Stone particle size detection method and device, electronic equipment and storage medium

Abstract

The invention discloses a stone granularity detection method and device, electronic equipment and a storage medium, and belongs to the technical field of image processing, and the method comprises the steps: obtaining a plurality of training stone images under different irradiation angles of a training stone; contour point coordinates of the training stone are determined; based on an image segmentation algorithm, the contour point coordinates of the training stones and the irradiation angles corresponding to the training stone images, performing training to obtain different stone contour models corresponding to different irradiation angles; obtaining a plurality of target stone images under different irradiation angles of the target stone, and inputting each target stone image into the corresponding stone contour model 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. According to the technical scheme provided by the embodiment of the invention, the stone images with different irradiation angles are synthesized into one stone image, and then the particle size value of the stone is determined according to the stone image, so that the stone particle size detection precision is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of image processing, and in particular relates to a stone particle size detection method, device, electronic equipment and storage medium. Background Art

[0002] With the development of science and technology, in concrete engineering, the particle size of stones affects the strength, durability and construction performance of concrete, so the determination of stone particle size is a very important issue.

[0003] In the prior art, the method of calculating the particle size of stones is mainly through manual methods, for example, selecting a screen with an appropriate aperture and a vibrating screen device, then weighing the stones, and screening them step by step, taking the weighing record of each screening particle, and calculating the particle size of the stones. However, this calculation method has a large error and low calculation accuracy. Summary of the invention

[0004] The embodiment of the present invention provides a solution to solve the problem in the related art that the traditional method of calculating the stone particle size has large errors and low calculation accuracy.

[0005] In a first aspect, the present invention provides a method for detecting stone particle size, the method comprising: Acquire multiple training stone images of the training stone under different illumination angles of the light source; Determining the coordinates of the contour points of the training stones in the plurality of training stone images; Based on the image segmentation algorithm, the coordinates of the contour points of the training stones in the plurality of training stone images and the illumination angles corresponding to the training stone images, different stone contour models corresponding to different illumination angles are obtained through training; Under different illumination angles of the light source, multiple target stone images of the target stone are obtained, and each target stone image is input into a stone contour model corresponding to the illumination angle corresponding to each target stone image, so as to obtain the contour point coordinates of the target stone in each target stone image; Based on the coordinates of the contour points of the target stone in each target stone image, the particle size value of the target stone is determined.

[0006] In a second aspect, the present invention provides a stone particle size detection device, the device comprising: An acquisition unit, used for acquiring a plurality of training stone images of the training stone under different illumination angles of the light source; A determination unit, used to determine the coordinates of the contour points of the training stones in the plurality of training stone images; A training unit, configured to train and obtain different stone contour models corresponding to different illumination angles based on an image segmentation algorithm, the coordinates of contour points of the training stones in the plurality of training stone images, and the illumination angles corresponding to the training stone images; An input unit is used to obtain multiple target stone images of the target stone under different illumination angles of the light source, and input each target stone image into a stone contour model corresponding to the illumination angle corresponding to each target stone image, so as to obtain the contour point coordinates of the target stone in each target stone image; The determination unit is further used to determine the particle size value of the target stone based on the coordinates of the contour points of the target stone in each target stone image.

[0007] In a third aspect, the present invention provides an electronic device, comprising: Processor; and A memory, configured to store executable instructions of the processor; The processor is configured to execute any method in the first aspect or any possible implementation of the first aspect by executing the executable instructions.

[0008] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the computer program implements any method in the first aspect or any possible implementation manner of the first aspect.

[0009] The technical solution provided by the present invention comprises the following steps: obtaining a plurality of scene images of a target scene at different shooting angles; obtaining a plurality of training stone images of a training stone at different illumination angles of a light source; determining the coordinates of contour points of the training stone in the plurality of training stone images; training different stone contour models corresponding to different illumination angles based on an image segmentation algorithm, the coordinates of contour points of the training stone in the plurality of training stone images, and the illumination angles corresponding to each training stone image; obtaining a plurality of target stone images of a target stone at different illumination angles of a light source, and inputting each target stone image into a stone contour model corresponding to the illumination angle corresponding to each target stone image, to obtain the coordinates of contour points of the target stone in each target stone image; and determining the particle size value of the target stone based on the coordinates of contour points of the target stone in each target stone image. The technical solution provided by each embodiment of the present invention obtains multiple training stone images of training stones under different illumination angles of the light source, and trains different stone contour models corresponding to different illumination angles according to the contour point coordinates of the training stones in the multiple training stone images and the illumination angles corresponding to each training stone image, and then uses the stone contour model and the obtained multiple target stone images of the target stone to obtain the contour point coordinates of the target stone in each target stone image; based on the contour point coordinates of the target stone in each target stone image, the particle size value of the target stone is determined, without the need to calculate the particle size value of the target stone manually, which greatly improves the accuracy of calculating the stone particle size value and improves work efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings: Figure 1 A schematic flow chart of a stone particle size detection method provided by an embodiment of the present invention; Figure 2 A schematic structural diagram of a stone particle size detection device provided by an embodiment of the present invention; Figure 3 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0011] The embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and intended to be used to explain the present invention, but should not be understood as limiting the present invention.

[0012] The terms "first" and "second" and the like in the specification, claims and drawings of the embodiments of the present invention are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein, for example. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device including a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0013] The stone particle size detection method provided in the embodiment of the present invention can be run on a terminal device or a server. The terminal device can be a local terminal device. The server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the terminal device can be a wearable device, and can also include desktops, mobile phones, tablets, etc., which are not limited here. With the development of science and technology, in concrete engineering, the particle size of stones affects the strength, durability and construction performance of concrete, so the determination of stone particle size is a very important issue.

[0014] In the prior art, the method of calculating the particle size of stones is mainly through manual methods, for example, selecting a screen with an appropriate aperture and a vibrating screen device, then weighing the stones, and screening them step by step, taking the weighing record of each screening particle, and calculating the particle size of the stones. However, this calculation method has a large error and low calculation accuracy.

[0015] The technical solution of the present invention and how the technical solution of the present invention solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present invention will be described below in conjunction with the accompanying drawings.

[0016] Figure 1 A flowchart of a stone particle size detection method provided by an exemplary embodiment of the present invention is provided. The method can be applied to an electronic device having a data processing function. Taking the method applied to a photographing device as an example, the scheme at least includes the following steps S101-S105: S101, acquiring a plurality of training stone images of the training stone under different illumination angles of a light source.

[0017] In some embodiments, the plurality of training stone images are taken by a photographing device at the same photographing angle of view.

[0018] Specifically, during the actual shooting process, a light source is set above the training stone. After the light source is turned on, the shooting device is used to shoot and obtain a training stone image. After the light source is set at another position and turned on again, the shooting device is used to shoot at the same position to obtain another training stone image.

[0019] In some embodiments, the method is applicable to a photographing device, which is provided with a plurality of light source devices, each of which is provided at a different position; wherein the light source devices correspond one to one with the illumination angles.

[0020] In this embodiment, the irradiation angle is preset.

[0021] Specifically, in order to improve the efficiency of shooting, multiple light sources can be set above the training stone, each light source is located at a different position, and when the first light source is turned on alone, the shooting device is used to shoot at a preset position to obtain the first training stone image, until the Nth light source is turned on alone, the shooting device is used to shoot at a preset position to obtain the Nth training stone image, and a total of N training stone images are obtained, where N is a positive integer.

[0022] In some embodiments, the method further includes: preprocessing the plurality of training stone images to improve the quality of the plurality of training stone images.

[0023] Wherein, the preprocessing at least includes: denoising and edge sharpening.

[0024] S102, determining the coordinates of the contour points of the training stones in the plurality of training stone images.

[0025] In some embodiments, the contour point coordinates are pixel coordinates.

[0026] S103, based on the image segmentation algorithm, the coordinates of the contour points of the training stones in the plurality of training stone images, and the illumination angles corresponding to the training stone images, different stone contour models corresponding to different illumination angles are obtained through training.

[0027] Specifically, the deep learning-based image segmentation algorithm ‌Mask R-CNN trains the contour point coordinates of the training stones in the multiple training stone images and the illumination angles corresponding to each training stone image. For example, if there are N light sources in total and N training stone images are taken, the deep learning-based image segmentation algorithm ‌Mask R-CNN can train N stone contour models, one for each light source. Wherein N is a positive integer.

[0028] Among them, Mask R-CNN is a deep learning-based model, which is mainly used for target detection and segmentation tasks to accurately segment the contours of the target.

[0029] In some embodiments, in order to improve the accuracy of the stone training model, the method may further include steps S11-S13: S11: Acquire multiple training stone images of the training stone at any illumination angle of the light source.

[0030] The number of training stone images under different illumination angles is the same. Specifically, under any illumination angle of the light source, training stone images of different training stones are collected, and the accuracy of the training stone contour model is improved by increasing the number of training stone images.

[0031] S12, determining the coordinates of the contour points of the training stones in all the training stone images.

[0032] S13, for a plurality of training stone images corresponding to any illumination angle, based on an image segmentation algorithm, the coordinates of the contour points of the training stones in the plurality of training stone images and the illumination angle, a stone contour model is trained to obtain a plurality of stone contour models corresponding to a plurality of illumination angles.

[0033] Specifically, if there are N light sources in total, 100 training stone images of different training stones are collected at a fixed position of each light source, and a total of 100*N training stone images are collected. Then, the 100 training stone images corresponding to each light source are trained based on the deep learning image segmentation algorithm Mask R-CNN to obtain a stone contour model. A total of N stone contour models can be trained, one stone contour model for each light source.

[0034] In some embodiments, in order to improve the accuracy and applicability of the stone contour model, during actual use, the collected target stones are used as training stones to optimize the stone contour model.

[0035] S104, obtaining multiple target stone images of the target stone under different illumination angles of the light source, and inputting each target stone image into a stone contour model corresponding to the illumination angle corresponding to each target stone image, to obtain the contour point coordinates of the target stone in each target stone image.

[0036] In some embodiments, according to the different stone contour models corresponding to different illumination angles obtained in the above step S103, the acquired multiple target stone images are input into the stone contour models corresponding to the illumination angles corresponding to each target stone image, and the contour point coordinates of the target stone in each target stone image are obtained. 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... stone contour model M, where M is a positive integer, and the acquired M target stone images, i.e., target stone image 1, target stone image 2... target stone image M, target stone image 1 is input into stone contour model 1, target stone image 2 is input into stone contour model 3... target stone image M is input into stone contour model M, and the contour point coordinates of the target stone in each target stone image output by each stone contour model are obtained.

[0037] S105 , determining the particle size value of the target stone based on the coordinates of the contour points of the target stone in each target stone image.

[0038] In some embodiments, the particle size value of the target stone is determined based on the contour point coordinates of the target stone in each target stone image, including: 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 determining the particle size value of the target stone based on the target contour point coordinates of the target stone in the target image.

[0039] Among them, non-maximum suppression (NMS) refers to suppressing elements that are not maximum values, which can be understood as local maximum search.

[0040] In some embodiments, the method further includes: determining the confidence level of the coordinates of the contour points of the target stone in each target stone image.

[0041] Among them, confidence refers to the ratio of the intervals containing the population parameter to the total number of sample intervals used to construct the population parameter, which is generally expressed as 1-α.

[0042] In this embodiment, the method of 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 target contour point coordinates of the target stone in the target image includes: Based on each target stone image, the confidence 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, non-maximum suppression is performed to obtain a target image, and the target contour point coordinates of the target stone in the target image are determined.

[0043] Specifically, there are three target stone images, namely, target stone image 1, target stone image 2, and target stone image 3; The contour point coordinates of the target stone in the target stone image 1 include: contour point coordinate 1 (confidence is 3), contour point coordinate 2 (confidence is 4), contour point coordinate 3 (confidence is 5), and contour point coordinate 4 (confidence is 4); The contour point coordinates of the target stone in the target stone image 2 include: contour point coordinate 1 (confidence is 2), contour point coordinate 2 (confidence is 5), contour point coordinate 3 (confidence is 4), and contour point coordinate 4 (confidence is 3); The contour point coordinates of the target stone in the target stone image 3 include: contour point coordinate 2 (confidence is 4), contour point coordinate 3 (confidence is 5), contour point coordinate 4 (confidence is 4), and contour point coordinate 5 (confidence is 5); In combination with the above example, based on each target stone image, the confidence 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, non-maximum suppression is performed to obtain a target image, and the target contour point coordinates of the target stone in the target image are determined. The specific process is as follows: Save the coordinates of the same contour points with the highest confidence, and extract the other contour points: Contour point coordinate 1 (confidence is 3) and contour point coordinate 1 (confidence is 2), retain contour point coordinate 1 (confidence is 3); Contour point coordinate 2 (confidence is 4), contour point coordinate 2 (confidence is 5) and contour point coordinate 2 (confidence is 4), retain contour point coordinate 2 (confidence is 5); Contour point coordinate 3 (confidence is 5), contour point coordinate 3 (confidence is 4) and contour point coordinate 3 (confidence is 5), retain contour point coordinate 3 (confidence is 5); Contour point coordinate 4 (confidence is 4), contour point coordinate 4 (confidence is 3) and contour point coordinate 4 (confidence is 4), retain contour point coordinate 4 (confidence is 4); Contour point coordinate 5 (confidence level 5), retain contour point coordinate 5 (confidence level 5); According to the above, target stone image 1, target stone image 2, and target stone image 3 are fused into a target stone image, wherein the contour point coordinates of the target stone in the target stone image are 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), respectively.

[0044] 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 into world coordinates through a calibration method; and determining the particle size value of the target stone based on the world coordinates and the target image.

[0045] 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.

[0046] The technical solution provided by the present invention comprises the following steps: obtaining a plurality of scene images of a target scene at different shooting angles; obtaining a plurality of training stone images of a training stone at different illumination angles of a light source; determining the coordinates of contour points of the training stone in the plurality of training stone images; training different stone contour models corresponding to different illumination angles based on an image segmentation algorithm, the coordinates of contour points of the training stone in the plurality of training stone images, and the illumination angles corresponding to each training stone image; obtaining a plurality of target stone images of a target stone at different illumination angles of a light source, and inputting each target stone image into a stone contour model corresponding to the illumination angle corresponding to each target stone image, to obtain the coordinates of contour points of the target stone in each target stone image; and determining the particle size value of the target stone based on the coordinates of contour points of the target stone in each target stone image. The technical solution provided by each embodiment of the present invention obtains multiple training stone images of training stones under different illumination angles of the light source, and trains different stone contour models corresponding to different illumination angles according to the contour point coordinates of the training stones in the multiple training stone images and the illumination angles corresponding to each training stone image, and then uses the stone contour model and the obtained multiple target stone images of the target stone to obtain the contour point coordinates of the target stone in each target stone image; based on the contour point coordinates of the target stone in each target stone image, the particle size value of the target stone is determined, without the need to calculate the particle size value of the target stone manually, which greatly improves the accuracy of calculating the stone particle size value and improves work efficiency.

[0047] Figure 2 A schematic structural diagram of a stone particle size detection device provided by an exemplary embodiment of the present invention; The device includes: an acquisition unit 201, a determination unit 202, a training unit 203 and an input unit 204; An acquisition unit 201 is used to acquire multiple training stone images of the training stone under different illumination angles of the light source; A determination unit 202, configured to determine the coordinates of the contour points of the training stones in the plurality of training stone images; A training unit 203 is used to train different stone contour models corresponding to different illumination angles based on an image segmentation algorithm, the coordinates of the contour points of the training stones in the plurality of training stone images, and the illumination angles corresponding to the training stone images; The input unit 204 is used to obtain multiple target stone images of the target stone under different illumination angles of the light source, and input each target stone image into the stone contour model corresponding to the illumination angle corresponding to each target stone image, so as to obtain the contour point coordinates of the target stone in each target stone image; The determination unit 202 is further configured to determine the particle size value of the target stone based on the coordinates of the contour points of the target stone in each target stone image.

[0048] In some embodiments, the device is used to determine the particle size value of the target stone based on the coordinates of the contour points of the target stone in each target stone image, and the device is specifically used to: 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, a target image is obtained, and the coordinates of the target contour points of the target stone in the target image are determined; Based on the target contour point coordinates of the target stone in the target image, the particle size value of the target stone is determined.

[0049] In some embodiments, the device is used to determine the particle size value of the target stone based on the target contour point coordinates of the target stone in the target image, and the device is specifically used to: The target contour point coordinates of the target stone in the target image are converted into world coordinates by a calibration method; Based on the world coordinates and the target image, a particle size value of the target stone is determined.

[0050] In some embodiments, the device is further used to: determine the confidence level of the coordinates of the contour points of the target stone in each target stone image; The device is also used to obtain 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 perform non-maximum suppression, and determine the target contour point coordinates of the target stone in the target image. The device is specifically used for: Based on each target stone image, the confidence 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, non-maximum suppression is performed to obtain a target image, and the target contour point coordinates of the target stone in the target image are determined.

[0051] In some embodiments, the apparatus is further used to: pre-process the plurality of training stone images to improve the quality of the plurality of training stone images; Wherein, the preprocessing at least includes: denoising and edge sharpening.

[0052] In some embodiments, the contour point coordinates are pixel coordinates.

[0053] In some embodiments, the device is suitable for a photographing device, and the photographing device is provided with a plurality of light source devices, each light source device being provided at a different position; Wherein, the light source equipment corresponds to the irradiation angle one by one.

[0054] The technical solution provided by the present invention comprises the following steps: obtaining a plurality of scene images of a target scene at different shooting angles; obtaining a plurality of training stone images of a training stone at different illumination angles of a light source; determining the coordinates of contour points of the training stone in the plurality of training stone images; training different stone contour models corresponding to different illumination angles based on an image segmentation algorithm, the coordinates of contour points of the training stone in the plurality of training stone images, and the illumination angles corresponding to each training stone image; obtaining a plurality of target stone images of a target stone at different illumination angles of a light source, and inputting each target stone image into a stone contour model corresponding to the illumination angle corresponding to each target stone image, to obtain the coordinates of contour points of the target stone in each target stone image; and determining the particle size value of the target stone based on the coordinates of contour points of the target stone in each target stone image. The technical solution provided by each embodiment of the present invention obtains multiple training stone images of training stones under different illumination angles of the light source, and trains different stone contour models corresponding to different illumination angles according to the contour point coordinates of the training stones in the multiple training stone images and the illumination angles corresponding to each training stone image, and then uses the stone contour model and the obtained multiple target stone images of the target stone to obtain the contour point coordinates of the target stone in each target stone image; based on the contour point coordinates of the target stone in each target stone image, the particle size value of the target stone is determined, without the need to calculate the particle size value of the target stone manually, which greatly improves the accuracy of calculating the stone particle size value and improves work efficiency.

[0055] It should be understood that the device embodiment and the method embodiment may correspond to each other, and similar descriptions may refer to the method embodiment. To avoid repetition, no further description is given here. Specifically, the device may perform the above method embodiment, and the above and other operations and / or functions of each module in the device are the corresponding processes in each method in the above method embodiment, respectively, and no further description is given here for the sake of brevity.

[0056] The above describes the device of the embodiment of the present invention from the perspective of the functional module in conjunction with the accompanying drawings. It should be understood that the functional module can be implemented in hardware form, can be implemented by instructions in software form, and can also be implemented by a combination of hardware and software modules. Specifically, the steps of the method embodiment in the embodiment of the present invention can be completed by the hardware integrated logic circuit and / or software instructions in the processor, and the steps of the method disclosed in the embodiment of the present invention can be directly embodied as a hardware decoding processor to execute, or a combination of hardware and software modules in the decoding processor to execute. Optionally, the software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps in the above method embodiment in conjunction with its hardware.

[0057] Figure 3 is a schematic block diagram of an electronic device provided by an embodiment of the present invention, and the electronic device may include: The memory 301 and the processor 302, the memory 301 is used to store the computer program and transmit the program code to the processor 302. In other words, the processor 302 can call and run the computer program from the memory 301 to implement the method in the embodiment of the present invention.

[0058] For example, the processor 302 may be configured to execute the above method embodiments according to instructions in the computer program.

[0059] In some embodiments of the present invention, the processor 302 may include but is not limited to: 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.

[0060] In some embodiments of the present invention, the memory 301 includes but is not limited to: Volatile memory and / or non-volatile memory. Among them, the non-volatile memory can be read-only memory (ROM), programmable ROM (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM) or flash memory. Volatile memory can be random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct memory bus random access memory (Direct Rambus RAM, DR RAM).

[0061] In some embodiments of the present invention, the computer program may be divided into one or more modules, which are stored in the memory 301 and executed by the processor 302 to complete the method provided by the present invention. The one or more modules may be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program in the electronic device.

[0062] like Figure 3 As shown, the electronic device may also include: The transceiver 303 may be connected to the processor 302 or the memory 301 .

[0063] The processor 302 may control the transceiver 303 to communicate with other devices, specifically, to send information or data to other devices, or to receive information or data sent by other devices. The transceiver 303 may include a transmitter and a receiver. The transceiver 303 may further include an antenna, and the number of antennas may be one or more.

[0064] It should be understood that the various components in the electronic device are connected via a bus system, wherein the bus system includes not only a data bus but also a power bus, a control bus and a status signal bus.

[0065] The present invention also provides a computer storage medium on which a computer program is stored, and when the computer program is executed by a computer, the computer can perform the method of the above method embodiment. In other words, the present invention also provides a computer program product containing instructions, and when the instructions are executed by a computer, the computer can perform the method of the above method embodiment.

[0066] When software is used for implementation, it can 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 the computer program instructions are loaded and executed on a computer, the process or function according to the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can 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 can be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media integrated. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a digital video disc (DVD)), or a semiconductor medium (e.g., a solid state disk (SSD)), etc.

[0067] Those skilled in the art will appreciate that the modules 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 the present invention.

[0068] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the module is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0069] The modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network units. 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, the functional modules in various embodiments of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0070] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for detecting stone particle size, characterized in that: Methods include: Acquire multiple training stone images of the training stone under different illumination angles of the light source; Determining the coordinates of the contour points of the training stones in the plurality of training stone images; Based on the image segmentation algorithm, the coordinates of the contour points of the training stones in the plurality of training stone images and the illumination angles corresponding to the training stone images, different stone contour models corresponding to different illumination angles are obtained through training; Under different illumination angles of the light source, multiple target stone images of the target stone are obtained, and each target stone image is input into a stone contour model corresponding to the illumination angle corresponding to each target stone image, so as to obtain the contour point coordinates of the target stone in each target stone image; Based on the coordinates of the contour points of the target stone in each target stone image, the particle size value of the target stone is determined.

2. The method according to claim 1, characterized in that Based on the coordinates of the contour points of the target stones in each target stone image, the particle size value of the target stone is determined, including: 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, a target image is obtained, and the coordinates of the target contour points of the target stone in the target image are determined; Based on the target contour point coordinates of the target stone in the target image, the particle size value of the target stone is determined.

3. The method according to claim 2, characterized in that 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: The target contour point coordinates of the target stone in the target image are converted into world coordinates by a calibration method; Based on the world coordinates and the target image, a particle size value of the target stone is determined.

4. The method according to claim 2, characterized in that: The method further comprises: determining the confidence of the coordinates of the contour points of the target stone in each target stone image; The method of 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, comprises: Based on each target stone image, the confidence 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, non-maximum suppression is performed to obtain a target image, and the target contour point coordinates of the target stone in the target image are determined.

5. The method according to claim 1, characterized in that The method further comprises: preprocessing the plurality of training stone images to improve the quality of the plurality of training stone images; Wherein, the preprocessing at least includes: denoising and edge sharpening.

6. The method according to claim 1, characterized in that The contour point coordinates are pixel coordinates.

7. The method according to any one of claims 1 to 6, characterized in that: The method is applicable to a photographing device, wherein a plurality of light source devices are arranged on the photographing device, and each light source device is arranged at a different position; Wherein, the light source equipment corresponds to the irradiation angle one by one.

8. A stone particle size detection device, characterized in that: The device comprises: An acquisition unit, used for acquiring a plurality of training stone images of the training stone under different illumination angles of the light source; A determination unit, used to determine the coordinates of the contour points of the training stones in the plurality of training stone images; A training unit, configured to train and obtain different stone contour models corresponding to different illumination angles based on an image segmentation algorithm, the coordinates of contour points of the training stones in the plurality of training stone images, and the illumination angles corresponding to the training stone images; An input unit is used to obtain multiple target stone images of the target stone under different illumination angles of the light source, and input each target stone image into a stone contour model corresponding to the illumination angle corresponding to each target stone image, so as to obtain the contour point coordinates of the target stone in each target stone image; The determination unit is further used to determine the particle size value of the target stone based on the coordinates of the contour points of the target stone in each target stone image.

9. An electronic device, characterized in that: include: processor; as well as A memory, configured to store executable instructions of the processor; The processor is configured to perform the method of any one of claims 1 to 7 by executing the executable instructions.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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