A coal mine underground target retrieval method and system

Abstract

The present invention proposes a method and system for underground coal mine target retrieval, belonging to the field of image retrieval technology. The method includes: constructing a target database of underground coal mine targets based on the targets to be retrieved; obtaining high-frequency and low-frequency images of the targets to be retrieved; obtaining a Markov graph of the low-frequency image of the targets to be retrieved and a Markov graph of the low-frequency target database based on a Markov transition field; using a feature extractor to extract features of the targets to be retrieved and the target database, and performing binarization processing; based on the binarized features, calculating the similarity between the target to be retrieved and each target in the target database to obtain a similarity metric, and then sorting the similarity metrics to obtain the final retrieval results. This method effectively reduces the adverse effects of complex external environments, thereby further increasing the accuracy of target descriptions and improving retrieval efficiency.

Description

Technical Field

[0001] The present invention belongs to the technical field of image retrieval, and in particular relates to a method and system for underground coal mine targets. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] As the coal mining industry accelerates its move toward intelligent operations, efficient monitoring and precise management of underground mine operating environments have become critical requirements. As mining depth and scale continue to expand, underground equipment and facilities are becoming increasingly complex, personnel activities are extensive and complex, and geological conditions are subject to numerous uncertainties. Effective retrieval of underground images allows for the rapid and precise location of specific targets, providing strong technical support for troubleshooting and maintenance supervision of critical equipment, real-time tracking and safety assurance of personnel, and early warning and dynamic monitoring of areas at risk of geological disasters.

[0004] Traditional target retrieval in underground coal mines primarily relies on techniques used in everyday scene object retrieval, namely, text-based image retrieval and content-based image retrieval. Text-based image retrieval often requires manual pre-labeling of images with descriptive text. These labels include information such as the name and model of underground coal mine equipment, location characteristics, personnel identification, action keywords, and geological structure-related terms. When searching, users enter relevant text keywords, and the system selects target images based on the degree of match between these keywords and the image labels. However, this approach has significant drawbacks. Due to the complex and ever-changing underground coal mine environment, manually annotated text labels may not be accurate or comprehensive, and are difficult to update in real time to reflect dynamic changes in underground conditions, which can easily lead to biased and missed search results. In recent years, content-based image retrieval has gained widespread favor among researchers. It focuses on extracting features directly from the visual content of an image for retrieval. For example, for images of underground coal mine equipment, visual elements such as shape, color, and texture are analyzed to construct feature vectors. For images of people, features such as the body's outline, posture, and clothing texture may be of interest. In geological structural images, the focus is on the texture and orientation of rock formations, the shape and distribution of cracks, and so on. However, the unique environmental conditions in coal mines, such as dim and unstable lighting, pervasive dust, and obstruction by various equipment and cables, can seriously interfere with the accurate extraction and feature construction of image visual content. This makes content-based image retrieval in coal mines face numerous challenges, significantly compromising its accuracy and reliability, making it difficult to efficiently meet the stringent requirements of target retrieval tasks such as underground coal mine safety monitoring, equipment management, and personnel location. Summary of the Invention

[0005] In order to overcome the shortcomings of the above-mentioned existing technologies, the present invention provides a method and system for underground coal mine target retrieval. On the one hand, before extracting the features of underground coal mine images, in order to increase the accuracy of image description, a method for analyzing underground coal mine target images based on Fourier transform and Markov algorithm is proposed; on the other hand, in the feature construction stage, in order to improve the retrieval efficiency, a feature binarization method based on Hill diversity is proposed.

[0006] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions:

[0007] A first aspect of the present invention provides a method for searching for targets in underground coal mines;

[0008] A method for retrieving a target in an underground coal mine, comprising:

[0009] Building a target database of targets to be retrieved in coal mines based on the targets to be retrieved, wherein the target database includes a high-frequency target database and a low-frequency target database;

[0010] Obtaining a high-frequency image and a low-frequency image of a target to be retrieved, inputting the low-frequency image of the target to be retrieved and the low-frequency target database into a Markov transformation field, respectively, to obtain a Markov graph of the low-frequency image of the target to be retrieved and a Markov graph of the low-frequency target database;

[0011] Extracting features of the target to be retrieved and features of the target database respectively, and performing binarization processing on the extracted features; wherein the features of the target to be retrieved are composed of high-frequency image features of the target to be retrieved and low-frequency image Markov graph features of the target to be retrieved; and the features of the target database are composed of high-frequency target database features and low-frequency target database Markov graph features;

[0012] Based on the binarized features, the similarity between the target to be retrieved and each target in the target database is calculated to obtain a similarity measure, and the final retrieval result is obtained by sorting the similarity measures.

[0013] As a further technical solution, the process of obtaining a high-frequency target database and a low-frequency target database is as follows: by traversing the target database, obtaining a high-frequency image and a low-frequency image of each target data in the target database corresponding to the target to be retrieved, and constructing a high-frequency target database and a low-frequency target database respectively.

[0014] As a further technical solution, a high-frequency image and a low-frequency image of the target to be retrieved are obtained by performing Fourier transform on the target to be retrieved; specifically:

[0015] Perform Fourier transform on the target to be retrieved to obtain a spectrum image of the target to be retrieved; the spectrum image is as follows:

[0016]

[0017] Where, Represents the grayscale value of the pixel at the coordinate (m,n) in the target to be retrieved, Indicates the corresponding spectrum; M is the width of the target to be retrieved; N is the height of the target to be retrieved; u is the frequency domain horizontal coordinate; v is the frequency domain vertical coordinate;

[0018] Center the spectrum image and move the zero-frequency part to the center of the spectrum;

[0019] A Gaussian high-pass filter is used to perform high-pass filtering to obtain a high-pass filtering spectrum diagram; a Gaussian low-pass filter is used to perform low-pass filtering to obtain a low-pass filtering spectrum diagram, and the zero-frequency domain part is moved back to the original position;

[0020] Perform inverse Fourier transform on the high-pass filter spectrum graph and the low-pass filter spectrum graph respectively to generate a high-frequency image and a low-frequency image of the target to be retrieved.

[0021] As a further technical solution, the low-frequency image of the target to be retrieved is input into the Markov transformation field, and the process of obtaining the Markov map of the low-frequency image of the target to be retrieved is:

[0022] Filling the low-frequency image of the target to be retrieved, obtaining a filled image and calculating the size of the filled image;

[0023] Segmenting the filled image to obtain image blocks; performing statistical processing on each image block to obtain a final description vector of the image block;

[0024] The final description vector is converted into a Markov field by using a Markov transformation field, and the Markov field is scaled to the same size as the low-frequency image of the target to be retrieved, thereby obtaining a Markov map of the low-frequency image of the target to be retrieved.

[0025] As a further technical solution, the features of the target to be retrieved are composed of the high-frequency image features of the target to be retrieved and the low-frequency image Markov graph features of the target to be retrieved. The features of the target to be retrieved are as follows:

[0026]

[0027] Where, is the feature of the target to be retrieved; is a high-frequency image feature; is the low-frequency image Markov graph feature of the target to be retrieved;

[0028] The characteristics of the target database are composed of the high-frequency target database characteristics and the Markov graph characteristics of the low-frequency target database. The characteristics of the target database are as follows:

[0029]

[0030] Where, is the characteristic of the target database; Represents the Nth feature of the target database; is the Nth feature of the high-frequency target database; is the Nth feature of the Markov graph of the low-frequency target database.

[0031] As a further technical solution, the process of binarizing the extracted features is as follows:

[0032] Dividing the features of the target to be retrieved and the features of the target database, and finding the neighborhood of each feature component;

[0033] Hill diversity is calculated based on neighborhood values. The formula for Hill diversity is:

[0034]

[0035] Where, is the characteristic component; N( )for Neighborhood; is the characteristic component Hill diversity value; for Neighborhood N( ) elements; based on Hill diversity, the features of the target to be retrieved and the features of the target database are binarized to obtain the binarized features.

[0036] As a further technical solution, the similarity measure is:

[0037]

[0038] Where D is the similarity measure; is the feature of the target to be retrieved after binarization processing; is the Nth feature of the target database after binarization.

[0039] A second aspect of the present invention provides a coal mine underground target retrieval system.

[0040] A coal mine underground target retrieval system, comprising:

[0041] The target database construction module is configured to: construct a target database of targets to be retrieved in a coal mine based on the targets to be retrieved, wherein the target database includes a high-frequency target database and a low-frequency target database;

[0042] The image analysis module is configured to: obtain a high-frequency image and a low-frequency image of the target to be retrieved, input the low-frequency image of the target to be retrieved and the low-frequency target database into a Markov transformation field, and obtain a Markov graph of the low-frequency image of the target to be retrieved and a Markov graph of the low-frequency target database;

[0043] The feature extraction and binarization processing module is configured to: extract features of the target to be retrieved and features of the target database respectively, and perform binarization processing on the extracted features; wherein the features of the target to be retrieved are composed of high-frequency image features of the target to be retrieved and low-frequency image Markov graph features of the target to be retrieved; and the features of the target database are composed of high-frequency target database features and low-frequency target database Markov graph features;

[0044] The retrieval module is configured to: calculate the similarity between the target to be retrieved and each target in the target database based on the binarized features, obtain similarity metrics, and obtain the final retrieval results by sorting the similarity metrics.

[0045] A third aspect of the present invention provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps of the coal mine underground target retrieval method as described in the first aspect of the present invention.

[0046] The fourth aspect of the present invention provides an electronic device, comprising a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, the steps of a method for underground target retrieval in a coal mine as described in the first aspect of the present invention are implemented.

[0047] One or more of the above technical solutions have the following beneficial effects:

[0048] (1) The present invention uses Fourier transform and Markov algorithm to retrieve target images in coal mines. By combining different domain descriptions, the adverse effects of the external complex environment on the accurate extraction of image visual content and feature construction are effectively reduced, thereby further increasing the accuracy of target description.

[0049] (2) The present invention uses Hill diversity to perform binary processing on features, which effectively solves the problem of heavy computational tasks and low retrieval efficiency when calculating similarity metrics based on extracted features, thereby improving retrieval efficiency while ensuring accuracy.

[0050] Advantages of additional aspects of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0052] Figure 1 This is a flow chart of the method of the first embodiment.

[0053] Figure 2 This is a flowchart of obtaining a high-frequency image of a target to be retrieved in the first embodiment.

[0054] Figure 3 This is a flowchart of obtaining a low-frequency image Markov graph of a target to be retrieved in the first embodiment.

[0055] Figure 4 Schematic diagram of four-level segmentation of the filled image in the first embodiment.

[0056] Figure 5 This is the feature binarization flow chart in the first embodiment.

[0057] Figure 6 This is a system structure diagram of the second embodiment. DETAILED DESCRIPTION

[0058] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0059] It should be noted that the terms used herein are for describing particular embodiments only and are not intended to limit the exemplary embodiments according to the present invention.

[0060] In the absence of conflict, the embodiments of the present invention and the features thereof may be combined with each other.

[0061] This paper proposes a method and system for underground coal mine target retrieval. This method uses Fourier transforms and a Markov algorithm to analyze underground coal mine target images. By combining different domain descriptions, the accuracy of target descriptions is increased. Furthermore, given the low retrieval efficiency of existing methods that directly calculate similarity metrics based on extracted features, this paper proposes a feature binarization method based on Hill diversity.

[0062] Example 1

[0063] This embodiment discloses a method for searching for targets in underground coal mines;

[0064] like Figure 1 As shown, a method for retrieving a target in an underground coal mine includes:

[0065] Step S1: constructing a target database of targets to be retrieved in a coal mine based on the targets to be retrieved, wherein the target database includes a high-frequency target database and a low-frequency target database.

[0066] In step S1, the target database of the target to be retrieved in the coal mine is constructed based on the target to be retrieved. , where , It is the target data in the database Q with a capacity of N. Based on the target to be retrieved, traverse the target database Q and obtain the high-frequency image of each target data in the target database corresponding to the target to be retrieved and low-frequency images , forming a high-frequency target database and low-frequency target database .

[0067] Step S2: Obtain high-frequency images of the target to be retrieved and low-frequency images ; The low-frequency image of the target to be retrieved and low-frequency target database Input them into the Markov transformation field respectively to obtain the low-frequency image Markov map of the target to be retrieved and Markov graph of low-frequency target database .

[0068] In step S2, the target to be retrieved is recorded as I. In this embodiment, the target to be retrieved I is obtained under discrete time signals. By performing Fourier transform on the target to be retrieved, a high-frequency image of the target to be retrieved is obtained. and low-frequency images .like Figure 2 As shown in the figure, the Fourier transform is a powerful tool widely used in mathematics and engineering. Its basic idea is to convert a function in the time domain (or spatial domain) (for images, this can be regarded as the spatial distribution of pixel values) into the frequency domain for analysis. Specifically, for a complex periodic signal (in an image, this can be analogous to a periodically changing part such as the image texture), the Fourier transform can decompose it into a combination of sine and cosine functions of different frequencies. In image analysis, the Fourier transform can be used to obtain the image spectrum. The low-frequency portion of the spectrum usually corresponds to relatively smooth areas in the image, such as a large background area, while the high-frequency portion is related to details and edges in the image.

[0069] Step S21: Perform Fourier transform on the target to be retrieved to obtain a spectrum image of the target to be retrieved. The obtained spectrum image is as follows:

[0070]

[0071] Where, Represents the grayscale value of the pixel at the coordinate (m,n) in the target to be retrieved, Represents the corresponding frequency spectrum; M is the width of the target to be retrieved; N is the height of the target to be retrieved; u is the horizontal coordinate in the frequency domain; v is the vertical coordinate in the frequency domain.

[0072] Step S22 centers the spectrum image, moving the zero-frequency portion to the center of the spectrum so that the zero-frequency component of the spectrum is located at the center, making it easier to observe and analyze. This further enhances the symmetry of the spectrum, facilitates frequency domain filtering operations, and facilitates intuitive understanding of frequency distribution.

[0073] In step S23, a Gaussian high-pass filter is used for high-pass filtering, and a Gaussian low-pass filter is used for low-pass filtering to obtain a high-pass filtered spectrum diagram and a low-pass filtered spectrum diagram, and the zero-frequency domain portion is moved back to the original position to provide the correct input for subsequent frequency domain operations so as to perform an inverse Fourier transform. The process of using a Gaussian high-pass filter for high-pass filtering to obtain a high-pass filtered spectrum diagram is as follows:

[0074]

[0075] Where, is the high-pass filter spectrum diagram; u is the frequency domain horizontal coordinate; v is the frequency domain vertical coordinate; is a constant, is a point in the frequency domain The distance from the center of the frequency domain rectangle.

[0076] A Gaussian low-pass filter is used for low-pass filtering. The process of obtaining the low-pass filtering spectrum is as follows:

[0077]

[0078] Where, is the low-pass filter spectrum diagram; u is the frequency domain horizontal coordinate; v is the frequency domain vertical coordinate; is a constant, is a point in the frequency domain The distance from the center of the frequency domain rectangle.

[0079] Step S24, respectively perform Fourier inverse transform on the high-pass filter spectrum graph and the low-pass filter spectrum graph to obtain a high-frequency image and a low-frequency image of the target to be retrieved. The Fourier inverse transform is:

[0080]

[0081] Where, Represents the grayscale value of the pixel at the coordinate (m,n) in the target to be retrieved, Represents the corresponding frequency spectrum; M is the width of the target to be retrieved; N is the height of the target to be retrieved; u is the horizontal coordinate in the frequency domain; v is the vertical coordinate in the frequency domain.

[0082] Furthermore, in step S2, for relatively smooth low-frequency images, it is considered that more pixels belong to background information and contribute less to the image description. In order to extract the most concise and comprehensive description, in this embodiment, the Markov transition field is obtained to extract its features. The Markov transition field (MTF) is a tool for processing time series data. It is developed based on the concept of the Markov chain. A Markov chain is a random process in which the future state depends only on the current state and is independent of the past state (given the current state). The Markov transition field expands the state transition information of the Markov chain into a two-dimensional field representation.

[0083] Combine Figure 3 , the low-frequency image of the target to be retrieved and low-frequency target database Input them into the Markov transformation field respectively to obtain the low-frequency image Markov map of the target to be retrieved and Markov graph of low-frequency target database The process includes:

[0084] Step S25: low-frequency image of the target to be retrieved Fill, get the filled image and calculate the size of the filled image; fill the low frequency image The size of , where W_L_Img, H_L_Img, and C_L_Img are low-frequency images respectively. The width, height and number of channels are calculated. In this embodiment, the filling amount in the w direction and h direction is calculated respectively. 、 , and its filling amount is calculated as follows:

[0085] ;

[0086] .

[0087] Add pixel values ​​to the filled area, get the filled image, and calculate the size of the filled image. As shown in the following formula:

[0088] ;

[0089] Where W_PaddingImg, H_PaddingImg, and C_PaddingImg are the width, height, and number of channels of the padding image, respectively. C_Img is the abbreviation of C_L_Img, which indicates the number of channels of the low-frequency image.

[0090] Step S26, segmenting the filled image to obtain image blocks; performing statistical processing on each image block to obtain a final description vector of the image block;

[0091] Combine Figure 4 , the padding image PaddingImg is segmented into 4 levels, and the segmentation blocks are recorded as P_1, P_2, P_3, P_4, P_5, P_6, P_7, P_8, P_9, P_10, P_12, P_13, P_14, P_15, P_16.

[0092] For each image block , calculate its statistical data, such as the mean ,variance , standard deviation , majority , median , skewness , kurtosis , extreme , forming a description vector , describing the vector As shown in the following formula:

[0093]

[0094] The description vectors of the image blocks are combined to form the final description vector S of the image, as shown in the following formula:

[0095]

[0096] Where, Describes the vector S The characteristic component value of the dimension.

[0097] Step S27: Use the Markov transformation field to convert the final description vector into a Markov field, scale the Markov field to the same size as the low-frequency image, and obtain the Markov map of the low-frequency image of the target to be retrieved. .

[0098] First, the number of quantile bins is determined. In this embodiment, the number of quantile bins is set to 32, which is the quantile bin of the data containing timestamp i. and contains the data quantile bins for time axis j The defined Markov field is shown as follows:

[0099]

[0100] Where M is the defined Markov field, express Finally, the Markov field is scaled to a low-frequency image Equal size, obtain the low-frequency image Markov map of the target to be retrieved . Correspondingly, for low-frequency target database By performing the above operation on each data in , we can obtain the Markov graph MLQ of the low-frequency target database. .

[0101] Step S3, respectively extract the features of the target to be retrieved and the features of the target database, and perform binarization processing on the extracted features; wherein, the features of the target to be retrieved are obtained by high-frequency image The features of and the low-frequency image Markov map of the target to be retrieved Feature composition; the target database features are composed of high-frequency target database and Markov graph of low-frequency target database Feature composition;

[0102] In step S3, the final image description is obtained by taking into account the features of the concatenated high-frequency image and the Markov image. Compared with the image description based on the original image, the number of features has doubled. As the number of features increases, the retrieval efficiency decreases. To optimize the retrieval efficiency of the system, in this embodiment, Hill diversity is used to binarize the features, improving retrieval efficiency while ensuring accuracy.

[0103] Hill integrated species richness and relative abundance into a type of diversity measurement index, and called these measurement indexes Hill numbers or effective numbers of species. The calculation of Hill number is shown as follows:

[0104]

[0105] in, is the Hill number, i represents the i-th species, and S is the total number of species. represents the relative abundance of species i. When q=0, Refers to species richness, which does not take into account the relative abundance of species. When q approaches 1, the limit of this formula is the Shannon entropy exponent, representing Shannon diversity. When q = 1, individuals are counted equally, and the number of species calculated is proportional to their abundance. When q = 2, it represents the Simpson index, which underestimates the role of species other than dominant species and is interpreted as the effective number of dominant species in the community.

[0106] Combine Figure 5 , m represents the feature dimension, Represents the features to be binarized, for each feature component , find its 3 nearest neighbors , as shown below:

[0107]

[0108] Where, For the neighborhood; 、 、 They are the minimum index value, the second smallest index value, and the maximum index value of the neighborhood respectively;

[0109] Hill diversity is calculated based on neighborhood values. The Hill diversity formula is:

[0110]

[0111] Where, is the characteristic component Hill diversity value; for 3 neighborhood N( ) in the element; is the characteristic component; N( )for 3 neighborhoods.

[0112] Based on Hill diversity, the characteristics Perform binarization to obtain the binarized features , the parameters in the binarized features As shown in the following formula:

[0113]

[0114] Where, is the characteristic component Hill diversity value; is the sum of the Hill diversity of all feature components.

[0115] Specifically, the features of the target I to be retrieved are determined by the high-frequency image of the target I to be retrieved. Low-frequency image Markov graph of features and targets to be retrieved Feature composition, extracting high-frequency images of the target I to be retrieved The characteristics are shown below:

[0116]

[0117] Where, is the feature of the high-frequency image of the target to be retrieved.

[0118] Extract the low-frequency image Markov map of the target to be retrieved The characteristics are shown below:

[0119]

[0120] Where, It is the feature of the low-frequency image Markov graph of the target to be retrieved.

[0121] The characteristics of the target database Q are composed of the high-frequency target database and Markov graph of low-frequency target database Feature composition, extracting high-frequency target database The characteristics are as follows:

[0122]

[0123] Where, is the feature of the extracted high-frequency target database; Representing an image The characteristics are as follows:

[0124]

[0125] Extracting Markov graphs from low-frequency target databases The characteristics are shown below:

[0126]

[0127] Where, The features of the Markov graph of the extracted low-frequency target database; Representing an image The characteristics are shown in the following formula:

[0128]

[0129] In the feature engineering stage, we first fuse the high-frequency images of the target I to be retrieved. Low-frequency image Markov graph of features and targets to be retrieved The features constitute the features of the target I to be retrieved, as shown in the following formula:

[0130]

[0131] Where, is the feature of the target I to be retrieved; The features of the high-frequency image of the target to be retrieved are extracted; It is the feature of the low-frequency image Markov graph of the target to be retrieved.

[0132] Binarize the features of the target I to be retrieved to obtain the binary features of the target I to be retrieved , and its binarization process is shown as follows:

[0133]

[0134] Fusion of high-frequency target database and Markov graph of low-frequency target database The features of constitute the features of the target database Q, and the features of the target database Q are shown in the following formula:

[0135]

[0136] Where FQ is the feature of the target database Q; Represents the Nth feature of the target database; is the Nth feature of the high-frequency target database; is the Nth feature of the Markov graph of the low-frequency target database.

[0137] The features of the target database Q are binarized to obtain the binary features BFQ of the target database Q. The binarization process is shown in the following formula:

[0138]

[0139] Where, is the binary feature of the target database Q; is the Nth feature of the target database after binarization.

[0140] Step S4, based on the binarized features, respectively calculate the similarity between the target to be retrieved and each target in the target database to obtain similarity metrics, and obtain the final retrieval result by sorting the similarity metrics.

[0141] Based on the binary features of the target I to be retrieved and the binary feature BFQ of the target database Q, respectively calculate the similarity between the target to be retrieved I and each target in the target database Q, and obtain the similarity measure , as follows:

[0142]

[0143] Where D is the similarity measure; is the feature of the target to be retrieved after binarization processing; is the Nth feature of the target database after binarization.

[0144] Similarity measure Sort and get the final search results.

[0145] Example 2

[0146] This embodiment discloses a coal mine underground target retrieval system;

[0147] like Figure 2 As shown, a coal mine underground target retrieval system includes:

[0148] The target database construction module is configured to: construct a target database based on the target image of the coal mine, wherein the target database includes a high-frequency target database and a low-frequency target database;

[0149] The image analysis module is configured to: obtain a target to be retrieved and perform Fourier transform to obtain a high-frequency image and a low-frequency image of the target to be retrieved; obtain a Markov graph of the low-frequency image of the target to be retrieved and a Markov graph of a low-frequency target database based on a Markov transition field;

[0150] The feature extraction and binarization processing module is configured to: use a feature extractor to extract features of the target to be retrieved and features of the target database respectively, and use Hill diversity to binarize the extracted features; wherein the features of the target to be retrieved are composed of Markov graph features of the high-frequency image and the low-frequency image of the target to be retrieved; and the features of the target database are composed of Markov graph features of the high-frequency target database and the low-frequency target database;

[0151] The retrieval module is configured to: calculate the similarity between the target to be retrieved and each target in the target database based on the binarized features, obtain similarity metrics, and obtain the final retrieval results by sorting the similarity metrics.

[0152] Example 3

[0153] The purpose of this embodiment is to provide a computer-readable storage medium.

[0154] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a coal mine underground target retrieval method as described in Example 1.

[0155] Example 4

[0156] The purpose of this embodiment is to provide an electronic device.

[0157] An electronic device includes a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, the steps of a method for underground target retrieval in a coal mine as described in Example 1 are implemented.

[0158] The steps involved in the apparatuses of Examples 2, 3, and 4 above correspond to those of Method Example 1. For detailed implementations, please refer to the relevant description of Example 1. The term "computer-readable storage medium" should be understood to mean a single medium or multiple media containing one or more instruction sets; it should also be understood to include any medium capable of storing, encoding, or carrying an instruction set for execution by a processor and causing the processor to perform any method of the present invention.

[0159] Those skilled in the art will appreciate that the modules or steps of the present invention described above can be implemented using a general-purpose computer device. Alternatively, they can be implemented using program code executable by a computing device, which can then be stored in a storage device and executed by the computing device. Alternatively, they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module for implementation. The present invention is not limited to any specific combination of hardware and software.

[0160] Although the above describes the specific embodiments of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without any creative work are still within the scope of protection of the present invention.

Claims

1. A method for searching targets in underground coal mines, characterized in that: include: Building a target database of targets to be retrieved in coal mines based on the targets to be retrieved, wherein the target database includes a high-frequency target database and a low-frequency target database; Obtain a high-frequency image and a low-frequency image of the target to be retrieved, input the low-frequency image of the target to be retrieved and the low-frequency target database into a Markov transformation field, and obtain a Markov graph of the low-frequency image of the target to be retrieved and a Markov graph of the low-frequency target database; wherein the process of obtaining the Markov graph of the low-frequency image of the target to be retrieved is: Filling the low-frequency image of the target to be retrieved, obtaining a filled image and calculating the size of the filled image; Segmenting the filled image to obtain image blocks; performing statistical processing on each image block to obtain a final description vector of the image block; The final description vector is converted into a Markov field by using a Markov transformation field, and the Markov field is scaled to the same size as the low-frequency image of the target to be retrieved, thereby obtaining a Markov map of the low-frequency image of the target to be retrieved; The features of the target to be retrieved and the features of the target database are extracted respectively, and the extracted features are binarized; wherein the features of the target to be retrieved are composed of the high-frequency image features of the target to be retrieved and the low-frequency image Markov graph features of the target to be retrieved; the features of the target database are composed of the high-frequency target database features and the Markov graph features of the low-frequency target database; wherein the features of the target to be retrieved are as follows: Where, is the feature of the target to be retrieved; is a high-frequency image feature; is the low-frequency image Markov graph feature of the target to be retrieved; The characteristics of the target database are as follows: Where, is the characteristic of the target database; Represents the Nth feature of the target database; is the Nth feature of the high-frequency target database; is the Nth feature of the Markov graph of the low-frequency target database; Based on the binarized features, the similarity between the target to be retrieved and each target in the target database is calculated to obtain a similarity measure. The similarity measures are sorted to obtain the final retrieval results. The process of binarizing the extracted features is as follows: Dividing the features of the target to be retrieved and the features of the target database, and finding the neighborhood of each feature component; The Hill diversity is calculated based on the neighborhood value. The formula for the Hill diversity is: Where, is the characteristic component; N( )for Neighborhood; is the characteristic component Hill diversity value; for Neighborhood N( ) in the element; Based on Hill diversity, the features of the target to be retrieved and the features of the target database are binarized to obtain the binarized features.

2. A method for searching underground coal mine targets according to claim 1, characterized in that: The process of obtaining the high-frequency target database and the low-frequency target database is as follows: by traversing the target database, obtaining the high-frequency image and the low-frequency image of each target data in the target database corresponding to the target to be retrieved, and constructing the high-frequency target database and the low-frequency target database respectively.

3. A method for searching underground coal mine targets according to claim 1, characterized in that: By performing Fourier transform on the target to be retrieved, a high-frequency image and a low-frequency image of the target to be retrieved are obtained; specifically: Perform Fourier transform on the target to be retrieved to obtain a spectrum image of the target to be retrieved; the spectrum image is as follows: Where, Represents the grayscale value of the pixel at the coordinate (m,n) in the target to be retrieved, Indicates the corresponding spectrum; M is the width of the target to be retrieved; N is the height of the target to be retrieved; u is the frequency domain horizontal coordinate; v is the frequency domain vertical coordinate; Center the spectrum image and move the zero-frequency part to the center of the spectrum; A Gaussian high-pass filter is used to perform high-pass filtering to obtain a high-pass filtering spectrum diagram; a Gaussian low-pass filter is used to perform low-pass filtering to obtain a low-pass filtering spectrum diagram, and the zero-frequency domain part is moved back to the original position; Perform inverse Fourier transform on the high-pass filter spectrum graph and the low-pass filter spectrum graph respectively to generate a high-frequency image and a low-frequency image of the target to be retrieved.

4. A method for searching for targets in underground coal mines according to claim 1, characterized in that: The similarity measure is: Where D is the similarity measure; is the feature of the target to be retrieved after binarization processing; is the Nth feature of the target database after binarization.

5. A coal mine underground target retrieval system, characterized in that: include: The target database construction module is configured to: construct a target database of targets to be retrieved in a coal mine based on the targets to be retrieved, wherein the target database includes a high-frequency target database and a low-frequency target database; The image analysis module is configured to: obtain a high-frequency image and a low-frequency image of the target to be retrieved, input the low-frequency image of the target to be retrieved and the low-frequency target database into a Markov transformation field, and obtain a Markov graph of the low-frequency image of the target to be retrieved and a Markov graph of the low-frequency target database; wherein the process of obtaining the Markov graph of the low-frequency image of the target to be retrieved is: Filling the low-frequency image of the target to be retrieved, obtaining a filled image and calculating the size of the filled image; Segmenting the filled image to obtain image blocks; performing statistical processing on each image block to obtain a final description vector of the image block; The final description vector is converted into a Markov field by using a Markov transformation field, and the Markov field is scaled to the same size as the low-frequency image of the target to be retrieved, thereby obtaining a Markov map of the low-frequency image of the target to be retrieved; The feature extraction and binarization processing module is configured to: extract features of the target to be retrieved and features of the target database respectively, and perform binarization processing on the extracted features; wherein the features of the target to be retrieved are composed of high-frequency image features of the target to be retrieved and low-frequency image Markov graph features of the target to be retrieved; the features of the target database are composed of high-frequency target database features and low-frequency target database Markov graph features; wherein the features of the target to be retrieved are as follows: Where, is the feature of the target to be retrieved; is a high-frequency image feature; is the low-frequency image Markov graph feature of the target to be retrieved; The characteristics of the target database are as follows: Where, is the characteristic of the target database; Represents the Nth feature of the target database; is the Nth feature of the high-frequency target database; is the Nth feature of the Markov graph of the low-frequency target database; The retrieval module is configured to: calculate the similarity between the target to be retrieved and each target in the target database based on the binarized features, obtain similarity metrics, and obtain the final retrieval results by sorting the similarity metrics; wherein the process of binarizing the extracted features is as follows: Dividing the features of the target to be retrieved and the features of the target database, and finding the neighborhood of each feature component; The Hill diversity is calculated based on the neighborhood value. The formula for the Hill diversity is: Where, is the characteristic component; N( )for Neighborhood; is the characteristic component Hill diversity value; for Neighborhood N( ) in the element; Based on Hill diversity, the features of the target to be retrieved and the features of the target database are binarized to obtain the binarized features.

6. A computer-readable storage medium having a program stored thereon, characterized in that: When the program is executed by a processor, the steps of the coal mine underground target retrieval method as described in any one of claims 1 to 4 are implemented.

7. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the coal mine underground target retrieval method according to any one of claims 1 to 4 are implemented.

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