Sample structure characteristic and component characteristic coupling detection method and system

Abstract

The invention relates to the technical field of sample detection, particularly provides a sample structure feature and component feature coupling detection method and system, and aims to solve the problem that the structure feature and the component feature of a sample cannot be obtained at the same time in an existing detection mode. In order to achieve the purpose, the sample structure characteristic and component characteristic coupling detection method comprises the steps that an image and an energy spectrum of a to-be-detected sample are obtained; obtaining image data reflecting the structural characteristics of the sample to be detected according to the image; obtaining energy spectrum data reflecting component characteristics of the sample to be detected according to the energy spectrum; and fusing the image data and the energy spectrum data to obtain a fused data set. According to the method, the image data and the energy spectrum data of the to-be-detected sample are fused to obtain the fused data set, so that the structural defect information and the element composition information in the to-be-detected sample can be intuitively obtained, and the comprehensiveness of sample detection is improved.

Description

Technical Field

[0001] The present application relates to the technical field of sample detection, and specifically provides a method and system for coupling detection of sample structural characteristics and component characteristics. Background Art

[0002] Neutron imaging is a relatively mature nondestructive testing technology. It obtains the light signal after the neutron beam passes through the test sample, and converts the above light signal into an image through specific imaging technology. The grayscale values ​​of pixels at different positions in the image reflect the internal structure information of the sample to be tested.

[0003] However, although the above-mentioned neutron imaging method can detect the internal structural defects of the sample, it is impossible to obtain the elemental composition information inside the sample. If the elemental composition information inside the sample is to be obtained, other technical means must be adopted. Therefore, it is difficult to achieve simultaneous detection of the above two dimensions of the sample.

[0004] Accordingly, the art needs a new technical solution to solve the above problems. Summary of the invention

[0005] The present application aims to solve the above-mentioned technical problem, that is, to solve the problem that the existing detection method cannot simultaneously obtain and realize the structural characteristics and composition characteristics of the sample.

[0006] In a first aspect, the present application provides a method for coupling detection of sample structure characteristics and composition characteristics, comprising:

[0007] Acquire the image and energy spectrum of the sample to be tested;

[0008] Obtaining image data reflecting the structural features of the sample to be detected according to the image;

[0009] Obtaining image data reflecting the structural features of the sample to be detected according to the image;

[0010] The image data and the energy spectrum data are fused to obtain a fused data set.

[0011] In a technical solution of the above-mentioned coupling detection method, the image data is an image gray value data set based on coordinate points, and the step of "obtaining image data reflecting the structural characteristics of the sample to be detected according to the image" includes:

[0012] determining a target area based on the image;

[0013] Traversing the pixel points in the target area to obtain the grayscale value of the pixel at each coordinate point in the target area;

[0014] The grayscale values ​​of the pixels at each coordinate point are integrated to generate the image grayscale value data set.

[0015] In a technical solution of the above coupling detection method, the image gray value data set is an image gray value matrix or an image gray value table.

[0016] In a technical solution of the above-mentioned coupling detection method, the energy spectrum data is a characteristic peak-to-peak value data set based on gamma energy values, wherein different gamma energy values ​​represent different types of elements, and the step of "obtaining energy spectrum data reflecting the component characteristics of the sample to be detected according to the energy spectrum" includes:

[0017] Determining the element type based on the horizontal axis of the energy spectrum;

[0018] Obtaining the characteristic peak value corresponding to each element according to the vertical axis of the energy spectrum;

[0019] The characteristic peak-to-peak value data corresponding to each element are integrated to generate the characteristic peak-to-peak value data set.

[0020] In a technical solution of the above coupling detection method, the characteristic peak-to-peak value data set is a characteristic peak-to-peak value array or a characteristic peak-to-peak value table.

[0021] In a technical solution of the above coupling detection method, the step of “fusing the image data with the energy spectrum data to obtain a fused data set” includes:

[0022] Unifying the dimension and size of the image gray value dataset and the characteristic peak value dataset to obtain a normalized image gray value dataset and a normalized characteristic peak value dataset respectively;

[0023] The normalized image gray value data set and the normalized feature peak value data set are fused to obtain a fused data set.

[0024] In a technical solution of the above coupling detection method, the step of “unifying the dimension and size of the image gray value dataset and the characteristic peak-to-peak value dataset” includes:

[0025] Performing dimensionality reduction on the image gray value dataset;

[0026] The measurement range of the image gray value data set and the measurement range of the characteristic peak-to-peak value data set are adjusted to the same range.

[0027] In a technical solution of the above coupling detection method, the “reducing the dimension of the image gray value data set” includes:

[0028] The dimension of the image gray value data set is reduced by principal component analysis.

[0029] In a technical solution of the above coupling detection method, the step of “fusing the normalized image data set and the normalized energy spectrum data set to obtain a fused data set” includes:

[0030] The normalized image gray value dataset and the normalized feature peak value dataset are spatially aligned and then merged to obtain a fused dataset.

[0031] In a second aspect, the present application provides a sample structure feature and composition feature coupling detection system, the coupling detection system comprising:

[0032] A neutron source for generating source neutrons;

[0033] An imaging device, which is used to receive the optical signal after the source neutrons pass through the sample to be detected and thus generate an image;

[0034] A neutron activation analysis device, which is used to receive characteristic radiation generated after the source neutrons bombard the sample to be detected and thus generate an energy spectrum;

[0035] An image data processing device, which is used to obtain image data reflecting the structural characteristics of the sample to be detected according to the image;

[0036] An energy spectrum data processing device, which is used to obtain energy spectrum data reflecting the component characteristics of the sample to be detected according to the energy spectrum;

[0037] A data fusion device is used to fuse the image data with the energy spectrum data to obtain a fused data set.

[0038] As mentioned above, under the condition of adopting the above-mentioned technical scheme, the present application, based on neutron photography technology, combines a neutron activation analysis device to simultaneously analyze the internal element composition and content of the sample to be tested, converts the image information and energy spectrum information into data information through data processing, and merges the image data and energy spectrum data into a data set. Based on the data set, the structural defect information and elemental composition information inside the sample to be tested can be intuitively obtained, thereby improving the comprehensiveness of the detection, thereby providing more comprehensive reference information in industrial applications, which is of great significance to the design and manufacture of industrial products. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The preferred embodiments of the present application are described below in conjunction with the accompanying drawings, in which:

[0040] Figure 1 It is a flow chart of the main steps of a method for coupling detection of sample structure characteristics and composition characteristics according to an embodiment of the present application;

[0041] Figure 2is a detailed flow chart of the steps of a method for coupling detection of sample structure characteristics and composition characteristics according to an embodiment of the present application;

[0042] Figure 3 It is a schematic block diagram of a sample structure feature and composition feature coupling detection system according to an embodiment of the present application. DETAILED DESCRIPTION

[0043] The preferred embodiments of the present application are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present application and are not used to limit the scope of protection of the present application. Those skilled in the art can make adjustments to them as needed to adapt to specific application scenarios.

[0044] It should be noted that in the description of this application, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the drawings. This is only for the convenience of description, and does not indicate or imply that the relevant devices or components must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on this application. In addition, the ordinal numbers "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0045] In addition, it should be noted that in the description of this application, unless otherwise clearly specified and limited, the terms "installation" and "connection" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium. For those skilled in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0046] In order to facilitate understanding of the technical solution of the present application, the detection method of samples in industrial applications is first briefly described. Taking pyrotechnics as the sample to be detected as an example, it may have different structural defects at different positions. Therefore, in some related technologies, imaging detection is usually performed on the sample to be detected based on neutron photography to obtain images of different positions of the sample to be detected, and the type of structural defects at a specific position is further determined based on the later data processing process (for example, cracks and fractures in the gunpowder column). However, for pyrotechnics, structural defects are only one dimension that reflects its overall state. The elemental composition information inside the pyrotechnics cannot be obtained through the above-mentioned neutron photography method, and the elemental composition information inside the sample also has a certain reference value (for example, for pyrotechnics, by detecting the hydrogen content in the sample, the water content in the sample can be determined, and then it can be determined whether there is water ingress). Therefore, the method of detecting sample structural defects simply through neutron photography has great limitations.

[0047] Reference Figure 1 , is a flow chart of the main steps of a method for coupling detection of sample structure characteristics and composition characteristics according to an embodiment of the present application, which includes:

[0048] S101: Acquire the image and energy spectrum of the sample to be detected.

[0049] It should be noted that the detection method of the present application is based on the detection of a neutron source. Specifically, an imaging device and a neutron activation analysis device are provided in front of the neutron source. The sample to be detected can be placed between the receiving port of the imaging device and the receiving port of the neutron activation analysis device. The neutron beam generated by the neutron source passes through the sample to be detected and then enters the imaging device. The imaging device receives the light signal and thus generates the above-mentioned image. At the same time, the neutron beam generated by the neutron source generates characteristic radiation (gamma rays) when bombarding the sample to be detected. The neutron activation analysis device receives the characteristic radiation and thus generates the above-mentioned energy spectrum (the energy spectrum described in the present application is a gamma energy spectrum). For the bombardment area of ​​the sample to be detected, an energy spectrum diagram is generated. The energy spectrum diagram includes characteristic peaks corresponding to different elements, and the height of the characteristic peaks indicates the content of the element. It should be understood that the above-mentioned elements in the present application may include multiple nuclides. For example, the energy spectrum diagram may include three nuclides of hydrogen. 1 H. 2 H. 3 H, at this time 1 H. 2 H. 3 H each has its own characteristic peak.

[0050] Optionally, during the image acquisition process, the image may be preprocessed to perform denoising and edge enhancement to ensure pixel clarity, and then image features may be extracted through a filter (eg, a Laplacian filter) to ensure the quality of the output image.

[0051] S102: Obtaining image data reflecting structural features of the sample to be detected based on the image.

[0052] The above image can roughly reflect the structural defect information of the inspection area of ​​the sample to be inspected, such as cracks, holes, etc. Based on the image data processing system, the image obtained in the above step S101 is converted into data information, that is, the above image data, and the specific information such as the depth of cracks and the size of holes is further analyzed through the image data.

[0053] S103: Obtaining energy spectrum data reflecting the component characteristics of the sample to be detected according to the energy spectrum.

[0054] The energy spectrum can roughly reflect the element composition information of the detection area of ​​the sample to be detected, such as the type of element, the relative size of the content of each element, etc. Based on the energy spectrum data processing system, the energy spectrum obtained in the above step S101 is converted into data information, that is, the above energy spectrum data, and the specific content of each element is intuitively displayed through the energy spectrum data.

[0055] S104: Fusing the image data with the energy spectrum data to obtain a fused data set.

[0056] Through step S104, the two data sets of image data and energy spectrum data are fused to obtain a fused data set, and the fused data set simultaneously reflects the structural characteristics and component characteristics of the sample to be detected.

[0057] As mentioned above, based on neutron photography technology, the present application combines a neutron activation analysis device to simultaneously analyze the internal element composition and content of the sample to be tested, converts the image information and energy spectrum information into data information through data processing, and merges the image data and energy spectrum data into a data set. Based on the data set, the structural defect information and elemental composition information inside the sample to be tested can be intuitively obtained, the detection dimension can be improved, and more comprehensive reference information can be provided in industrial applications, which is of great significance to the design and manufacture of industrial products.

[0058] Reference Figure 2 , is a detailed flow chart of the steps of a method for coupling detection of sample structure characteristics and composition characteristics according to an embodiment of the present application, which is as follows:

[0059] In one embodiment of the present application, step S102 specifically includes the following steps:

[0060] S1021: Determine a target area based on the image.

[0061] It should be noted that in step S1021, the target area can be determined according to actual needs. For example, for pyrotechnics, after neutron imaging of the ignition end of the sample to be tested, the target area can be the entire area of ​​the ignition end, that is, the overall image obtained; in some other cases, if it is desired to analyze a local area of ​​the ignition end (that is, a more detailed local position), then the target area can be obtained by cropping the overall image.

[0062] S1022: Traverse the pixel points in the target area to obtain the grayscale value of the pixel at each coordinate point in the target area.

[0063] In the data processing process of step S1022, the grayscale image is first loaded through the image processing library (such as OpenCV), and then a two-dimensional coordinate system is generated based on the target area, and the corresponding grayscale value data is obtained for each coordinate point after traversal. The grayscale image is composed of multiple pixels, and the grayscale value range of the pixel is [0,255].

[0064] S1023: Integrate the grayscale values ​​of the pixels at each coordinate point to generate an image grayscale value data set.

[0065] The specific form of the above-mentioned image grayscale value data set may be an image grayscale value matrix or an image grayscale value table, which is not limited in the present application.

[0066] In one implementation of the present application, the image gray value dataset is an image gray matrix, which means that it is an M×N matrix. For example, its specific form can be:

[0067] The above image grayscale matrix is ​​a 100×100 matrix, which means that the target area is divided into 10,000 coordinate points, that is, 10,000 pixels are collected. Gray(x, y) is the grayscale value of the corresponding coordinate point, and the measurement range of the grayscale value is [0, 255].

[0068] In one embodiment of the present application, step S103 specifically includes the following steps:

[0069] S1031: Determine the element type based on the horizontal axis of the energy spectrum.

[0070] S1032: Obtain the characteristic peak value corresponding to each element according to the vertical axis of the energy spectrum.

[0071] First, a brief introduction is given to the gamma energy spectrum obtained by the neutron activation analysis device. The energy spectrum is a line graph, the horizontal axis of which is the gamma energy, and the vertical axis is the number of energy points under a specific gamma energy, wherein different gamma energies on the horizontal axis represent different types of elements, and different numbers of energy points on the vertical axis represent different element contents, and different elements have specific characteristic peaks. Different elements are distinguished by different characteristic peaks, and the peak value of the characteristic peak is the above-mentioned number of energy points. Based on this, step S1031 and step S1032 are performed.

[0072] S1033: Integrate the characteristic peak-to-peak value data corresponding to each element to generate a characteristic peak-to-peak value data set.

[0073] In some embodiments of the present application, the above-mentioned characteristic peak-to-peak value data set may be in array form or table form, that is, a characteristic peak-to-peak value array or a characteristic peak-to-peak value table.

[0074] For example, the Feature Peak Value dataset can be organized into the following table: GammaEnergy b1 b2 …… bn Counts c1 c2 …… cn

[0075] Alternatively, the feature peak value dataset can be assembled into the following array:

[0076] GammaEnergy=(b1, b2,...,bn)

[0077] Counts=(c1,c2,…,cn)

[0078] Taking the detection of explosive devices as an example, the energy range of GammaEnergy is 1e 3 eV~2e 7 eV, Counts is the value of the energy point (characteristic peak-to-peak value), and its measurement range is [0, 30].

[0079] In one embodiment of the present application, step S104 specifically includes the following steps:

[0080] S1041: unify the dimension and dimension of the image gray value dataset and the feature peak value dataset to obtain a normalized image gray value dataset and a normalized feature peak value dataset, respectively.

[0081] S1042: Fusing the normalized image gray value dataset and the normalized feature peak value dataset to obtain a fused dataset.

[0082] The unified dimension in step S1041 is to facilitate the data fusion in the subsequent step S1042. For example, the measurement range of the above image grayscale data set is [0, 255], and the measurement range of the above characteristic peak-to-peak value data set (element content) is [0, 30]. In an optional embodiment, the measurement ranges of the two can be adjusted to the same range, for example, unified to [0, 1]. Therefore, the values ​​in the above image grayscale data set and the characteristic peak-to-peak value data set need to be converted to the range of [0, 1] respectively.

[0083] The unified dimension in step S1041 means that the image grayscale data set is two-dimensional data, and the feature peak value data set is one-dimensional data, so the image grayscale value data set needs to be reduced in dimension, and the two-dimensional data is reduced to one-dimensional data, so as to facilitate data fusion in the later stage. For example, in one implementation of the present application, the principal component analysis method can be used to reduce the dimension of the image grayscale data set. The principal component analysis method is a well-known technology in the field of data processing, and the present application will not elaborate on it here.

[0084] It can be seen that the above-mentioned “normalized image gray value dataset” and “normalized feature peak value dataset” refer to the datasets obtained after unifying the measurement range and data dimension of the original image gray value dataset and feature peak value dataset respectively.

[0085] In one implementation of the present application, during the data fusion process in step S1042, the normalized image gray value dataset and the normalized feature peak value dataset need to be spatially aligned first, and then merged to obtain a fused dataset.

[0086] In the above data alignment process, manual alignment can be performed based on the spatial regions of the two data sets, or image registration can be performed, that is, image processing techniques (such as feature matching, mutual information, correlation, etc.) are used to register the images, key point detection (such as SIFT or ORB algorithm) is used to extract feature points, and alignment is performed by matching these feature points. Interpolation or conversion can be performed when necessary to achieve the purpose of alignment. In the fusion process, the aligned normalized image gray value data set and the normalized feature peak value data set can be directly merged to form an overall feature vector, that is, the above fused data set.

[0087] Of course, in some other implementation methods, data fusion can also be performed based on the deep neural network model training method. First, the first target detection model is trained based only on the structural feature detection data, and the second target detection model is trained based on the component feature detection data. Then, based on this training, a coupled detection model is trained, and the acquired image gray value dataset and the characteristic peak-to-peak value dataset are respectively input into the coupled detection model as feature values, and a fused dataset is output. The model prediction results are continuously verified and updated to improve the accuracy of the coupled detection model.

[0088] As mentioned above, although in the embodiments of the present application, pyrotechnics are used as examples of samples to be detected, this does not constitute a limitation to the present application. It can also be applied to other fields, such as the detection of samples such as fossils and cultural relics in the archaeological field, or the detection of meteorites or other particulate matter in the field of space exploration, etc. This coupled detection method has important guiding significance for both industrial applications and the above-mentioned fields of astronomy, archaeology, etc.

[0089] The present application also discloses a sample structure feature and composition feature coupling detection system, referring to Figure 3, is a schematic block diagram of a sample structure feature and composition feature coupling detection system according to an embodiment of the present application, which includes a neutron source, an imaging device, a neutron activation analysis device, an image data processing device, an energy spectrum data processing device and a data fusion device, wherein the neutron source is used to generate source neutrons; the imaging device is used to receive the light signal after the source neutrons pass through the sample to be detected and thus generate an image; the neutron activation analysis device is used to receive the characteristic radiation generated after the source neutrons bombard the sample to be detected and thus generate an energy spectrum; the image data processing device is used to obtain image data reflecting the structural characteristics of the sample to be detected based on the image; the energy spectrum data processing device is used to obtain energy spectrum data reflecting the composition characteristics of the sample to be detected based on the energy spectrum; the data fusion device is used to fuse the image data with the energy spectrum data to obtain a fused data set.

[0090] It should be noted that a slowing-down and collimating device is also provided between the neutron source and the imaging device and the neutron activation analysis device. The slowing-down and collimating device is used to slow down the fast neutrons emitted by the neutron source into thermal neutrons and collimate them into parallel beams to carry out neutron structural defect detection and elemental composition detection.

[0091] It should be noted that the above-mentioned devices can exist independently, or two or more devices can be combined into one device according to actual production design requirements. This application does not limit its specific form.

[0092] The above-mentioned sample structure characteristics and composition characteristics coupling detection system is used to perform Figure 1 or Figure 2 The embodiment of the sample structure feature and component feature coupling detection method shown in the figure has similar technical principles, technical problems solved and technical effects produced. Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working process and related instructions of the sample structure feature and component feature coupling detection system can refer to the contents described in the embodiment of the sample structure feature and component feature coupling detection method, and will not be repeated here.

[0093] So far, the technical solutions of the present application have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present application is obviously not limited to these specific embodiments. Without departing from the principles of the present application, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present application.

Claims

1. A method for coupling detection of sample structure characteristics and composition characteristics, characterized in that: include: Acquire the image and energy spectrum of the sample to be tested; Obtaining image data reflecting the structural features of the sample to be detected according to the image; Obtaining energy spectrum data reflecting the component characteristics of the sample to be detected according to the energy spectrum; The image data and the energy spectrum data are fused to obtain a fused data set.

2. The coupling detection method according to claim 1, characterized in that: The image data is an image gray value data set based on coordinate points, and the step of "obtaining image data reflecting the structural features of the sample to be detected according to the image" includes: determining a target area based on the image; Traversing the pixel points in the target area to obtain the grayscale value of the pixel at each coordinate point in the target area; The grayscale values ​​of the pixels at each coordinate point are integrated to generate the image grayscale value data set.

3. The coupling detection method according to claim 2, characterized in that: The image gray value data set is an image gray value matrix or an image gray value table.

4. The coupling detection method according to claim 2, characterized in that: The energy spectrum data is a characteristic peak-to-peak value data set based on gamma energy values, wherein different gamma energy values ​​represent different types of elements, and the step of "obtaining energy spectrum data reflecting the component characteristics of the sample to be detected according to the energy spectrum" includes: Determining the element type based on the horizontal axis of the energy spectrum; Obtaining the characteristic peak value corresponding to each element according to the vertical axis of the energy spectrum; The characteristic peak-to-peak value data corresponding to each element are integrated to generate the characteristic peak-to-peak value data set.

5. The coupling detection method according to claim 4, characterized in that: The characteristic peak-to-peak value data set is a characteristic peak-to-peak value array or a characteristic peak-to-peak value table.

6. The coupling detection method according to claim 4, characterized in that: The step of "fusing the image data with the energy spectrum data to obtain a fused data set" includes: Unifying the dimension and size of the image gray value dataset and the characteristic peak value dataset to obtain a normalized image gray value dataset and a normalized characteristic peak value dataset respectively; The normalized image gray value data set and the normalized feature peak value data set are fused to obtain a fused data set.

7. The coupling detection method according to claim 6, characterized in that: The step of "unifying the dimension and size of the image gray value dataset and the characteristic peak value dataset" comprises: Performing dimensionality reduction on the image gray value dataset; The measurement range of the image gray value data set and the measurement range of the characteristic peak-to-peak value data set are adjusted to the same range.

8. The coupling detection method according to claim 6, characterized in that: The “reducing the dimension of the image gray value data set” includes: The dimension of the image gray value data set is reduced by principal component analysis.

9. The coupling detection method according to claim 6, characterized in that: The step of "fusing the normalized image data set and the normalized energy spectrum data set to obtain a fused data set" includes: The normalized image gray value dataset and the normalized feature peak value dataset are spatially aligned and then merged to obtain a fused dataset.

10. A sample structure feature and composition feature coupling detection system, characterized in that: The coupling detection system comprises: A neutron source for generating source neutrons; An imaging device, which is used to receive the optical signal after the source neutrons pass through the sample to be detected and thus generate an image; A neutron activation analysis device, which is used to receive characteristic radiation generated after the source neutrons bombard the sample to be detected and thus generate an energy spectrum; An image data processing device, which is used to obtain image data reflecting the structural characteristics of the sample to be detected according to the image; An energy spectrum data processing device, which is used to obtain energy spectrum data reflecting the component characteristics of the sample to be detected according to the energy spectrum; A data fusion device is used to fuse the image data with the energy spectrum data to obtain a fused data set.

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