CN119762490B - Stone granularity detection method and device, electronic equipment and storage medium - Google Patents
Stone granularity detection method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The invention discloses a stone granularity detection method, a device, electronic equipment and a storage medium, which belong to the technical field of image processing, wherein the method comprises the steps of obtaining a plurality of training stone images under different irradiation angles of a training stone; the method comprises the steps of determining contour point coordinates of a training stone, training to obtain different stone contour models corresponding to different irradiation angles based on an image segmentation algorithm, the contour point coordinates of the training stone and the irradiation angles corresponding to all training stone images, obtaining a plurality of target stone images under different irradiation angles of a target stone, inputting all the target stone images into the corresponding stone contour models to obtain the contour point coordinates of the target stone in all the target stone images, and determining the granularity value of the target stone based on the contour point coordinates of the target stone. According to the technical scheme provided by the embodiment of the invention, the stone images with different irradiation angles are adopted to synthesize one stone image, so that the granularity value of the stone is determined according to the stone image, and the accuracy of stone granularity detection is improved.
Description
Technical Field
The invention belongs to the technical field of image processing, and particularly relates to a stone granularity detection method, a stone granularity detection device, electronic equipment and a storage medium.
Background
With the development of scientific technology, in concrete engineering, the granularity of stone affects the strength, durability and workability of the concrete, so the determination of the granularity of stone is a very important problem.
In the prior art, the particle size of the stone is calculated mainly manually, for example, a screen mesh with proper aperture and a vibrating screen device are selected, then the stone is weighed and screened step by step, the particle weighing record of each screened particle is taken down, and the particle size of the stone is calculated. However, the calculation mode has larger error and lower calculation accuracy.
Disclosure of Invention
The embodiment of the invention provides a scheme for solving the problems of larger error and lower calculation precision of the traditional mode for calculating the granularity of the stone in the related technology.
In a first aspect, the invention provides a method for detecting the granularity of stone blocks, the method comprising:
Under different illumination angles of the light source, acquiring a plurality of training stone images of the training stone;
determining contour point coordinates of training stones in the plurality of training stone images;
training to obtain different stone contour models corresponding to different irradiation angles based on an image segmentation algorithm, contour point coordinates of training stones in the plurality of training stone images and irradiation angles corresponding to the training stone images;
Under different irradiation angles of a light source, acquiring a plurality of target stone images of a target stone, and inputting each target stone image into a stone contour model corresponding to the irradiation angle corresponding to each target stone image to obtain contour point coordinates of the target stone in each target stone image;
and determining the granularity value of the target stone based on the contour point coordinates of the target stone in each target stone image.
In a second aspect, the present invention provides a rock particle size detection apparatus, the apparatus comprising:
The acquisition unit is used for acquiring a plurality of training stone images of the training stone under different illumination angles of the light source;
the determining unit is used for determining the contour point coordinates of the training stones in the plurality of training stone images;
the training unit is used for training to obtain different stone contour models corresponding to different irradiation angles based on an image segmentation algorithm, contour point coordinates of training stones in the plurality of training stone images and irradiation angles corresponding to the training stone images;
The input unit is used for acquiring a plurality of target stone images of the target stone under different irradiation angles of the light source, and inputting each target stone image into a stone contour model corresponding to the irradiation angle corresponding to each target stone image to obtain contour point coordinates of the target stone in each target stone image;
the determining unit is further used for determining the granularity value of the target stone block based on the contour point coordinates of the target stone block in each target stone block image.
In a third aspect, the present invention provides an electronic device comprising:
processor, and
A memory for storing executable instructions of the processor;
Wherein the processor is configured to perform the first aspect or any of the possible implementation manners of the first aspect via execution of the executable instructions.
In a fourth aspect, embodiments of the present invention provide a computer readable storage medium having stored thereon a computer program which when executed by a processor implements the first aspect or any of the possible implementations of the first aspect.
The technical scheme includes that a plurality of scene images of a target scene under different shooting angles are obtained, a plurality of training stone images of training stones are obtained under different irradiation angles of a light source, contour point coordinates of the training stones in the plurality of training stone images are determined, different stone contour models corresponding to different irradiation angles are obtained through training based on an image segmentation algorithm, the contour point coordinates of the training stones in the plurality of training stone images and the irradiation angles corresponding to the training stone images, a plurality of target stone images of the target stones are obtained under different irradiation angles of the light source, the target stone images are input into the stone contour models corresponding to the irradiation angles corresponding to the target stone images, the contour point coordinates of the target stones in the target stone images are obtained, and the particle size value of the target stone is determined based on the contour point coordinates of the target stones in the target stone images. According to the technical scheme provided by the embodiment of the invention, the plurality of training stone images of the training stone under different irradiation angles of the light source are obtained, different stone contour models corresponding to different irradiation angles are obtained through training according to the contour point coordinates of the training stone in the plurality of training stone images and the irradiation angles corresponding to the training stone images, the contour point coordinates of the target stone in each target stone image can be obtained by utilizing the stone contour model and the obtained plurality of target stone images of the target stone, and the granularity value of the target stone is determined based on the contour point coordinates of the target stone in each target stone image, so that the granularity value of the target stone is not required to be calculated manually, the accuracy value of calculating the granularity value of the stone is greatly improved, and the working efficiency is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the related art, the drawings that are required to be used in the embodiments or the related technical descriptions will be briefly described, and it is apparent that the drawings in the following descriptions are some embodiments of the present invention, and other drawings may be obtained according to the drawings without inventive effort for those skilled in the art. In the drawings:
FIG. 1 is a schematic flow chart of a method for detecting stone granularity according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a block size detection apparatus according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings. The embodiments described below by referring to the drawings are illustrative and intended to explain the present invention and should not be construed as limiting the invention.
The terms first and second and the like in the description, the claims and the drawings of embodiments of the invention are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that embodiments of the invention described herein may be implemented, for example, in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The stone granularity detection method provided by the embodiment of the invention can be operated on terminal equipment or a server. The terminal device may be a local terminal device. The server may be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDNs, basic cloud computing services such as big data and artificial intelligent platforms, etc., the terminal device may be a wearable device, and may also include a desktop, a mobile phone, a tablet computer, etc., without limitation
With the development of science and technology, in the concrete engineering, the granularity of stone influences the strength of the concrete. Durability and workability, the determination of the particle size of the stone blocks is a very important issue.
In the prior art, the particle size of the stone is calculated mainly manually, for example, a screen mesh with proper aperture and a vibrating screen device are selected, then the stone is weighed and screened step by step, the particle weighing record of each screened particle is taken down, and the particle size of the stone is calculated. However, the calculation mode has larger error and lower calculation accuracy.
The following describes the technical scheme of the present invention and how the technical scheme of the present invention solves the above technical problems in detail with specific embodiments. The following embodiments may be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments. Embodiments of the present invention will be described below with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart of a method for detecting stone granularity according to an exemplary embodiment of the present invention, where the method may be applied to an electronic device with a data processing function, and the method is applied to a photographing device, and the method at least includes the following steps S101 to S105:
s101, acquiring a plurality of training stone images of the training stone under different illumination angles of the light source.
In some embodiments, the plurality of training stone images are captured by a capture device at the same capture perspective.
Specifically, in the actual shooting process, a light source is arranged above the training stone, after the light source is turned on, shooting is performed through a shooting device, a training stone image is obtained, after the light source is arranged at other positions, after the light source is turned on again, shooting is performed at the same position through the shooting device, and another training stone image is obtained.
In some embodiments, the method is suitable for a shooting device, a plurality of light source devices are arranged on the shooting device, the positions of the light source devices are different, and the light source devices are in one-to-one correspondence with the irradiation angles.
In this embodiment, the irradiation angle is preset.
Specifically, in order to improve shooting efficiency, a plurality of light sources can be arranged above the training stone, each light source is located at a different position, when a first light source is independently turned on, shooting is performed at a preset position through a shooting device, a first training stone image is obtained, until an nth light source is independently turned on, shooting is performed at the preset position through the shooting device, an nth training stone image is obtained, and N training stone images are obtained altogether, wherein N is a positive integer.
In some embodiments, the method further comprises pre-processing the plurality of training stone images to improve the quality of the plurality of training stone images.
The preprocessing at least comprises denoising processing and edge sharpening.
S102, determining contour point coordinates of the training stones in the plurality of training stone images.
In some embodiments, the contour point coordinates are pixel coordinates.
And S103, training to obtain different stone contour models corresponding to different irradiation angles based on an image segmentation algorithm, contour point coordinates of training stones in the plurality of training stone images and irradiation angles corresponding to the training stone images.
Specifically, an image segmentation algorithm Mask R-CNN based on deep learning is used for training the contour point coordinates of the training stones in the plurality of training stone images and the corresponding irradiation angles of the training stone images. For example, if there are N total light sources, and N training stone images are captured, then the depth learning based image segmentation algorithm Mask R-CNN may train N stone contour models, one for each light source. Wherein N is a positive integer.
The Mask R-CNN is a model based on deep learning, and is mainly used for target detection and segmentation tasks, and accurately segments the outline of a target.
In some embodiments, to improve the accuracy of the rock training model, the method may further comprise steps S11-S13:
S11, acquiring a plurality of training stone images of the training stone under any irradiation angle of the light source.
Wherein the number of training stone images at different illumination angles is the same. Specifically, under any illumination angle of the light source, training stone images of different training stones are acquired, and the accuracy of the training stone outline model is improved by increasing the number of the training stone images.
S12, determining the outline point coordinates of the training stones in all the training stone images.
S13, training to obtain a stone contour model aiming at a plurality of training stone images corresponding to any irradiation angle based on an image segmentation algorithm, contour point coordinates of training stones in the plurality of training stone images and the irradiation angle, and further obtaining a plurality of stone contour models corresponding to the plurality of irradiation angles.
Specifically, if there are N light sources in total, under the fixed position of each light source, 100 training stone images of different training stones are collected, 100 x N training stone images are collected in total, then 100 training stone images corresponding to each light source are trained based on an image segmentation algorithm Mask R-CNN for deep learning to obtain a stone contour model, and N stone contour models can be trained in total, and each light source corresponds to one stone contour model.
In some embodiments, in order to improve the accuracy and applicability of the stone contour model, in the actual use process, the collected target stone is used as a training stone, and the stone contour model is optimized.
S104, under different irradiation angles of the light source, acquiring a plurality of target stone images of the target stone, and inputting each target stone image into a stone contour model corresponding to the irradiation angle corresponding to each target stone image to obtain contour point coordinates of the target stone in each target stone image.
In some embodiments, according to the different stone contour models corresponding to the different irradiation angles obtained in the step S103, the obtained multiple target stone block images are input into the stone contour model corresponding to the irradiation angle corresponding to each target stone block image, so as to obtain the contour point coordinates of the target stone block in each target stone block image. For example, if there are M illumination angles in total, M stone contour models are trained, i.e. stone contour model 1, stone contour model 2. M is a positive integer, and the acquired M target stone images, i.e. target stone image 1, target stone image 2. A target stone image 1 is input into the stone contour model 1, a target stone image 2 is input into the stone contour model 3. And obtaining the contour point coordinates of the target stone in each target stone image output by each stone contour model.
S105, determining the granularity value of the target stone based on the contour point coordinates of the target stone in each target stone image.
In some embodiments, determining the particle size value of the target stone based on the contour point coordinates of the target stone in each target stone image includes obtaining a target image based on each target stone image and the contour point coordinates of the target stone in each target stone image and performing non-maximum suppression, determining the target contour point coordinates of the target stone in the target image, and determining the particle size value of the target stone based on the target contour point coordinates of the target stone in the target image.
Non-maximum suppression (NMS, non-Maximum Suppression), which refers to suppressing elements that are not maxima, can be understood as local maximum searches.
In some embodiments, the method further includes determining a confidence level for contour point coordinates of the target stone in each of the target stone images.
The confidence level refers to the ratio of the total number of intervals containing the overall parameters in a plurality of sample intervals for constructing the overall parameters, and is generally expressed by 1-alpha.
In this embodiment, the obtaining a target image based on each target stone image and the coordinates of the contour points of the target stone in each target stone image and performing non-maximum suppression, and determining the coordinates of the target contour points of the target stone in the target image includes:
and performing non-maximum suppression based on each target stone image, the confidence coefficient of the contour point coordinates of the target stones in each target stone image and the contour point coordinates of the target stones in each target stone image to obtain a target image, and determining the target contour point coordinates of the target stones in the target image.
Specifically, there are three target stone images, namely a target stone image 1, a target stone image 2, a target stone image 3;
The contour point coordinates of the target stone in the target stone image 1 comprise contour point coordinates 1 (confidence level is 3), contour point coordinates 2 (confidence level is 4), contour point coordinates 3 (confidence level is 5) and contour point coordinates 4 (confidence level is 4);
the contour point coordinates of the target stone in the target stone image 2 comprise contour point coordinates 1 (confidence coefficient is 2), contour point coordinates 2 (confidence coefficient is 5), contour point coordinates 3 (confidence coefficient is 4) and contour point coordinates 4 (confidence coefficient is 3);
The contour point coordinates of the target stone in the target stone image 3 comprise contour point coordinates 2 (with confidence degree of 4), contour point coordinates 3 (with confidence degree of 5), contour point coordinates 4 (with confidence degree of 4) and contour point coordinates 5 (with confidence degree of 5);
In combination with the above example, non-maximum suppression is performed based on each target stone image, the confidence coefficient of the contour point coordinates of the target stone in each target stone image, and the contour point coordinates of the target stone in each target stone image, to obtain a target image, and the target contour point coordinates of the target stone in the target image are determined, which specifically includes the following steps:
Storing the confidence coefficient maximum in the same contour point coordinates, and taking out other contour points:
Contour point coordinate 1 (confidence level 3) and contour point coordinate 1 (confidence level 2), reserving contour point coordinate 1 (confidence level 3);
Contour point coordinate 2 (confidence 4), contour point coordinate 2 (confidence 5) and contour point coordinate 2 (confidence 4), contour point coordinate 2 (confidence 5) is reserved;
Contour point coordinates 3 (confidence 5), contour point coordinates 3 (confidence 4) and contour point coordinates 3 (confidence 5), contour point coordinates 3 (confidence 5) are reserved;
Contour point coordinate 4 (confidence 4), contour point coordinate 4 (confidence 3) and contour point coordinate 4 (confidence 4), contour point coordinate 4 (confidence 4) is reserved;
Contour point coordinates 5 (confidence 5), contour point coordinates 5 (confidence 5) are reserved;
According to the above, the target stone image 1, the target stone image 2, and the target stone image 3 are fused into one target stone image, wherein in the target stone image, the contour point coordinates of the target stone are respectively contour point coordinate 1 (confidence is 3), contour point coordinate 2 (confidence is 5), contour point coordinate 3 (confidence is 5), contour point coordinate 4 (confidence is 4), and contour point coordinate 5 (confidence is 5).
In some embodiments, determining the particle size value of the target stone based on the target contour point coordinates of the target stone in the target image includes converting the target contour point coordinates of the target stone in the target image to world coordinates by a calibration method, and determining the particle size value of the target stone based on the world coordinates and the target image.
In this embodiment, after the target contour point coordinates of the target stone in the target image are converted into world coordinates by a calibration method, the size of the target stone in the target image is the actual size of the target stone.
The technical scheme includes that a plurality of scene images of a target scene under different shooting angles are obtained, a plurality of training stone images of training stones are obtained under different irradiation angles of a light source, contour point coordinates of the training stones in the plurality of training stone images are determined, different stone contour models corresponding to different irradiation angles are obtained through training based on an image segmentation algorithm, the contour point coordinates of the training stones in the plurality of training stone images and the irradiation angles corresponding to the training stone images, a plurality of target stone images of the target stones are obtained under different irradiation angles of the light source, the target stone images are input into the stone contour models corresponding to the irradiation angles corresponding to the target stone images, the contour point coordinates of the target stones in the target stone images are obtained, and the particle size value of the target stone is determined based on the contour point coordinates of the target stones in the target stone images. According to the technical scheme provided by the embodiment of the invention, the plurality of training stone images of the training stone under different irradiation angles of the light source are obtained, different stone contour models corresponding to different irradiation angles are obtained through training according to the contour point coordinates of the training stone in the plurality of training stone images and the irradiation angles corresponding to the training stone images, the contour point coordinates of the target stone in each target stone image can be obtained by utilizing the stone contour model and the obtained plurality of target stone images of the target stone, and the granularity value of the target stone is determined based on the contour point coordinates of the target stone in each target stone image, so that the granularity value of the target stone is not required to be calculated manually, the accuracy value of calculating the granularity value of the stone is greatly improved, and the working efficiency is improved.
Fig. 2 is a schematic structural diagram of a stone granularity detecting device according to an exemplary embodiment of the present invention;
The device comprises an acquisition unit 201, a determination unit 202, a training unit 203 and an input unit 204;
An obtaining unit 201, configured to obtain a plurality of training stone images of a training stone under different illumination angles of a light source;
a determining unit 202, configured to determine coordinates of contour points of training stones in the plurality of training stone images;
the training unit 203 is configured to train to obtain different stone contour models corresponding to different irradiation angles based on an image segmentation algorithm, coordinates of contour points of training stones in the plurality of training stone images, and irradiation angles corresponding to each training stone image;
the input unit 204 is configured to obtain multiple target stone images of a target stone under different irradiation angles of the light source, and input each target stone image into a stone contour model corresponding to an irradiation angle corresponding to each target stone image, so as to obtain contour point coordinates of a target stone in each target stone image;
The determining unit 202 is further configured to determine a granularity value of the target stone based on coordinates of contour points of the target stone in each target stone image.
In some embodiments, the apparatus is configured to determine a particle size value of the target stone based on coordinates of contour points of the target stone in each image of the target stone, and the apparatus is specifically configured to:
Based on each target stone image and the contour point coordinates of the target stone in each target stone image, performing non-maximum suppression to obtain a target image, and determining the target contour point coordinates of the target stone in the target image;
and determining the granularity value of the target stone block based on the target contour point coordinates of the target stone block in the target image.
In some embodiments, the apparatus is configured to determine a particle size value of a target stone block based on target contour point coordinates of the target stone block in the target image, and the apparatus is specifically configured to:
Converting the coordinates of the target contour points of the target stone in the target image into world coordinates by a calibration method;
and determining the granularity value of the target stone block based on the world coordinates and the target image.
In some embodiments, the apparatus is further configured to determine a confidence level for contour point coordinates of the target stone in each of the target stone images;
The device is also used for obtaining a target image based on each target stone image and the contour point coordinates of the target stone in each target stone image and performing non-maximum suppression, and determining the target contour point coordinates of the target stone in the target image, and the device is specifically used for:
and performing non-maximum suppression based on each target stone image, the confidence coefficient of the contour point coordinates of the target stones in each target stone image and the contour point coordinates of the target stones in each target stone image to obtain a target image, and determining the target contour point coordinates of the target stones in the target image.
In some embodiments, the apparatus is further configured to pre-process the plurality of training stone images to improve the quality of the plurality of training stone images;
the preprocessing at least comprises denoising processing and edge sharpening.
In some embodiments, the contour point coordinates are pixel coordinates.
In some embodiments, the device is suitable for a shooting device, and a plurality of light source devices are arranged on the shooting device, and the positions of the light source devices are different;
wherein the light source devices are in one-to-one correspondence with the irradiation angles.
The technical scheme includes that a plurality of scene images of a target scene under different shooting angles are obtained, a plurality of training stone images of training stones are obtained under different irradiation angles of a light source, contour point coordinates of the training stones in the plurality of training stone images are determined, different stone contour models corresponding to different irradiation angles are obtained through training based on an image segmentation algorithm, the contour point coordinates of the training stones in the plurality of training stone images and the irradiation angles corresponding to the training stone images, a plurality of target stone images of the target stones are obtained under different irradiation angles of the light source, the target stone images are input into the stone contour models corresponding to the irradiation angles corresponding to the target stone images, the contour point coordinates of the target stones in the target stone images are obtained, and the particle size value of the target stone is determined based on the contour point coordinates of the target stones in the target stone images. According to the technical scheme provided by the embodiment of the invention, the plurality of training stone images of the training stone under different irradiation angles of the light source are obtained, different stone contour models corresponding to different irradiation angles are obtained through training according to the contour point coordinates of the training stone in the plurality of training stone images and the irradiation angles corresponding to the training stone images, the contour point coordinates of the target stone in each target stone image can be obtained by utilizing the stone contour model and the obtained plurality of target stone images of the target stone, and the granularity value of the target stone is determined based on the contour point coordinates of the target stone in each target stone image, so that the granularity value of the target stone is not required to be calculated manually, the accuracy value of calculating the granularity value of the stone is greatly improved, and the working efficiency is improved.
It should be understood that apparatus embodiments and method embodiments may correspond with each other and that similar descriptions may refer to the method embodiments. To avoid repetition, no further description is provided here. Specifically, the apparatus may perform the above method embodiments, and the foregoing and other operations and/or functions of each module in the apparatus are respectively for corresponding flows in each method in the above method embodiments, which are not described herein for brevity.
The apparatus of the embodiments of the present invention is described above in terms of functional modules with reference to the accompanying drawings. It should be understood that the functional module may be implemented in hardware, or may be implemented by instructions in software, or may be implemented by a combination of hardware and software modules. Specifically, each step of the method embodiment in the embodiment of the present invention may be implemented by an integrated logic circuit of hardware in a processor and/or an instruction in a software form, and the steps of the method disclosed in connection with the embodiment of the present invention may be directly implemented as a hardware decoding processor or implemented by a combination of hardware and software modules in the decoding processor. Alternatively, the software modules may be located in a well-established storage medium in the art such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, and the like. The storage medium is located in a memory, and the processor reads information in the memory, and in combination with hardware, performs the steps in the above method embodiments.
Fig. 3 is a schematic block diagram of an electronic device provided by an embodiment of the present invention, which may include:
A memory 301 and a processor 302, the memory 301 being for storing a computer program and for transmitting the program code to the processor 302. In other words, the processor 302 may call and run a computer program from the memory 301 to implement the method in the embodiment of the present invention.
For example, the processor 302 may be configured to perform the above-described method embodiments according to instructions in the computer program.
In some embodiments of the invention, the processor 302 may include, but is not limited to:
A general purpose Processor, digital signal Processor (DIGITAL SIGNAL Processor, DSP), application SPECIFIC INTEGRATED Circuit (ASIC), field programmable gate array (Field Programmable GATE ARRAY, FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like.
In some embodiments of the present invention, the memory 301 includes, but is not limited to:
Volatile memory and/or nonvolatile memory. The nonvolatile Memory may be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable EPROM (EEPROM), or a flash Memory. The volatile memory may be random access memory (Random Access Memory, RAM) which acts as external cache memory. By way of example, and not limitation, many forms of RAM are available, such as static random access memory (STATIC RAM, SRAM), dynamic random access memory (DYNAMIC RAM, DRAM), synchronous Dynamic Random Access Memory (SDRAM), double data rate Synchronous dynamic random access memory (Double DATA RATE SDRAM, DDR SDRAM), enhanced Synchronous dynamic random access memory (ENHANCED SDRAM, ESDRAM), synchronous link dynamic random access memory (SYNCH LINK DRAM, SLDRAM), and Direct memory bus RAM (DR RAM).
In some embodiments of the invention, the computer program may be partitioned into one or more modules that are stored in the memory 301 and executed by the processor 302 to perform the methods provided by the invention. The one or more modules may be a series of computer program instruction segments capable of performing the specified functions, which are used to describe the execution of the computer program in the electronic device.
As shown in fig. 3, the electronic device may further include:
A transceiver 303, the transceiver 303 being connectable to the processor 302 or the memory 301.
The processor 302 may control the transceiver 303 to communicate with other devices, and in particular, may send information or data to other devices, or receive information or data sent by other devices. The transceiver 303 may include a transmitter and a receiver. The transceiver 303 may further include antennas, the number of which may be one or more.
It will be appreciated that the various components in the electronic device are connected by a bus system that includes, in addition to a data bus, a power bus, a control bus, and a status signal bus.
The present invention also provides a computer storage medium having stored thereon a computer program which, when executed by a computer, enables the computer to perform the method of the above-described method embodiments. Alternatively, embodiments of the present invention also provide a computer program product comprising instructions which, when executed by a computer, cause the computer to perform the method of the method embodiments described above.
When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, produces a flow or function in accordance with embodiments of the invention, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by a wired (e.g., coaxial cable, fiber optic, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains an integration of one or more available media. The usable medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a digital video disc (digital video disc, DVD)), or a semiconductor medium (e.g., a Solid State Drive (SSD)), or the like.
Those of ordinary skill in the art will appreciate that the various illustrative modules and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be additional divisions when actually implemented, for example, multiple modules or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or modules, which may be in electrical, mechanical, or other forms.
The modules illustrated as separate components may or may not be physically separate, and components shown as modules may or may not be physical modules, i.e., may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. For example, functional modules in various embodiments of the present invention may be integrated into one processing module, or each module may exist alone physically, or two or more modules may be integrated into one module.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily appreciate variations or alternatives within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (7)
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| CN202510274653.7A CN119762490B (en) | 2025-03-10 | 2025-03-10 | Stone granularity detection method and device, electronic equipment and storage medium |
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| CN202510274653.7A CN119762490B (en) | 2025-03-10 | 2025-03-10 | Stone granularity detection method and device, electronic equipment and storage medium |
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| CN118464725A (en) * | 2024-05-10 | 2024-08-09 | 苏州昂森诺智能科技有限公司 | Intelligent vision online granularity analysis system based on AI algorithm technology |
| CN119206530A (en) * | 2024-09-09 | 2024-12-27 | 山东师范大学 | A method, device, equipment and medium for dynamic target recognition of remote sensing images |
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| CN118464725A (en) * | 2024-05-10 | 2024-08-09 | 苏州昂森诺智能科技有限公司 | Intelligent vision online granularity analysis system based on AI algorithm technology |
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