 8 µm to 14 µm, respectively, and are used to measure the temperature of an object by detecting radiant heat emitted from a target or to determine the composition of a material through spectrum analysis.

[0059] As described above, the beam splitter (100) splits incident light (A) into a plurality of beams (B, C) having different infrared (IR) wavelength bands, and the first sensor (200) and the second sensor (300) receive each of the split beams (B, C) and detect and generate wavelength spectra of different infrared wavelength bands from the received beams (B, C). The first sensor (200) and the second sensor (300) detect infrared light in the LWIR (long-wavelength infrared) band in particular.

[0061] Figure 2 is a diagram showing the infrared wavelength spectrum.

[0062] In the case of the LWIR band, as illustrated in Fig. 2, it includes a fingerprint region with material-specific characteristics within the wavelength spectrum. The fingerprint region is within the infrared wavelength spectrum (4000-400 cm⁻¹). -1 ) Among them, at 1500 cm⁻¹, stretching vibrations of mainly CC, CN, CO, and CX single bonds and bending vibrations of CH, NH, and OH are observed. -1 It refers to a lower frequency region. In the spectra of organic compounds, the fingerprint region generally consists of many overlapping vibration absorption bands. The intensity of each vibration absorption band varies, and due to vibrational mixing, spectroscopic band assignment to the bands appearing in the fingerprint region is not easy. However, based on the fact that the fingerprint region spectrum differs for each compound, it can be utilized to identify compounds, much like human fingerprints. The infrared wavelength spectrum of the fingerprint region is useful for identifying compounds through comparison with existing known infrared spectrum libraries.

[0064] Figure 3 is a diagram showing examples of the first wavelength spectrum and the second wavelength spectrum.

[0065] The first wavelength spectrum and the second wavelength spectrum are each determined by the intrinsic spectrum of at least one substance contained in the sample (1) and the wavelength separation characteristics of the beam splitter (100).

[0066] The beam splitter (100) separates incident light (A) (the intrinsic spectrum of the material) into reflected light (B) and transmitted light (C) according to the wavelength separation characteristics of each beam splitter (100). According to the intrinsic wavelength separation characteristics, the beam splitter (100) reflects some wavelength range of infrared light in the LWIR band and transmits it to the first sensor (200), and transmits other wavelength ranges and transmits them to the second sensor (300).

[0067] S1 in FIG. 3 represents the first wavelength spectrum of reflected light (B) that is reflected by the beam splitter (100) and transmitted to the first sensor (200). S2 in FIG. 3 represents the second wavelength spectrum of transmitted light (C) that is transmitted through the beam splitter (100) and transmitted to the second sensor (300).

[0068] The wavelength spectrum of FIG. 3 may vary depending on the wavelength separation characteristics of the beam splitter (100). For example, the beam splitter (100) may have wavelength separation characteristics that reflect infrared rays in a lower frequency range and transmit infrared rays in a higher frequency range.

[0070] FIG. 4 is a diagram exemplarily showing the intrinsic spectrum of a certain substance as one embodiment, and FIG. 5 is a diagram exemplarily showing the intrinsic spectrum of a certain substance separated into a first wavelength spectrum and a second wavelength spectrum as one embodiment.

[0071] For example, if the intrinsic spectrum of a certain substance is as shown in FIG. 4, the intrinsic spectrum of the specific substance can be separated into a first beam (B) and a second beam (C) by a beam splitter (100) and transmitted to a first sensor (200) and a second sensor (300), respectively. The first sensor (200) and the second sensor (300) each receive the first beam (B) and the second beam (C) separated by the beam splitter (100), and can detect and generate a first wavelength spectrum and a second wavelength spectrum based on the received first beam (B) and second beam (C).

[0072] At this time, for example, if the intensity of the low frequency range of the intrinsic spectrum of a certain material is stronger than the intensity of another frequency range of a higher frequency range, the wavelength spectrum of the low frequency range transmitted to the first sensor (200) as shown in FIG. 5 may be expressed more strongly, and the wavelength spectrum of the higher frequency range transmitted to the second sensor (300) may be expressed less strongly.

[0073] In FIG. 5, S1 represents a first wavelength spectrum, and S2 represents a second wavelength spectrum. A first sensor (200) calculates a first characteristic value (V1) based on the first wavelength spectrum, and a second sensor (300) calculates a second characteristic value (V2) based on the second wavelength spectrum. The first characteristic value (V1) represents the integral value of the first wavelength spectrum. The second characteristic value (V2) represents the integral value of the second wavelength spectrum. The first sensor (200) and the second sensor (300) transmit and output the calculated first characteristic value (V1) and second characteristic value (V2) to a processor (400).

[0075] The processor (400) receives a first characteristic value (V1) and a second characteristic value (V2) from the first sensor (200) and the second sensor (300). Based on the received first characteristic value (V1) and second characteristic value (V2), the processor (400) derives an eigenvalue (<λ>) as shown in FIG. 5. The eigenvalue (<λ>) derived by the processor (400) represents the representative wavelength of at least one substance contained in the sample (1). The processor (400) calculates the eigenvalue (<λ>) according to the integral value of the first wavelength spectrum and the second wavelength spectrum and the wavelength separation characteristic of the beam splitter (100) as shown in <Equation 1> below.

[0077]

[0078] <Formula 1>

[0080] As expressed in Equation 1, the eigenvalue (<λ>) is determined as the weighted mean of the first characteristic value (V1) and the second characteristic value (V2) that reflect the wavelength separation characteristics of the beam splitter (100).

[0081] The weighted average refers to a variation of the arithmetic mean calculated by multiplying each individual value by a weight. As the processor (400) uses the weighted average when calculating the eigenvalue (<λ>), it can calculate the representative wavelength of the material considering the wavelength separation characteristics of the beam splitter (100).

[0082] The first wavelength spectrum and the second wavelength spectrum may be modified by the intrinsic spectrum of at least one material contained in the sample (1) as described above. Accordingly, the first characteristic value (V1) and the second characteristic value (V2) are variables that change depending on the material. w1 and w2 are values ​​determined by the wavelength separation characteristics of the beam splitter (100) and are constants that do not change unless the beam splitter (100) is modified.

[0084] FIG. 6 is a diagram showing the reflected light separation characteristics of a beam splitter (100) as an embodiment, and FIG. 7 is a diagram showing the transmitted light separation characteristics of a beam splitter (100) as an embodiment. FIG. 8 is a diagram showing the first and second wavelength spectra of a material that is reflected and transmitted by the beam splitter (100) and reaches the first sensor (200) and the second sensor (300).

[0085] The beam splitter (100) may have wavelength separation characteristics that separate incident light (A) into different wavelength regions, as illustrated in FIGS. 6 and 7 as an embodiment. FIG. 6 illustrates the reflected light separation characteristics of the beam splitter (100), specifically 900 to 700 cm within the fingerprint area. -1 It shows the reflected light separation characteristics of a beam splitter (100) that does not reflect infrared light within a range. FIG. 7 illustrates the transmitted light separation characteristics of the beam splitter (100), showing the 1300 ~ 1100 cm area of ​​the fingerprint region. -1 The following shows the transmitted light separation characteristics of a beam splitter (100) that does not transmit infrared light within the range below.

[0086] The graphs shown in the upper and lower left of FIG. 8, respectively, represent the intrinsic spectrum of at least one specific substance contained in the sample (1). When incident light (A) containing the intrinsic spectrum of a specific substance is reflected and transmitted through a beam splitter (100) having wavelength separation characteristics as shown in FIG. 6 and FIG. 7, the resulting values ​​reaching the first sensor (200) and the second sensor (300), respectively—namely, the first wavelength spectrum and the second wavelength spectrum—are as shown in the upper and lower right of FIG. 8, respectively. The first wavelength spectrum is shown in the upper right of FIG. 8, and the second wavelength spectrum is shown in the lower right of FIG. 8.

[0087] The reflected light separation characteristics of the beam splitter (100) are 900 to 700 cm in the fingerprint area as shown in FIG. 6. -1 In the case of having wavelength separation characteristics that do not reflect infrared rays within the range, 900 to 700 cm within the fingerprint area at incident light (A) -1 Reflected light having a first wavelength spectrum with the infrared region within the range removed reaches the first sensor (200).

[0088] As shown in FIG. 7, the transmitted light separation characteristics of the beam splitter (100) are 1300 to 1100 cm in the fingerprint area. -1 In the case of having wavelength separation characteristics that do not transmit infrared rays within the range, 1300 ~ 1100 cm of the fingerprint area at incident light (A) -1 Transmitted light having a second wavelength spectrum with the infrared region within the range below removed reaches the second sensor (300).

[0089] The first sensor (200) calculates a first characteristic value (V1) based on the first wavelength spectrum received, and the second sensor (300) calculates a second characteristic value (V2) based on the second wavelength spectrum received.

[0091] Generalizing the above <Equation 1>, it can be expressed as the following <Equation 2>.

[0093]

[0094] <Equation 2>

[0096] In <Equation 2>, V1 and V2 are the integral values ​​of the first wavelength spectrum and the second wavelength spectrum, as in <Equation 1>. a, b, c, and d are constants determined according to the wavelength separation characteristics of the beam splitter (100). a in <Equation 2> is expressed as 1 in <Equation 1>, b in <Equation 2> is expressed as 1 in <Equation 1>, c in <Equation 2> is expressed as w1 in <Equation 1>, and d in <Equation 2> is expressed as w2 in <Equation 1>.

[0098] <Equation 2> can be derived in the following way.

[0099] If the brightness of a specific wavelength (λ) is denoted as I(λ), then the total brightness is as shown in <Equation 3> below. silver It can be expressed as.

[0100] The aforementioned eigenvalue (<λ>) represents the average of the brightness of all wavelengths within the fingerprint area, and It can be expressed as follows.

[0102]

[0103]

[0104] <Equation 3>

[0106] In addition, assuming there exists a relationship where the sum of the two functions is 1 or λ regardless of wavelength, and assuming that <Equation 4> below holds,

[0108]

[0109]

[0110] <Equation 4>

[0112] The total brightness and integral values ​​of each wavelength spectrum are summarized as shown in <Equation 5> and <Equation 6> below.

[0114]

[0115]

[0116]

[0117]

[0118] <Equation 5>

[0120]

[0121]

[0122]

[0123]

[0124] <Equation 6>

[0126] Therefore, by rearranging <Equation 5> and <Equation 6>, the above <Equation 2> can be derived.

[0128] The processor (400) can identify or specify at least one substance contained in the sample (1) based on the calculated eigenvalue (<λ>).

[0129] The processor (400) may further include a memory that stores a material-eigenvalue table in which an eigenvalue (<λ>) corresponding to each material is stored.

[0130] The processor (400) can identify the substance contained in the sample (1) by matching the eigenvalue (<λ>) derived by calculation and the corresponding eigenvalue (<λ>) in the stored substance-eigenvalue (<λ>) table.

[0131] The processor (400) can update the material-eigenvalue table by matching the eigenvalue (<λ>) derived by calculation with the material specified based on the eigenvalue (<λ>) and storing it in memory.

[0133] FIG. 9 is a diagram showing the flow of the material detection process of the material detection device and material detection system according to the present invention.

[0134] The processor (400) has a total energy value of incident light (A) incident from an area containing a sample (1). ) and eigenvalues ​​(<λ>) are mapped onto the HSV (Hue-Saturation-Value) space to obtain HSV values, and the HSV values ​​are converted into RGB values ​​to obtain a color image.

[0135] Referring to FIG. 9, the processor (400) [requires] a total energy value ( ) and eigenvalue (<λ>) (representative wavelength) are derived. The processor (400) derives the derived total energy value ( ) and eigenvalues ​​(<λ>) are mapped onto the HSV (Hue-Saturation-Value) space to obtain HSV values.

[0136] HSV is a method of specifying color using Hue, Saturation, and Value.

[0137] The processor (400) derives the total energy value ( ) and eigenvalues ​​(<λ>) are visualized by replacing them with Hue-Saturation-Values ​​(HSV) in the visible light region. Here, the Hue value is set to the eigenvalue (<λ>), and the Value is the total energy value ( It is set to ). The saturation value is set to a constant (e.g., 100%) to obtain the HSV value.

[0138] The processor (400) converts the acquired HSV values ​​into RGB using a known HSV technique and reconstructs the images received from the first sensor (200) and the second sensor (300) based on the converted RGB values.

[0139] The image reconstructed by the processor (400) based on RGB values ​​can be displayed through the display (500). The display (500) displays at least one specific material as a colored image.

[0141] FIGS. 10 and FIGS. 11 are drawings showing a material detection device and a material detection system according to another embodiment of the present invention.

[0142] Referring to FIG. 10, a material detection device and material detection system according to another embodiment of the present invention may include a plurality of lenses (10). In this case, the lenses (10) may include a first lens (11) composed of a concave lens (10) that first reflects incident light (A), a second lens (13) composed of a convex lens (10) that reflects incident light (A) reflected from the first lens (11), and a third lens (15) that transmits incident light (A) reflected from the second lens (13) to a beam splitter (100). The plurality of lenses (10) amplify the incident light (A). However, this is not limited thereto, and embodiments of other lenses (10) capable of amplifying the intensity of the incident light (A) may also be applied.

[0143] Referring to FIG. 11, a material detection device and a material detection system according to another embodiment of the present invention may include a plurality of IR cameras (200, 300, 600). Additionally, a beam splitter (100) may also include a plurality of beam splitters (100, 110). Here, the beam splitter (100, 110) may be a dichroic beam splitter (100, 110) that separates incident light (A) incident from an area containing a sample (1) into a plurality of beams having different infrared (IR) wavelength bands. Each of the plurality of beam splitters (100, 110) may have different infrared wavelength regions for reflection and transmission.

[0144] A plurality of beam IR cameras (200, 300, 600) each receive a plurality of beams through a dichroic beam splitter (100, 110) and each generate a plurality of output signals. The plurality of output signals generated by the plurality of beam IR cameras (200, 300, 600) include the aforementioned first wavelength spectrum and second wavelength spectrum.

[0145] As described above, the processor (400) receives a plurality of output signals generated from a plurality of IR cameras (200, 300, 600), combines the plurality of output signals according to preset conditions to obtain a combination signal, identifies at least one material based on the combination signal, and matches a color corresponding to at least one identified material. Here, the combination signal obtained by the processor (400) is a first characteristic value (V1), a second characteristic value (V2), an eigenvalue (<λ>) and a total energy value of incident light (A) that are output based on the first wavelength spectrum and the second wavelength spectrum described above. ...includes ... etc. Here, the pre-set condition is determined according to the wavelength separation characteristics of the dichroic beam splitter (100, 110). Here, matching a color corresponding to at least one specific material includes obtaining an HSV value by mapping the total energy value and the eigenvalue (<λ>) of the incident light (A) incident from the area containing the sample (1) onto the HSV (Hue-Saturation-Value) space, and obtaining a color image by converting the HSV value into an RGB value.

[0147] The display (500), as described above, displays at least one specific material as a colored image.

[0149] FIGS. 12 to 16 are flowcharts showing the flow of a substance detection method according to the present invention.

[0150] Referring to FIG. 12, the method for detecting a substance according to the present invention comprises the steps of: a beam splitter (100) separating incident light (A) incident from a region containing a sample (1) into a first beam (B) having a first wavelength spectrum and a second beam (C) having a second wavelength spectrum different from the first wavelength spectrum (S100); a first sensor (200) receiving the first beam (B) to detect a first characteristic value (V1) and a second sensor (300) receiving the second beam (C) to detect a second characteristic value (V2) (S110); and a processor (400) deriving an eigenvalue (<λ>) based on the first characteristic value (V1) and the second characteristic value (V2), and identifying at least one substance contained in the sample (1) based on the derived eigenvalue (<λ>) (S120).

[0151] Referring to FIG. 13, the method for detecting a substance according to the present invention further includes, after step S120, a step (S121) in which a processor (400) stores an eigenvalue (<λ>) and a substance specified based on the eigenvalue (<λ>).

[0152] Step S120, with reference to FIG. 14, includes the step (S1201) in which a processor (400) identifies a material corresponding to the derived eigenvalue based on a previously stored material-eigenvalue table.

[0153] Referring to FIG. 15, the method for detecting a substance according to the present invention, after step S120, the processor (400) determines the total energy value of the incident light (A) incident from the region containing the sample (1) ( The method further includes the step (S130) of obtaining HSV values ​​by mapping ) and eigenvalues ​​(<λ>) onto the HSV (Hue-Saturation-Value) space, and obtaining a color image corresponding to a specific material by converting the obtained HSV values ​​into RGB values.

[0154] Referring to FIG. 16, the method for detecting a substance according to the present invention further includes a step (S131) ​​of displaying a color image obtained on a display (500) after step S130.

[0155] A detailed description of each step of the substance detection method is as previously stated.

[0157] Hereinafter, the results of detecting a substance using the substance detection device, substance detection system, and substance detection method according to the present invention are described.

[0158] Table 1 below shows the transmitted brightness, reflected brightness, sum of transmitted brightness and reflected brightness, difference between transmitted brightness and reflected brightness, and ratio of sum and difference of the substances included in the samples, and Table 2 shows the mean, standard deviation, and error of the difference between transmitted brightness and reflected brightness and the ratio of sum and difference of each substance.

[0160] Referring to , five samples each of PET, PP, and LDPE were placed in the sample, and the brightness of the first and second beams in the long-wavelength infrared region reflected and transmitted by the beam splitter was measured through the first sensor and the second sensor, and the sum and difference between the transmitted brightness and the reflected brightness, and the ratio thereof were calculated.

[0162]

[0163] <Table 1: Calculation of Brightness and Ratio for Each Material>

[0165] That is, as a result of the calculation, it can be seen that the average ratio for PET is 123.7, the average ratio for PP is 101.4, and the average ratio for LDPE is 73.3.

[0166] Therefore, it can be seen that the substances contained in the sample can be identified based on the ratio of the sum and difference calculated based on the transmitted brightness and reflected brightness using only 2-band infrared separated into two by a beam splitter.

[0168] For example, as shown in below, if the ratio of the sum and difference calculated based on the transmitted brightness and the reflected brightness is within a 2% error range of 123.7, the material can be determined to be PET. Also, if the ratio of the sum and difference calculated based on the transmitted brightness and the reflected brightness is within a 5% error range of 101.4, the material can be determined to be PP. And if the ratio of the sum and difference calculated based on the transmitted brightness and the reflected brightness is within a 10% error range of 73.3, the material can be determined to be LDPE.

[0170]

[0171] <Table 2: Average values ​​for ratios in Table 1, etc.>

[0173] As shown in and , it can be seen that plastics have unique values ​​depending on their type. and above are examples, and as the measurement data increases, the substance can be detected more accurately.

[0175] The memory according to the present invention may include a database management system (hereinafter, DBMS). A DBMS is a set of software tools that enables multiple users to access data within the memory. The DBMS may include IMS, CODASYL DB, DB2, ORACLE, INFORMIX, SYBASE, INGRES, MS-SQL, Objectivity, O2, Versanat, Ontos, Gemstone, Unisql, Object Store, Starburst, Postgres, Tibero, MySQL, or MS-access. The DBMS makes it possible to access specific data corresponding to the input of a specific command.

[0176] In this specification, the processor (400) may include processors (400) that execute a series of processes stored in memory. It may operate as software modules driven and controlled by the processor (400). Furthermore, the processor (400) may be a hardware device.

[0177] The scope of protection of the present invention is not limited to the description and expression of the embodiments explicitly described above. Furthermore, it is added once again that the scope of protection of the present invention cannot be limited by obvious changes or substitutions in the technical field to which the present invention belongs. Explanation of the symbols

[0178] 1 : Sample 10 : Lens 11: First lens 13: Second lens 15: Third lens 100 : Beam Splitter 200: 1st sensor 300: Second sensor 400 : Processor 500 : Display 600: Third sensor

Claims

Claim 1 A beam splitter that separates incident light incident from a region containing a sample into a first beam having a first wavelength spectrum and a second beam having a second wavelength spectrum different from the first wavelength spectrum; a first sensor configured to receive the first beam and output a first characteristic value based on the first wavelength spectrum; a second sensor configured to receive the second beam and output a second characteristic value based on the second wavelength spectrum; and a processor that derives an eigenvalue based on the first characteristic value and the second characteristic value, and identifies at least one substance contained in the sample based on the eigenvalue. A substance detection device comprising: a first characteristic value is an integral value of the first wavelength spectrum, and a second characteristic value is an integral value of the second wavelength spectrum, wherein the first wavelength spectrum and the second wavelength spectrum are each determined by the intrinsic spectrum of at least one substance included in the sample and the wavelength separation characteristic of the beam splitter, and the intrinsic value is determined as a weighted mean value of the first characteristic value and the second characteristic value reflecting the wavelength separation characteristic of the beam splitter. Claim 2 A material detection device according to claim 1, wherein the first sensor receives a beam reflected by the beam splitter among incident light incident from an area containing a sample, and the second sensor receives a beam transmitted through the beam splitter among incident light incident from an area containing a sample. Claim 3 A material detection device according to claim 1, wherein the first wavelength spectrum band and the second wavelength spectrum band are included in the long-wave infrared (LWIR) region. Claim 4 A material detection device according to claim 1, wherein the first wavelength spectrum band and the second wavelength spectrum band are 8 μm or more and 14 μm or less. Claim 5 delete Claim 6 delete Claim 7 A substance detection device according to claim 1, wherein the eigenvalue is derived by the following relationship between the first characteristic value and the second characteristic value. (Here, constants a, b, c, and d are determined by the wavelength separation characteristics of the beam splitter) Claim 8 A material detection device according to claim 1, wherein the processor obtains an HSV value by correlating the total energy value of incident light incident from a region containing the sample with the eigenvalue in a Hue-Saturation-Value (HSV) space, and obtains a color image by converting the HSV value into RGB values. Claim 9 A material detection device according to claim 8, further comprising a display for displaying the acquired color image. Claim 10 A material detection device according to claim 1, further comprising a lens that collects incident light incident from an area containing a sample and guides it to the beam splitter. Claim 11 A method for detecting a substance, comprising the steps of: separating incident light incident from a region containing a sample into a first beam having a first wavelength spectrum and a second beam having a second wavelength spectrum different from the first wavelength spectrum using a beam splitter; receiving the first beam to detect a first characteristic value and receiving the second beam to detect a second characteristic value; deriving an eigenvalue based on the first characteristic value and the second characteristic value, and identifying at least one substance contained in the sample based on the eigenvalue, wherein the first characteristic value is an integral value of the first wavelength spectrum and the second characteristic value is an integral value of the second wavelength spectrum, wherein the first wavelength spectrum and the second wavelength spectrum are each determined by the eigenvalue of at least one substance contained in the sample and the wavelength separation characteristic of the beam splitter, and the eigenvalue is determined as a weighted mean value of the first characteristic value and the second characteristic value reflecting the wavelength separation characteristic of the beam splitter. Claim 12 A substance detection method according to claim 11, further comprising the step of storing the above-mentioned unique value and the above-mentioned specified substance corresponding to the above-mentioned unique value. Claim 13 In claim 11, the step of identifying at least one substance included in the sample based on the eigenvalue is a substance detection method that identifies a substance corresponding to the eigenvalue based on a previously stored substance-eigenvalue table. Claim 14 A method for detecting a substance according to claim 11, further comprising the step of obtaining an HSV value by correlating the total energy value of incident light incident from an area containing the sample and the eigenvalue in a Hue-Saturation-Value (HSV) space, and obtaining a color image corresponding to the specified substance by converting the HSV value into RGB values. Claim 15 A substance detection method according to claim 14, further comprising the step of displaying the acquired color image. Claim 16 A dichroic beam splitter that separates incident light incident from an area containing a sample into a plurality of beams, each beam having a first wavelength spectrum and a second beam having a second wavelength spectrum different from the first wavelength spectrum; a plurality of IR cameras that receive the first beam and output a first characteristic value based on the first wavelength spectrum, and receive the second beam and output a second characteristic value based on the second wavelength spectrum; a processor that receives a plurality of output signals including the first characteristic value and the second characteristic value from the plurality of IR cameras, derives an eigenvalue based on the first characteristic value and the second characteristic value, identifies at least one substance contained in the sample based on the eigenvalue, and matches a color corresponding to the at least one identified substance; and a display that displays the at least one identified substance as a color-assigned image; wherein the first characteristic value is an integral value of the first wavelength spectrum and the second characteristic value is an integral value of the second wavelength spectrum, and the first wavelength spectrum and the second wavelength spectrum are, A substance detection system in which the intrinsic spectrum of at least one substance contained in a sample and the wavelength separation characteristics of the dichroic beam splitter are each determined, and the intrinsic value is determined as the weighted mean value of the first characteristic value and the second characteristic value reflecting the wavelength separation characteristics of the dichroic beam splitter. Claim 17 delete Claim 18 A material detection system according to claim 16, wherein matching a color corresponding to at least one specified material comprises matching the total energy value of incident light incident from an area containing the sample with the eigenvalue in a Hue-Saturation-Value (HSV) space to obtain an HSV value, and converting the HSV value into an RGB value to obtain a color image.

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