A method, device and system for uniformity detection of a multispectral sensor

Abstract

This application relates to the field of sensor detection and provides a method, apparatus, and system for uniformity detection of multispectral sensors. The method includes: acquiring at least one frame of multispectral data, along with the optical power value and exposure time of the corresponding light source; extracting channel data from the multispectral data and grouping the channel data corresponding to different channels into several groups of channel data; calculating the first filter response data for each channel corresponding to different frames of multispectral data based on the channel data, the optical power value of the corresponding light source, and the exposure time; dividing the channels into spatial regions and calculating the second filter response data corresponding to each channel in each spatial region based on the first filter response data; and obtaining the uniformity detection result based on the offset between the second filter response data corresponding to different channels in each spatial region. This method enables uniformity detection of multispectral sensors, facilitating adjustments to the manufacturing process of multispectral sensors.

Description

Technical Field

[0001] This application belongs to the field of sensor detection technology, and in particular relates to a method, apparatus and system for detecting the uniformity of a multispectral sensor. Background Technology

[0002] Multispectral imaging is one of the main imaging technologies currently available. The multispectral data collected by multispectral sensors not only contains image information but also spectral information. The spectral information can reflect the spectral intensity of each pixel in various bands, which is helpful for qualitative and quantitative analysis of the target to be detected.

[0003] Current multispectral sensors only provide an average filter curve and do not take into account the non-uniformity of different spatial regions. Since the spatial uniformity of multispectral sensors affects their imaging performance, how to detect the uniformity of multispectral sensors has become an urgent problem to be solved. Summary of the Invention

[0004] This application provides a method, apparatus, and system for detecting the uniformity of a multispectral sensor, which can solve the above-mentioned problems.

[0005] In a first aspect, embodiments of this application provide a method for uniformity detection of a multispectral sensor, comprising: acquiring at least one frame of multispectral data collected by the multispectral sensor, and the light power value and exposure time of the light source corresponding to each frame of multispectral data; extracting channel data from the multispectral data, and grouping the channel data corresponding to different channels to obtain several groups of channel data; calculating the first filter response data of each channel corresponding to different frames of multispectral data based on the channel data, the light power value and exposure time of the light source corresponding to the multispectral data; dividing the channel into spatial regions, and calculating the second filter response data of the channel in each spatial region based on the first filter response data of the channel; and obtaining the uniformity detection result of the multispectral sensor based on the offset between the second filter response data of different channels in each spatial region.

[0006] Secondly, embodiments of this application provide a uniformity detection device for a multispectral sensor, comprising: an acquisition unit for acquiring at least one frame of multispectral data collected by the multispectral sensor, and the light power value and exposure time of the light source corresponding to each frame of multispectral data; a grouping unit for extracting channel data from the multispectral data and grouping the channel data corresponding to different channels to obtain several groups of channel data; a first calculation unit for calculating first filter response data of each channel corresponding to different frames of multispectral data based on the channel data, the light power value of the light source corresponding to the multispectral data, and the exposure time; a second calculation unit for dividing the channel into spatial regions and calculating the second filter response data of the channel in each spatial region based on the first filter response data of the channel; and a detection unit for obtaining the uniformity detection result of the multispectral sensor based on the offset between the second filter response data of different channels in each spatial region.

[0007] Thirdly, embodiments of this application provide a uniformity detection system for a multispectral sensor, including a monochromator, an optical power meter, a processor, a memory, and a computer program stored in the memory and executable on the processor; the monochromator is used to provide a narrow-band light source for the multispectral sensor; the optical power meter is used to measure the optical power value of the light source; and the processor is used to implement the method as described in the first aspect when executing the computer program.

[0008] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in the first aspect above.

[0009] In this embodiment, several frames of multispectral data are first acquired. Each frame of multispectral data can be collected by a multispectral sensor under light sources of different wavelengths. The multispectral data includes channel data for several channels, and the light power value and exposure time of the corresponding light source for each frame. Next, channel data for the same channel in each frame is extracted to obtain several sets of channel data. Based on these sets of channel data, the light power value of the corresponding light source, and the exposure time of the corresponding light source, the first filter response data corresponding to each channel is obtained. Then, the channel data for each channel in each frame is spatially divided, and based on the first filter response data for each channel, the second filter response data for each channel in each spatial region is obtained. Finally, based on the second filter response data for each channel in each spatial region, the uniformity detection result of the multispectral sensor is obtained. This method, based on extracting filter response data from different spatial regions and determining the consistency between filter response curves, achieves uniformity detection of the multispectral sensor, effectively aiding in the quality inspection of the multispectral sensor and facilitating adjustments to the manufacturing process. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a schematic diagram of the structure of the uniformity detection system of the multispectral sensor provided in the embodiments of this application;

[0012] Figure 2 This is a schematic flowchart of a method for detecting the uniformity of a multispectral sensor provided in the first embodiment of this application;

[0013] Figure 3 This is a schematic diagram showing the first channel image provided in an embodiment of this application;

[0014] Figure 4 This is a schematic diagram showing the spatial region division of the first channel image provided in an embodiment of this application;

[0015] Figure 5 This is a schematic diagram of the uniformity detection device for the multispectral sensor provided in the second embodiment of this application. Detailed Implementation

[0016] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0017] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0018] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0019] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0020] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0021] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0022] A multispectral sensor is a visual sensor that utilizes multispectral imaging technology to acquire multispectral data. Multispectral data includes channel data from multiple channels, the number of which is related to the wavelength resolution of the sensor. Each channel captures light at a specific wavelength; one channel corresponds to one wavelength. Since multispectral data contains spectral information from multiple bands, at least two channels corresponding to different wavelengths can be obtained. Before mass production, multispectral sensors are typically prototyped and tested to ensure they meet expected performance requirements. Uniformity testing of the multispectral sensor is used to check whether the light signal received by each channel of the manufactured sensor is uniform. On the production line, after uniformity testing of the prototype, if the uniformity does not meet expectations, adjustments to the production process are necessary to ensure that the performance of subsequently mass-produced multispectral sensors meets expectations.

[0023] Please see Figure 1 , Figure 1This is a schematic diagram of the structure of the uniformity detection system for the multispectral sensor provided in this embodiment. In this embodiment, the uniformity detection system for the multispectral sensor includes a processor 110, a memory 111, a computer program 112 stored in the memory 111 and executable on the processor 110, a monochromator 120, and an optical power meter 121.

[0024] Monochromator 120 is a spectrophotometer suitable for generating monochromatic light. The monochromator can output a series of independent optical signals with sufficiently narrow spectral ranges, and the wavelength of the output optical signals can be continuously adjusted as needed. In this embodiment, monochromator 120 is used to provide a narrow-band optical signal to the multispectral sensor under test. Optical power meter 121 is an instrument used to measure absolute optical power or the relative loss of optical power through a section of optical fiber. In this embodiment, optical power meter 121 is used to measure the optical power value of the optical signal output by monochromator 120. Processor 110, memory 111, and computer program 112 stored in memory 111 and executable on processor 110 can be integrated into an electronic device; no limitation is imposed here.

[0025] In this embodiment, the uniformity detection system for a multispectral sensor can be used to detect the uniformity of a multispectral sensor. To better illustrate how this system detects the uniformity of a multispectral sensor, the detection principle is briefly explained below:

[0026] The filter response data for each channel of a multispectral sensor is related to the quantum efficiency, which is defined as the ratio of the number of photoelectrons generated by the absorption of incident photons by the multispectral sensor to the number of incident photons. It reflects the spectral sensitivity of the imaging device within the spectral response range.

[0027] When measuring quantum efficiency, the energy of a monochromatic uniform light source under a certain light intensity is first measured using an optical power meter, and the output signal of the device under that monochromatic light is tested. The light energy E received by a single pixel at a specific wavelength within a certain integration time is calculated according to formula (1). λ .

[0028]

[0029] Where P is the output power of the optical power meter, and A d Let A be the area of ​​the sensor pixel being measured, T be the sensor integration time, and A be the area of ​​the sensor pixel being measured. s The area of ​​the optical power meter probe is denoted as .

[0030] The energy of a single photon at a specific wavelength λ can be calculated using formula (2).

[0031]

[0032] Where h is Planck's point constant and c is the speed of light in a vacuum.

[0033] Calculate the number of photons N received by each pixel during the integration time using formula (3). λ .

[0034]

[0035] Among them, E λ E represents the light energy received by each pixel within a certain integration time. γ This represents the energy of a single photon.

[0036] Then, calculate the number N of electrons generated and collected for each pixel according to formula (4). e . +

[0037]

[0038] Where V0 represents the sensor output signal (Dn), S V This represents the sensor's conversion gain (DN / e-).

[0039] According to the definition of quantum efficiency, the quantum efficiency η is calculated using formula (5).

[0040]

[0041] Where, N e N represents the number of electrons in each pixel. λ This represents the number of photons received by each pixel during the integration time.

[0042] The quantum efficiency testing steps include: First, calibrating the optical power at different wavelengths. Place the optical power meter probe at position A in front of a monochromatic uniform light source, ensuring that the monochromatic light source can uniformly illuminate the optical power meter probe. Within the measurement wavelength range, scan the monochromator with a fixed step size and record the optical power value P at each wavelength. Remove the optical power meter and place the sensor under test at the same position A, ensuring that the monochromatic light source can uniformly illuminate the surface of the sensor under test. Within the measurement wavelength range, scan the monochromator with a fixed step size. At each wavelength, adjust the sensor exposure time so that the sensor output value reaches half saturation, and record the exposure time T and the average sensor output value (DN) at each wavelength. To facilitate material acquisition, the image sensor exposure time can also be fixed, but it must be ensured that the sensor output is not overexposed. Here, exposure time refers to the exposure time of the sensor, and the average output value refers to the signal value of the sensor, expressed in digital quantity (DN). Since it is necessary to obtain the number of photoelectrons (e-) collected by the sensor within a certain exposure time, and the number of electrons is difficult to measure, the ratio of the output signal value (DN) to the conversion gain (DN / e-) is used to indirectly calculate the number of electrons (e-). The conversion gain (the unit can be DN / e- or uV / e-) can be understood as the conversion coefficient of the signal conversion stage (charge > voltage > digital signal) acquired by the image sensor.

[0043] Please see Figure 2 , Figure 2 This is a schematic flowchart illustrating a method for uniformity detection of a multispectral sensor according to the first embodiment of this application. In this embodiment, the execution subject of the method for uniformity detection of a multispectral sensor is a system or electronic device with a multispectral sensor uniformity detection function, such as an image sensor and camera performance testing device, a PC, and a mobile phone, etc. Figure 2 The uniformity detection method for the multispectral sensor shown may include:

[0044] S201: Acquire at least one frame of multispectral data collected by the multispectral sensor, as well as the light power value and exposure time of the light source corresponding to each frame of multispectral data.

[0045] To ensure more accurate testing, multispectral sensors are typically controlled to acquire multiple frames of multispectral data to prevent measurement bias. Furthermore, to assess the uniformity of the multispectral sensor under various light source conditions, the acquired multispectral data is preferably collected under light sources of different wavelengths. In one optional implementation, the light sources of different wavelengths are monochromatic light of different wavelengths.

[0046] Optionally, the wavelength intervals between the aforementioned light sources in different wavelength bands are fixed. For example, a monochromatic light (e.g., 400nm, 450nm, ... 950nm) can be taken every 50nm (using 50nm intervals as an example; in actual applications, the wavelength intervals can be arbitrarily designed according to requirements) within the 400–950nm wavelength range to obtain 12 different wavelengths of monochromatic light. Optionally, the wavelength intervals of the aforementioned monochromatic light can also be non-fixed, which is not limited in detail here. In an optional embodiment, considering that the monochromator can provide a relatively uniform monochromatic light spot, monochromatic light of different wavelengths can be emitted by the monochromator.

[0047] In an alternative implementation, before acquiring multispectral data, the lens of the multispectral sensor can be removed and a light homogenizer can be installed to ensure that the monochromatic light entering the multispectral sensor is as uniform as possible on the sensing surface of the multispectral sensor, thereby improving the accuracy of subsequent uniformity detection results.

[0048] In one embodiment, a multispectral sensor acquires A frames of multispectral data, with each frame corresponding to a light source in a different wavelength band, i.e., there are A wavelength bands of light sources. If the multispectral sensor has B channels, then each frame of multispectral data includes channel data acquired under the first channel, channel data acquired under the second channel, and channel data acquired under the same wavelength band light source, up to the Bth channel. Both A and B represent positive integers.

[0049] It is understandable that the channel data records information about the grayscale image. If the channel data is represented as a matrix, then the matrix stores the pixel values ​​of each pixel in the grayscale image. Therefore, it can be understood that each multispectral data includes information about the grayscale image under the first channel, information about the grayscale image under the second channel, and information about the grayscale image under the B channel.

[0050] In a specific embodiment, uniformity detection is performed on a 9-channel multispectral sensor to acquire 12 frames of multispectral data collected by the sensor. That is, the value of A is 12, the value of B is 9, therefore, the multispectral data includes MSI1(m,n,b), MSI2(m,n,b), ..., MSI12(m,n,b). MSI1(m,n,b) represents the multispectral data acquired under a first wavelength light source, MSI2(m,n,b) represents the multispectral data acquired under a second wavelength light source, and so on, with MSI12(m,n,b) representing the multispectral data acquired under a twelfth wavelength light source. Here, the channel data is represented as a matrix, where m represents the row, n represents the column, and b is an integer between [1, B]. If B is 9, then b is an integer between [1, 9].

[0051] In addition to the above, the optical power values ​​of the corresponding light sources in each frame of multispectral data can be collected by a power meter. The optical power values ​​are denoted as P, including P1, P2, ..., P12. The processor will also record the exposure time, which is denoted as E, including E1, E2, ..., E12.

[0052] It is understandable that the optical power values ​​of the light sources corresponding to the channel data collected under the same light source band are the same. Since the value of A in this embodiment is 12, there are a total of 12 optical power values, P1, P2 to P12.

[0053] Furthermore, the exposure times corresponding to the channel data collected under the same light source are the same. Since the value of A in this embodiment is 12, there are a total of 12 exposure times, E1, E2 to E12.

[0054] S202: Extract channel data from multispectral data and group the channel data corresponding to different channels to obtain several groups of channel data.

[0055] As mentioned above, if the multispectral sensor under test has B channels, then each frame of multispectral data includes channel data under the first channel, channel data under the second channel, and channel data under the Bth channel. Therefore, the processor can extract the channel data under the same channel in each frame of multispectral data to obtain B sets of channel data.

[0056] In one optional implementation, the processor extracts channel data from the same channel in each frame of multispectral data, and combines the channel data from the same channel along a preset dimension to obtain several sets of channel data. Specifically, the processor extracts channel data from the first channel in each frame of multispectral data, and combines the channel data from the first channel along a preset dimension to obtain the first set of channel data. The processor extracts channel data from the second channel in each frame of multispectral data, and combines the channel data from the second channel along a preset dimension to obtain the second set of channel data. Similarly, the processor extracts channel data from the Bth channel in each frame of multispectral data, and combines the channel data from the Bth channel along a preset dimension to obtain the Bth set of channel data. Here, the preset dimension refers to the third dimension besides the dimensions of the rows and columns of the matrix. Therefore, the several sets of channel data ultimately obtained by the processor refer to the aforementioned first set of channel data, second set of channel data, and so on up to the Bth set of channel data.

[0057] In one alternative implementation, the value of A is configured to be 12 and the value of B is configured to be 9.

[0058] The processor extracts the channel data of the first channel in each frame of multispectral data. The channel data of the first channel are represented as C1_400, C1_450, C1_500, C1_550, C1_600, C1_650, C1_700, C1_750, C1_800, C1_850, C1_900 and C1_950 respectively.

[0059] Wherein, C1_400 refers to the channel data of the first channel acquired under monochromatic light at a wavelength of 400nm, C1_450 refers to the channel data of the first channel acquired under monochromatic light at a wavelength of 450nm, C1_500 refers to the channel data of the first channel acquired under monochromatic light at a wavelength of 500nm, C1_550 refers to the channel data of the first channel acquired under monochromatic light at a wavelength of 550nm, C1_600 refers to the channel data of the first channel acquired under monochromatic light at a wavelength of 600nm, and C1_650 refers to the number of channels in the first channel acquired under monochromatic light at a wavelength of 650nm. According to the data, C1_700 refers to the channel data of the first channel acquired under monochromatic light at a wavelength of 700nm, C1_750 refers to the channel data of the first channel acquired under monochromatic light at a wavelength of 750nm, C1_800 refers to the channel data of the first channel acquired under monochromatic light at a wavelength of 800nm, C1_850 refers to the channel data of the first channel acquired under monochromatic light at a wavelength of 850nm, C1_900 refers to the channel data of the first channel acquired under monochromatic light at a wavelength of 900nm, and C1_950 refers to the channel data of the first channel acquired under monochromatic light at a wavelength of 950nm.

[0060] C1_400=MSI1(m,n,1), C1_450=MSI2(m,n,1), C1_500=MSI3(m,n,1), C1_550=MSI4(m,n,1), C1_600=MSI5(m,n,1), C1_650=MSI6(m,n,1), C 1_700=MSI7(m,n,1), C1_750=MSI8(m,n,1), C1_800=MSI9(m,n,1), C1_850=MSI10(m,n,1), C1_900=MSI11(m,n,1), C1_950=MSI12(m,n,1).

[0061] The channel data under the first channel are combined in a preset dimension to obtain the first group of channel data, which is represented as Response1(m,n,12).

[0062] Response1(m,n,12)=[C1_400,C1_450,C1_500,C1_550,C1_600,C1_650,C1 _700,C1_750,C1_800,C1_850,C1_900,C1_950]=[MSI1(m,n,1),MSI2(m,n, 1),MSI3(m,n,1),MSI4(m,n,1),MSI5(m,n,1),MSI6(m,n,1),MSI7(m,n,1), MSI8(m,n,1),MSI9(m,n,1),MSI10(m,n,1),MSI11(m,n,1),MSI12(m,n,1)].

[0063] Please see Figure 3 , Figure 3 This is a schematic diagram showing the first channel image provided in an embodiment of this application. As described above, the processor extracts the channel data of the first channel from each frame of multispectral data, and combines the channel data of the first channel in a preset dimension to obtain a first set of channel data. The first set of channel data records information about the first channel image.

[0064] Specifically, if the wavelengths of different monochromatic lights are 400nm, 450nm, ... 950nm, then, as Figure 3 As shown, the first set of channel data includes the first channel image acquired under monochromatic light with a wavelength of 400nm, 450nm, 500nm, 550nm, 600nm, 650nm, 700nm, 750nm, 800nm, 850nm, 900nm, and 950nm.

[0065] Understandably, the processor can also obtain the second set of channel data and the third to ninth sets of channel data in the same way. The second set of channel data and the third to ninth sets of channel data are represented as Response2(m,n,12), Response2(m,n,12), ..., Response9(m,n,12), respectively.

[0066] Response2(m,n,12)=[MSI1(m,n,2),MSI2(m,n,2),MSI3(m,n,2),MSI4(m,n,2),MSI5(m,n,2),MSI6 (m,n,2),MSI7(m,n,2),MSI8(m,n,2),MSI9(m,n,2),MSI10(m,n,2),MSI11(m,n,2),MSI12(m,n,2)]

[0067] Response3(m,n,12)=[MSI1(m,n,3),MSI2(m,n,3),MSI3(m,n,3),MSI4(m,n,3),MSI5(m,n,3),MSI6 (m,n,3),MSI7(m,n,3),MSI8(m,n,3),MSI9(m,n,3),MSI10(m,n,3),MSI11(m,n,3),MSI12(m,n,3)] ......

[0069] Response9(m,n,12)=[MSI1(m,n,9),MSI2(m,n,9),MSI3(m,n,9),MSI4(m,n,9),MSI5(m,n,9),MSI6 (m,n,9),MSI7(m,n,9),MSI8(m,n,9),MSI9(m,n,9),MSI10(m,n,9),MSI11(m,n,9),MSI12(m,n,9)]

[0070] S203: Calculate the first filter response data for each channel corresponding to different frames of multispectral data based on the light power value and exposure time of the light source corresponding to the channel data and multispectral data.

[0071] The processor calculates the first filter response data for each channel corresponding to different frames of multispectral data based on the optical power value and exposure time of the light source corresponding to the channel data and multispectral data. In an optional implementation, the processor obtains the relative filter parameters corresponding to each frame of multispectral data by multiplying the optical power value of the light source corresponding to each frame of multispectral data by the exposure time of the corresponding light source.

[0072] As mentioned above, if the value of configuration A is 12, the optical power value is represented by P, including P1, P2, ..., P12, and the exposure time is represented by E, including E1, E2, ..., E12. The relative filter parameters corresponding to each frame of multispectral data are represented by P*E, including P1*E1, P2*E2, P3*E3, P4*E4, P5*E5, P6*E6, P7*E7, P8*E8, P9*E9, P10*E10, P11*E11, and P12*E12. P1*E1 represents the relative filter parameters corresponding to the multispectral data acquired under the first wavelength light source, P2*E2 represents the relative filter parameters corresponding to the multispectral data acquired under the second wavelength light source, and so on, with P12*E12 representing the relative filter parameters corresponding to the multispectral data acquired under the twelfth wavelength light source.

[0073] The first wavelength, the second wavelength, and the twelfth wavelength can be 400nm, 450nm, or 950nm. For details, please refer to the description of the monochromatic light wavelengths used in the embodiments of this application.

[0074] Then, the processor obtains the first filter response data for each channel based on the relative filter parameters corresponding to several sets of channel data and multispectral data of each frame.

[0075] If the multispectral sensor has 9 channels, the first filter response data corresponding to each channel includes the first filter response data corresponding to the first channel, the first filter response data corresponding to the second channel, the first filter response data corresponding to the third channel, the first filter response data corresponding to the fourth channel, the first filter response data corresponding to the fifth channel, the first filter response data corresponding to the sixth channel, the first filter response data corresponding to the seventh channel, the first filter response data corresponding to the eighth channel, and the first filter response data corresponding to the ninth channel.

[0076] The first filter response data corresponding to the first channel is represented as QE1(m,n,12).

[0077] QE1(m,n,12)=[MSI1(m,n,1) / (P1*E1),MSI2(m,n,1) / (P2*E2),MSI3(m,n,1) / (P3*E3),MSI4(m,n,1) / (P4*E4),MSI5(m,n,1) / (P5*E5),MSI6(m,n,1) / (P6*E6 ),MSI7(m,n,1) / (P7*E7),MSI8(m,n,1) / (P8*E8),MSI9(m,n,1) / (P9*E9),MSI1 0(m,n,1) / (P10*E10),MSI11(m,n,1) / (P11*E11),MSI12(m,n,1) / (P12*E12)].

[0078] Wherein, MSI1(m,n,1) / (P1*E1) represents the first filter response data corresponding to the first channel under monochromatic light of the first wavelength, MSI2(m,n,1) / (P2*E2) represents the first filter response data corresponding to the first channel under monochromatic light of the second wavelength, and so on, MSI12(m,n,1) / (P12*E12) represents the first filter response data corresponding to the first channel under monochromatic light of the twelfth wavelength.

[0079] For explanations of other symbols, please refer to the above; they will not be repeated here.

[0080] Similarly, the first filter response data corresponding to the second channel is represented as QE2(m,n,12).

[0081] QE2(m,n,12)=[MSI1(m,n,2) / (P1*E1),MSI2(m,n,2) / (P2*E2),MSI3(m,n,2) / (P3*E3),MSI4(m,n,2) / (P4*E4),MSI5(m,n,2) / (P5*E5),MSI6(m,n,2) / (P6*E6 ),MSI7(m,n,2) / (P7*E7),MSI8(m,n,2) / (P8*E8),MSI9(m,n,2) / (P9*E9),MSI 10(m,n,2) / (P10*E10),MSI11(m,n,2) / (P11*E11),MSI12(m,n,2) / (P12*E12)]

[0082] Similarly, the first filter response data corresponding to the ninth channel is represented as QE9(m,n,12).

[0083] QE9(m,n,12)=[MSI1(m,n,9) / (P1*E1),MSI2(m,n,9) / (P2*E2),MSI3(m,n,9) / (P3*E3),MSI4(m,n,9) / (P4*E4),MSI5(m,n,9) / (P5*E5),MSI6(m,n,9) / (P6*E6 ),MSI7(m,n,9) / (P7*E7),MSI8(m,n,9) / (P8*E8),MSI9(m,n,9) / (P9*E9),MSI 10(m,n,9) / (P10*E10),MSI11(m,n,9) / (P11*E11),MSI12(m,n,9) / (P12*E12)]

[0084] S204: Divide the channel into spatial regions, and calculate the second filter response data of the channel in each spatial region based on the first filter response data of the channel.

[0085] The processor divides the channel data of each channel in each frame of multispectral data into spatial regions. That is, if the value of the number of channels B is 9, the processor divides the first group of channel data, the second group of channel data, and even the ninth channel data into spatial regions. It can also be simply understood as the processor dividing the first channel image, the second channel image, and even the ninth channel image into spatial regions.

[0086] Understandably, in step S203, the first filter response data corresponding to each channel includes the filter response value of each pixel. Directly deriving the filter response value of each pixel to determine the uniformity of the multispectral sensor would require excessive computation, and the large amount of data would not achieve the desired uniformity detection effect. Therefore, it is necessary to divide the spatial region of the channel into reasonable spatial regions suitable for uniformity detection, and then calculate the average of the first filter response values ​​of all pixels within each spatial region to obtain the corresponding second filter response data for each spatial region. In uniformity detection, it is preferable to divide the spatial region of each channel into 4, 9, 16, 25, or 36 relatively equal-sized blocks.

[0087] Please see Figure 4 , Figure 4 This is a schematic diagram illustrating the spatial region division of the first channel image provided in an embodiment of this application. As a non-limiting example, this embodiment divides the spatial region of the first channel into 9 blocks. Then, the processor obtains the second filter response data corresponding to each channel in each spatial region based on the first filter response data corresponding to each channel.

[0088] In one optional implementation, the processor acquires the first filter response data corresponding to each channel in each spatial region, calculates the average value of the first filter response data corresponding to each channel in each spatial region, and obtains the second filter response data corresponding to each channel in each spatial region.

[0089] The second filter response data in the first spatial region corresponding to the first channel is represented as Qe_Loc1.

[0090] Qe_Loc1=[mean(QE1[x_Loc1,y_Loc1,1]),mean(QE1[x_Loc1,y_Loc1,2]),...mean(QE1[x_Loc1,y_Loc1,12])]

[0091] Where Mean represents the mean, x_Loc1 represents the row of the matrix corresponding to the first spatial region, and y_Loc1 represents the column of the matrix corresponding to the first spatial region.

[0092] QE1[x_Loc1,y_Loc1,1] represents the first filter response data of the first channel in the first spatial region under monochromatic light of the first wavelength; QE1[x_Loc1,y_Loc1,2] represents the first filter response data of the first channel in the first spatial region under monochromatic light of the second wavelength; and so on, QE1[x_Loc1,y_Loc1,12] represents the first filter response data of the first channel in the first spatial region under monochromatic light of the twelfth wavelength.

[0093] Similarly, the second filter response data in the second spatial region corresponding to the first channel to the second filter response data in the ninth spatial region corresponding to the first channel can be obtained, denoted as Qe_Loc2 to Qe_Loc9 respectively.

[0094] Qe_Loc2=[mean(QE1[x_Loc2,y_Loc2,1]),mean(QE1[x_Loc2,y_Loc2,2]),...mean(QE1[x_Loc2,y_Loc2,12])]

[0095] Qe_Loc9=[mean(QE1[x_Loc9,y_Loc9,1]),mean(QE1[x_Loc9,y_Loc9,2]),...mean(QE1[x_Loc9,y_Loc9,12])]

[0096] The meanings of the symbols in Qe_Loc2 to Qe_Loc9 can be derived from the parameter description of Qe_Loc1.

[0097] Based on the same calculation method, the second filter response data for each channel in each spatial region can be obtained.

[0098] S205: Based on the offset between the second filter response data corresponding to different channels in each spatial region, the uniformity detection result of the multispectral sensor is obtained.

[0099] The processor obtains the uniformity detection result of the multispectral sensor based on the second filter response data corresponding to each channel in each spatial region. For different channels, the processor can obtain the uniformity detection result of the multispectral sensor on that channel based on the second filter response data corresponding to that channel in each spatial region.

[0100] Specifically, the greater the difference between the second filter response data in each spatial region corresponding to the channel, the worse the spatial uniformity of the multispectral sensor in that channel; conversely, the smaller the difference between the second filter response data in each spatial region corresponding to the channel, the better the spatial uniformity of the multispectral sensor in that channel.

[0101] In one optional implementation, the processor obtains the filter curve consistency detection result for each channel based on the second filter response data for each spatial region corresponding to each channel and a preset filter curve consistency detection algorithm; and obtains the uniformity detection result for each channel of the multispectral sensor based on the filter curve consistency detection result for each channel.

[0102] The second filter response data for each channel in each spatial region records the filter response curve information for each channel in each spatial region. The more consistent the filter response curves for a given channel across different spatial regions, the better the spatial uniformity of the multispectral sensor for that channel. Conversely, the more inconsistent the filter response curves for a given channel across different spatial regions, the worse the spatial uniformity of the multispectral sensor for that channel.

[0103] The aforementioned preset filter curve consistency detection algorithm is used to detect the consistency of the filter response curve.

[0104] Specifically, in one optional implementation, the preset filter curve consistency detection algorithm can refer to an existing cosine angle calculation algorithm or an existing correlation coefficient calculation algorithm.

[0105] The filter response curves will not be illustrated here. However, it's understood that the more consistent the filter response curves are, the more overlap they will have visually. Ideally, the filter response curves should completely overlap.

[0106] In this embodiment, the more consistent the filter response curves, the better the uniformity detection result of the multispectral sensor; conversely, the more inconsistent the filter response curves, the worse the uniformity detection result. When accepting a multispectral sensor, the uniformity detection result can serve as an indicator for evaluating its quality, and thus has broad application value.

[0107] In this embodiment, the processor first acquires several frames of multispectral data. Each frame of multispectral data is collected by the multispectral sensor under light sources of different wavelengths. Each frame of multispectral data includes channel data for several channels, and the corresponding light power value and exposure time of the light source. Next, the processor extracts the channel data for the same channel from each frame of multispectral data, obtaining several sets of channel data. Based on these sets of channel data, the corresponding light power value of the light source, and the corresponding exposure time of the light source, the processor obtains the first filter response data corresponding to each channel. Then, the processor divides the channel data for each channel in each frame of multispectral data into spatial regions, and based on the first filter response data corresponding to each channel, obtains the second filter response data corresponding to each channel in each spatial region. Finally, the processor obtains the uniformity detection result of the multispectral sensor based on the second filter response data corresponding to each channel in each spatial region. This method, based on extracting filter response data from different spatial regions and determining the consistency between filter response curves, achieves uniformity detection of the multispectral sensor, effectively aiding in the quality inspection of the multispectral sensor and facilitating the adjustment of the multispectral sensor's manufacturing process.

[0108] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0109] Please see Figure 5 , Figure 5 This is a schematic diagram of the uniformity detection device for a multispectral sensor provided in the second embodiment of this application. The included units are used to perform... Figure 2 The steps in the corresponding embodiments. Please refer to the details. Figure 2 The relevant descriptions in the corresponding embodiments are shown below. For ease of explanation, only the parts relevant to this embodiment are shown. See also... Figure 5 The uniformity detection device 5 of the multispectral sensor includes:

[0110] The acquisition unit 510 is used to acquire at least one frame of multispectral data collected by the multispectral sensor, as well as the light power value and exposure time of the light source corresponding to each frame of multispectral data;

[0111] Grouping unit 520 is used to extract channel data from multispectral data and group the channel data corresponding to different channels to obtain several groups of channel data.

[0112] The first calculation unit 530 is used to calculate the first filter response data of each channel corresponding to different frames of multispectral data based on the light power value and exposure time of the light source corresponding to the channel data and multispectral data.

[0113] The second calculation unit 540 is used to divide the channel into spatial regions and calculate the second filter response data of the channel in each spatial region based on the first filter response data of the channel.

[0114] The detection unit 550 is used to obtain the uniformity detection result of the multispectral sensor based on the offset between the second filter response data corresponding to different channels in each spatial region.

[0115] Furthermore, the grouping unit 520 is specifically used to: extract channel data from each group of multispectral data, and combine the channel data under the same channel in a preset dimension to obtain several groups of channel data.

[0116] Furthermore, the first calculation unit 530 is specifically used to: obtain the relative filter parameters corresponding to the multispectral data based on the product of the light power value of the light source corresponding to the multispectral data and the exposure time of the corresponding light source; and obtain the first filter response data corresponding to the channel based on the channel data and the relative filter parameters corresponding to the multispectral data.

[0117] Furthermore, the detection unit 550 is specifically used to: obtain the filter curve consistency detection result corresponding to different channels based on the second filter response data corresponding to different channels in each spatial region and a preset filter curve consistency detection algorithm; and obtain the uniformity detection result of the multispectral sensor based on the filter curve consistency detection result corresponding to different channels.

[0118] In this embodiment of the application, the electronic device may include one or more processors 110 ( Figure 1 (Only one is shown in the image), a memory 111, and a computer program 112 stored in the memory 111 and executable on one or more processors 110, such as an evaluation program for the color characterization capability of a multispectral sensor. When one or more processors 110 execute the computer program 112, they can implement the various steps in the embodiment of the method for evaluating the color characterization capability of a multispectral sensor. Alternatively, when one or more processors 110 execute the computer program 112, they can implement the functions of the units in the embodiment of the uniformity detection device for a multispectral sensor, which is not limited here.

[0119] Those skilled in the art will understand that Figure 1 The electronic devices shown are merely examples and do not constitute a limitation on electronic devices. Electronic devices may include more or fewer components than shown, or combinations of certain components, or different components. For example, electronic devices may also include input / output devices, network access devices, buses, etc.

[0120] In one embodiment, the processor 110 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0121] In one embodiment, memory 111 can be an internal storage unit of an electronic device, such as a hard drive or RAM. Memory 111 can also be an external storage device of the electronic device, such as a plug-in hard drive, smart media card (SMC), secure digital (SD) card, flash card, etc. Furthermore, memory 111 can include both internal and external storage units. Memory 111 is used to store computer programs and other programs and data required by the electronic device. Memory 111 can also be used to temporarily store data that has been output or will be output.

[0122] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units is used as an example. In practical applications, the above functions can be assigned to different functional units as needed, that is, the internal structure of the device can be divided into different functional units to complete all or part of the functions described above. The functional units in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0123] This application also provides a network device, which includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor executes the computer program to implement the steps in any of the above method embodiments.

[0124] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps described in the various method embodiments above.

[0125] This application provides a computer program product that, when run on a mobile terminal, enables the mobile terminal to implement the steps described in the above-described method embodiments.

[0126] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a photographing device / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0127] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0128] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0129] In the embodiments provided in this application, it should be understood that the disclosed apparatus / device and method can be implemented in other ways. For example, the apparatus / device embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0130] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0131] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for detecting the uniformity of a multispectral sensor, characterized in that, include: Acquire at least one frame of multispectral data collected by a multispectral sensor, as well as the light power value and exposure time of the light source corresponding to each frame of multispectral data; Extract the channel data from the multispectral data, and group the channel data corresponding to different channels to obtain several groups of channel data; Based on the channel data, the light power value of the light source corresponding to the multispectral data, and the exposure time, calculate the first filter response data of each channel corresponding to different frames of multispectral data; The channel is divided into spatial regions, and the second filter response data corresponding to the channel in each spatial region is calculated based on the first filter response data corresponding to the channel. The uniformity detection result of the multispectral sensor is obtained based on the offset between the second filter response data corresponding to different channels in each spatial region.

2. The method for detecting the uniformity of a multispectral sensor as described in claim 1, characterized in that, The process involves extracting channel data from the multispectral data and grouping the channel data corresponding to different channels to obtain several groups of channel data, including: Extract the channel data from each group of multispectral data, and combine the channel data of the same channel in a preset dimension to obtain several groups of channel data.

3. The method for detecting the uniformity of a multispectral sensor as described in claim 1, characterized in that, The step of calculating the first filter response data for each channel corresponding to different frames of multispectral data based on the channel data, the optical power value of the light source corresponding to the multispectral data, and the exposure time includes: The relative filtering parameters corresponding to the multispectral data are obtained by multiplying the optical power value of the light source corresponding to the multispectral data with the exposure time of the light source. Based on the relative filtering parameters corresponding to the channel data and the multispectral data, the first filtering response data corresponding to the channel is obtained.

4. The method for detecting the uniformity of a multispectral sensor as described in claim 1, characterized in that, The step of obtaining the uniformity detection result of the multispectral sensor based on the offset between the second filter response data corresponding to different channels in each spatial region includes: Based on the second filter response data corresponding to different channels in each spatial region and the preset filter curve consistency detection algorithm, the filter curve consistency detection results corresponding to different channels are obtained. The uniformity detection result of the multispectral sensor is obtained based on the consistency detection results of the filter curves corresponding to different channels.

5. A uniformity detection device for a multispectral sensor, characterized in that, include: The acquisition unit is used to acquire at least one frame of multispectral data collected by a multispectral sensor, as well as the light power value and exposure time of the light source corresponding to each frame of multispectral data; A grouping unit is used to extract channel data from the multispectral data and group the channel data corresponding to different channels to obtain several groups of channel data. The first calculation unit is used to calculate the first filter response data of each channel corresponding to different frames of multispectral data based on the channel data, the light power value of the light source corresponding to the multispectral data and the exposure time; The second calculation unit is used to divide the channel into spatial regions and calculate the second filter response data of the channel in each spatial region based on the first filter response data of the channel. The detection unit is used to obtain the uniformity detection result of the multispectral sensor based on the offset between the second filter response data corresponding to different channels in each spatial region.

6. The uniformity detection device for a multispectral sensor as described in claim 5, characterized in that, The grouping unit is specifically used to extract channel data from each group of multispectral data, and combine the channel data under the same channel in a preset dimension to obtain several groups of channel data.

7. The uniformity detection device for a multispectral sensor as described in claim 5, characterized in that, The first calculation unit is specifically used to obtain the relative filter parameters corresponding to the multispectral data based on the product of the light power value of the light source corresponding to the multispectral data and the exposure time of the corresponding light source; and to obtain the first filter response data corresponding to the channel based on the channel data and the relative filter parameters corresponding to the multispectral data.

8. The uniformity detection device for a multispectral sensor as described in claim 5, characterized in that, The detection unit is specifically used to obtain the filter curve consistency detection result corresponding to different channels based on the second filter response data corresponding to different channels in each spatial region and the preset filter curve consistency detection algorithm. The uniformity detection result of the multispectral sensor is obtained based on the consistency detection results of the filter curves corresponding to different channels.

9. A uniformity detection system for a multispectral sensor, characterized in that, It includes a monochromator, an optical power meter, a processor, a memory, and a computer program stored in the memory and executable on the processor; The monochromator is used to provide a narrow band of light source for the multispectral sensor; The optical power meter is used to measure the optical power value of the light source; The processor is configured to implement the steps of the method as described in any one of claims 1 to 4 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 4.

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