Method, device and equipment for identifying blasting lumpiness of open-air deep-hole step and medium

Abstract

The invention discloses an open-air deep-hole step blasting lumpiness identification method, device and equipment and a medium, and belongs to the technical field of image data processing. The method comprises the following steps: acquiring a to-be-identified post-blasting field image; on the basis of an image recognition model, the field image after blasting is recognized, and a recognition result is output and comprises the contour and the size of each broken stone; wherein the image recognition model is obtained by training a YOLO deep learning model. According to the open-air deep-hole bench blasting lumpiness identification method, automatic identification and statistics of broken stone lumpiness after blasting can be realized by using a deep learning technology and an image processing algorithm, the identification precision and efficiency are improved, the production efficiency and the working quality are improved, and the method is suitable for popularization and application. The method is of great significance in improving the intelligent level of the open-air deep-hole bench blasting technology and promoting sustainable development of the mining industry and engineering construction field.

Description

Technical Field

[0001] The present application belongs to the field of image data processing technology, and in particular relates to a method, device, equipment and medium for identifying block size of open-pit deep hole step blasting. Background Art

[0002] In the field of mining and various engineering construction, open-pit deep hole step blasting technology has become one of the indispensable key technologies due to its advantages of efficient mining, convenient operation of large-scale machinery and equipment, and mechanized and automated production. This technology not only improves the mining intensity and production scale, but also promotes the introduction and application of new technologies and methods, and significantly improves economic benefits. However, the distribution of crushed stone block size produced after blasting has a direct and important impact on the subsequent crushing, screening and processing processes, and is directly related to the overall production efficiency and product quality.

[0003] Traditionally, the identification and statistics of the size of broken rocks after blasting mainly rely on manual measurement, which is not only inefficient and time-consuming, but also easily interfered by human factors such as subjective judgment, fatigue and environmental conditions of the surveyors, making it difficult to ensure the accuracy and consistency of the measurement results. With the accelerated transformation of mining and engineering construction towards digitalization and intelligence, the limitations of traditional manual measurement methods have become increasingly prominent, and they cannot meet the needs of modern industrial production for high precision and high efficiency.

[0004] In view of this, the development of an efficient and accurate automated image recognition technology to achieve rapid, non-contact measurement and statistical analysis of the size of broken stones after blasting has become a technical problem that needs to be solved urgently. Summary of the invention

[0005] The purpose of the present invention is to provide a method, device, equipment and medium for identifying the block size of open-pit deep hole step blasting. By utilizing deep learning technology and image processing algorithm, automatic recognition and statistics of the block size of broken stones after blasting can be realized, thereby improving the accuracy and efficiency of recognition.

[0006] A first aspect of an embodiment of the present application provides a method for identifying block size of open-pit deep hole step blasting, comprising:

[0007] Acquire the post-blasting scene image to be identified;

[0008] Based on the image recognition model, the post-blasting scene image is recognized and the recognition results are output, including the outline and size of each piece of gravel;

[0009] Among them, the image recognition model is trained using the YOLO deep learning model.

[0010] Optionally, the training method of the image recognition model includes:

[0011] Collect multiple high-resolution images with different colors and content;

[0012] Use Roboflow graphics annotation platform to annotate high-resolution images;

[0013] Randomly cut a number of small image samples from the labeled high-resolution image to obtain high-resolution image samples;

[0014] Use the image scaling algorithm to downsample the high-resolution image samples by 4 times to generate the corresponding low-resolution image samples;

[0015] Input the high-resolution image samples and the corresponding low-resolution image samples as training sets into the YOLO deep learning model;

[0016] Extract shallow features of low-resolution image samples based on convolutional neural networks;

[0017] The spatial attention mechanism and residual dense connection block are used to fuse shallow features, extract feature information of different frequencies, and generate multi-scale feature maps;

[0018] The fused multi-scale feature map is upsampled to restore it to the size of the high-resolution image. Skip connections and sub-pixel convolutions are used in the upsampling process.

[0019] Based on the preset loss function, the reconstructed high-resolution image and the original high-resolution image are back-propagated to optimize the model parameters.

[0020] Optionally, the parameters of the training process are set as follows:

[0021] Model type: YOLOv8; Number of training rounds: 100 rounds; Image size: 640x640 pixels; Batch size: 16; Initial learning rate: 0.01; Learning rate decay: 0.0001; Momentum parameter: 0.937; Weight decay: 0.0005; Number of warm-up rounds: 3; Warm-up momentum: 0.8; Warm-up learning rate: 0.1; Data augmentation: On.

[0022] Optionally, a method for identifying the block size of open-pit deep hole step blasting also includes:

[0023] The original image and the recognition result are compared and displayed, and the recognized gravel parts are marked with different colors;

[0024] Conduct statistical analysis on the identified gravel sizes and generate statistical reports.

[0025] A second aspect of an embodiment of the present application provides a device for identifying block size of open-pit deep hole step blasting, including: a module for executing the above method.

[0026] A third aspect of an embodiment of the present application provides an electronic device, which includes a memory and a processor. The memory stores a computer program that can be run on the processor. When the processor executes the computer program, the steps of the above method are implemented.

[0027] A fourth aspect of an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of the above method are implemented.

[0028] A fifth aspect of an embodiment of the present application provides a computer program product, including a computer program, which implements the steps of the above method when the computer program is executed by a processor.

[0029] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0030] The embodiments of the present application provide a method, device, equipment and medium for identifying the block size of open-pit deep-hole step blasting. For the blast pile produced by open-pit deep-hole step blasting, the invention can identify stones of various sizes with high recognition accuracy, accurately mark the contour and size of the stones in the blast pile, realize automatic recognition and statistics of the block size of the crushed stones after blasting, and improve the recognition accuracy and efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0032] Figure 1 It is a flow chart of a method for identifying block size of open-pit deep hole step blasting provided in one embodiment of the present application;

[0033] Figure 2 It is a post-blasting scene image provided by an embodiment of the present application;

[0034] Figure 3 is a schematic diagram showing a recognition result provided by an embodiment of the present application;

[0035] Figure 4 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0036] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0037] The method for identifying the size of open-pit deep hole step blasting provided in the embodiment of the present application can be applied to electronic devices such as computers, servers, and mobile phones. In this case, the electronic device is the executor of the method for identifying the size of open-pit deep hole step blasting provided in the embodiment of the present application. The embodiment of the present application does not impose any restrictions on the specific type of electronic device.

[0038] In order to illustrate the technical solution described in this application, a specific embodiment is provided below for illustration.

[0039] A method for identifying the size of open-pit deep-hole step blasting fragments provided in the embodiment of the present application aims to intelligently identify and measure the size of the gravel in the image through advanced image processing technology and algorithms, thereby providing objective and accurate gravel size distribution data, providing a scientific basis for optimizing blasting design and adjusting subsequent processing technology, and further improving overall production efficiency and economic benefits.

[0040] See also Figure 1 As shown, the method for identifying the block size of open-pit deep hole step blasting includes the following steps:

[0041] S110: Data acquisition.

[0042] The data acquisition step specifically includes collecting and labeling training samples.

[0043] S111. Collect high-resolution images: Collect multiple high-resolution images with different colors and contents from the blasting site to ensure the diversity and breadth of the data.

[0044] S112. Image annotation: Use a professional image annotation platform (such as Roboflow) to annotate the collected high-resolution images, including the outlines and categories of gravel of different sizes. The annotated data is used for model training.

[0045] S210: Data preprocessing.

[0046] The data preprocessing step specifically involves processing and converting high-resolution images for use in model training.

[0047] S211, randomly cut image samples: randomly cut a number of small image samples from the labeled high-resolution images, each sample size is 64x64 pixels, and back up these image samples. This can ensure the diversity and representativeness of the training data.

[0048] S212, generating low-resolution images: using an image scaling algorithm (such as a bicubic interpolation algorithm), downsampling the high-resolution image samples by 4 times to generate corresponding low-resolution image samples. In this way, a one-to-one high-resolution and low-resolution image pair is generated for subsequent model training.

[0049] S310: Model training.

[0050] The model training step involves training an image recognition model using deep learning techniques such as the YOLO model.

[0051] S311, constructing a training set: inputting high-resolution image samples and corresponding low-resolution image samples as training sets into a deep learning model. The training set includes shallow features and deep features of the image.

[0052] S312, Feature Extraction: Extract shallow features of low-resolution image samples based on convolutional neural networks. These features include basic information such as image edges and textures.

[0053] S313, Feature Fusion: Use spatial attention mechanism and residual dense connection block to fuse shallow features, extract feature information of different frequencies, and generate multi-scale feature maps. These feature maps combine high-frequency and low-frequency information of the image and can more accurately represent the details of the image.

[0054] S314, feature upsampling: Upsample the fused multi-scale feature map to restore it to the size of the high-resolution image. Skip connections and sub-pixel convolutions are used in the upsampling process to ensure the clarity and accuracy of the feature map.

[0055] S315, model optimization: Based on a preset loss function (such as L1 or L2 loss function), back-propagate the reconstructed high-resolution image and the original high-resolution image to optimize the model parameters so that the model can accurately restore the details in the low-resolution image.

[0056] The parameter settings for model training are as follows:

[0057] Model type: YOLOv8;

[0058] Number of training rounds: 100;

[0059] Image size: 640x640 pixels;

[0060] Batch size: 16

[0061] Initial learning rate: 0.01;

[0062] Learning rate decay: 0.0001;

[0063] Momentum parameter: 0.937;

[0064] Weight decay: 0.0005;

[0065] Number of warm-up rounds: 3;

[0066] Preheat momentum: 0.8;

[0067] Warm-up learning rate: 0.1;

[0068] Data augmentation: On.

[0069] The above parameter settings include the number of times the model fully explores the dataset, the learning speed of the model, the size of the data subset learned each time, and the expansion of the dataset size and data diversity through data enhancement.

[0070] S410: Image recognition.

[0071] The image recognition step includes using the trained image recognition model to recognize the post-blasting scene image.

[0072] S411, loading model: loading the trained image recognition model into the system, preparing for image recognition.

[0073] S412, input image: input the image of the scene after the blasting to be identified into the model, the model processes the image and outputs the identification result. For example, identifying the outline of gravel: the model identifies the gravel in the image and marks the outline and size of each gravel.

[0074] S510: Results display.

[0075] The result presentation step includes the presentation and analysis of the recognition results.

[0076] S511, display recognition results: compare and display the original image and the recognition results, and mark the recognized gravel parts with different colors to facilitate user viewing. Figure 2 and 3 shown.

[0077] S512, statistical analysis: perform statistical analysis on the identified gravel sizes and generate a statistical report, including the number of gravel, size distribution and other information, to provide reference for users.

[0078] The above-mentioned method for identifying the size of open-pit deep hole step blasting fragments can utilize deep learning technology and image processing algorithms to realize automatic identification and statistics of the fragment size of broken stones after blasting, improve the accuracy and efficiency of identification, and improve production efficiency and work quality. It is of great significance to enhance the intelligence level of open-pit deep hole step blasting technology and promote sustainable development in the fields of mining and engineering construction.

[0079] Corresponding to the method for identifying the size of open-pit deep hole step blasting fragments described in the above embodiments, an embodiment of the present application also provides a device for identifying the size of open-pit deep hole step blasting fragments, including a module for executing the method for identifying the size of open-pit deep hole step blasting fragments provided in any of the above embodiments.

[0080] The process of each module realizing its own function in the device for identifying the block size of open-pit deep hole step blasting provided in the embodiment of the present application can be specifically referred to the aforementioned Figure 1 The description of the illustrated embodiment and other related method embodiments will not be repeated here.

[0081] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0082] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean 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 the present application.

[0083] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0084] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0085] The method for identifying the block size of open-pit deep hole step blasting provided in the embodiment of the present application can be applied to electronic devices such as computers, servers, tablet computers, mobile phones, etc. The embodiment of the present application does not impose any restrictions on the specific type of electronic devices.

[0086] Figure 4 Schematic diagram of the structure of an electronic device provided by an embodiment of the present application. Figure 4 As shown, the electronic device 4 of this embodiment includes: at least one processor 40 ( Figure 4 Only one is shown in the figure), a memory 41, wherein the memory 41 stores a computer program 42 that can be run on the processor 40. When the processor 40 executes the computer program 42, the steps in the above-mentioned method for identifying the block size of open-pit deep hole step blasting are implemented, for example Figure 1 Alternatively, when the processor 40 executes the computer program 42, the functions of the modules / units in the above-mentioned device embodiments are realized.

[0087] The electronic device 4 may be a desktop computer, a notebook, a PDA, a cloud server, etc. The electronic device may include, but is not limited to, a processor 40 and a memory 41. Those skilled in the art will appreciate that Figure 4 It is only an example of the electronic device 4 and does not constitute a limitation of the electronic device 4. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the electronic device may also include an input sending device, a network access device, a bus, etc.

[0088] The processor 40 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0089] In some embodiments, the memory 41 may be an internal storage unit of the electronic device 4, such as a hard disk or memory of the electronic device 4. The memory 41 may also be an external storage device of the electronic device 4, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 4. Further, the memory 41 may also include both an internal storage unit of the electronic device 4 and an external storage device. The memory 41 is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program, etc. The memory 41 may also be used to temporarily store data that has been sent or is to be sent.

[0090] In addition, those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units in each embodiment of the present application can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units.

[0091] An embodiment of the present application also provides an electronic device, which includes at least one memory, at least one processor, and a computer program stored in the at least one memory and executable on the at least one processor, wherein when the processor executes the computer program, the electronic device implements the steps of any of the above-mentioned method embodiments.

[0092] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0093] An embodiment of the present application provides a computer program product. When the computer program product runs on an electronic device, the electronic device can implement the steps in the above-mentioned method embodiments when executing the computer program product.

[0094] If the integrated module / unit is implemented in the form of 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, the present application implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable storage medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0095] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0096] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example 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 performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0097] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0098] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for identifying the size of open-pit deep hole step blasting, characterized in that: include: Acquire the post-blasting scene image to be identified; Based on the image recognition model, the post-blasting scene image is recognized, and a recognition result is output, wherein the recognition result includes the outline and size of each piece of gravel; The image recognition model is obtained by training with the YOLO deep learning model.

2. The method for identifying the size of open-pit deep hole step blasting according to claim 1 is characterized in that: The training method of the image recognition model includes: Collect multiple high-resolution images with different colors and content; Annotate the high-resolution image using the Roboflow graphics annotation platform; Randomly cut a number of small image samples from the labeled high-resolution image to obtain high-resolution image samples; Using an image scaling algorithm, downsampling the high-resolution image samples by a factor of 4 to generate corresponding low-resolution image samples; Input the high-resolution image samples and the corresponding low-resolution image samples as training sets into a YOLO deep learning model; Extracting shallow features of the low-resolution image samples based on a convolutional neural network; The shallow features are fused using a spatial attention mechanism and a residual dense connection block to extract feature information of different frequencies and generate a multi-scale feature map; Upsampling the fused multi-scale feature map to restore it to the size of a high-resolution image, using skip connections and sub-pixel convolutions in the upsampling process; Based on the preset loss function, the reconstructed high-resolution image and the original high-resolution image are back-propagated to optimize the model parameters.

3. The method for identifying the size of open-pit deep hole step blasting according to claim 2 is characterized in that: The parameters of the training process are set as follows: Model type: YOLOv8; Number of training rounds: 100 rounds; Image size: 640x640 pixels; Batch size: 16; Initial learning rate: 0.01; Learning rate decay: 0.0001; Momentum parameter: 0.937; Weight Attenuation: 0.0005; Number of preheating rounds: 3; Preheating momentum: 0.8; Warm-up learning rate: 0.1; Data augmentation: On.

4. A method for identifying the size of open-pit deep hole step blasting according to any one of claims 1 to 3, characterized in that: Also includes: The original image and the recognition result are compared and displayed, and the recognized gravel parts are marked with different colors; Conduct statistical analysis on the identified gravel sizes and generate statistical reports.

5. A device for identifying the size of open-pit deep hole step blasting, characterized in that: include: A module for executing the method according to any one of claims 1 to 4.

6. An electronic device, characterized in that: The electronic device includes a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, the steps of the method according to any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

8. A computer program product, characterized in that The invention comprises a computer program, which implements the steps of the method according to any one of claims 1 to 4 when being executed by a processor.

Images