Insulating layer slice parallel detection device, method and equipment based on multi-camera

Abstract

The application discloses a kind of based on multi-view camera's insulating layer slice parallel detection device, method and equipment, and the application belongs to electric power equipment technical field.The application includes: image acquisition module, for being photographed to slice sample by at least two cameras, and the sample image of slice sample is obtained;Fusion boundary determination module is used to determine the fusion boundary of each sample image;Target parameter splitting module is used to split target parameter into first type parameter and second type parameter according to the fusion boundary;Target parameter detection module is used to detect first type parameter to sample image before fusion;Image fusion module is used to fuse sample image into measurement image;Target parameter detection module is also used to detect second type parameter to the measurement image.The technical scheme can achieve the purpose of slice sample distribution detection, improve detection efficiency and accuracy.

Description

Technical Field

[0001] This application belongs to the field of power equipment technology, specifically relating to a parallel detection device, method and equipment for insulation layer slicing based on a multi-view camera. Background Technology

[0002] As the scope of electricity use continues to expand, the requirements for power transmission are also constantly increasing. To ensure the safe and reliable transmission of electricity to power-consuming areas, cable insulation plays a crucial role, and the methods for testing cable insulation are continuously being upgraded.

[0003] Current methods for inspecting cable insulation primarily involve using slicing equipment to cut sections of the cable insulation and placing these sections onto the testing platform. However, existing testing equipment uses a monocular camera to acquire images of the insulation slices, and processes these images using a single CPU. This not only limits the measurement range of the insulation slices but also suffers from low processing efficiency and measurement accuracy. Summary of the Invention

[0004] The purpose of this application is to provide a parallel detection device, method, and equipment for insulation layer slices based on multi-cameras. This can solve the problems of small detection range, low processing efficiency, and low measurement accuracy of current cable insulation layer slice detection. By using multiple cameras to capture sample slices to obtain multiple sample images and fusion boundaries, the target parameters of the slice samples are decomposed through the fusion boundaries. Then, the parameters of the slice samples before and after fusion are detected according to the decomposed target parameters, which can achieve the purpose of detecting the distribution of slice samples and improve detection efficiency and accuracy.

[0005] In a first aspect, embodiments of this application provide a parallel detection device for insulating layer slicing based on a multi-view camera, the device comprising:

[0006] The image acquisition module is used to capture images of the sliced ​​sample using at least two cameras to obtain sample images of the sliced ​​sample;

[0007] The fusion boundary determination module is used to determine the fusion boundary for each sample image;

[0008] The target parameter splitting module is used to split the target parameters into a first type of parameters and a second type of parameters according to the fusion boundary;

[0009] The target parameter detection module is used to detect the first type of parameters in the sample images before fusion.

[0010] The image fusion module is used to fuse sample images into measurement images;

[0011] The target parameter detection module is also used to detect a second type of parameter in the measured image.

[0012] Furthermore, the target parameter splitting module is specifically used for:

[0013] Based on the fusion boundary, the effective range of each sample image is determined;

[0014] Identify a first type of parameter that performs individual detection based on the effective range of each sample image; and a second type of parameter that performs unified detection on the effective range of each sample image.

[0015] Furthermore, the target parameter detection module is specifically used for:

[0016] Based on the effective range of each sample image, determine the target parameters for a single detection supported by each sample image;

[0017] At least two CPUs are used to process each sample image separately to detect the target parameters supported by the current sample image.

[0018] Furthermore, the target parameter detection module is specifically used for:

[0019] The target CPU for each sample image is determined based on the target parameters of the single detection supported by each sample image.

[0020] Each sample image is transmitted to its corresponding target CPU for processing.

[0021] Furthermore, the fusion boundary determination module is specifically used for:

[0022] The fusion boundary of each sample image is determined based on the setting position and parameter information of the at least two cameras.

[0023] Secondly, embodiments of this application provide a parallel detection device for insulating layer slicing based on a multi-view camera, the device comprising:

[0024] The slice sample is photographed by at least two cameras, and the sample image of the slice sample is obtained by the image acquisition module.

[0025] The fusion boundary of each sample image is determined by the fusion boundary determination module;

[0026] The target parameter splitting module splits the target parameter into a first type of parameter and a second type of parameter according to the fusion boundary.

[0027] The target parameter detection module performs the detection of the first type of parameters on the sample images before fusion.

[0028] The sample images are fused into a measurement image using the image fusion module;

[0029] The target parameter detection module performs second-type parameter detection on the measured image.

[0030] Furthermore, the step of splitting the target parameters into a first type of parameter and a second type of parameter according to the fusion boundary by the target parameter splitting module includes:

[0031] Based on the fusion boundary, the effective range of each sample image is determined;

[0032] Identify a first type of parameter that performs individual detection based on the effective range of each sample image; and a second type of parameter that performs unified detection on the effective range of each sample image.

[0033] Furthermore, the detection of the first type of parameter in the sample image before fusion by the target parameter detection module includes:

[0034] Based on the effective range of each sample image, determine the target parameters for a single detection supported by each sample image;

[0035] At least two CPUs are used to process each sample image separately to detect the target parameters supported by the current sample image.

[0036] Furthermore, the detection of the first type of parameter in the sample image before fusion by the target parameter detection module includes:

[0037] The target CPU for each sample image is determined based on the target parameters of the single detection supported by each sample image.

[0038] Each sample image is transmitted to its corresponding target CPU for processing.

[0039] Thirdly, embodiments of this application provide an electronic device including a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the method described in the second aspect.

[0040] Fourthly, embodiments of this application provide a readable storage medium on which a program or instructions are stored, which, when executed by a processor, implement the steps of the method described in the second aspect.

[0041] Fifthly, embodiments of this application provide a chip, the chip including a processor and a communication interface, the communication interface being coupled to the processor, the processor being used to run programs or instructions to implement the method as described in the second aspect.

[0042] In this embodiment, the image acquisition module is used to capture images of the sliced ​​samples using at least two cameras to obtain sample images of the sliced ​​samples; the fusion boundary determination module is used to determine the fusion boundary of each sample image; the target parameter splitting module is used to split the target parameters into a first type of parameter and a second type of parameter according to the fusion boundary; the target parameter detection module is used to detect the first type of parameter in the sample image before fusion; the image fusion module is used to fuse the sample images into a measurement image; and the target parameter detection module is also used to detect the second type of parameter in the measurement image. The above-described parallel detection device for insulation layer slices based on multi-cameras can solve the problems of small detection range, low processing efficiency, and low measurement accuracy in current cable insulation layer slice detection. By capturing sample slices with multiple cameras to obtain multiple sample images and fusion boundaries, and splitting the target parameters of the sliced ​​samples according to the fusion boundaries, the parameters of the sliced ​​samples before and after fusion can be detected according to the split target parameters, thereby achieving the purpose of slice sample distribution detection and improving detection efficiency and accuracy. Attached Figure Description

[0043] Figure 1 This is a schematic diagram of the structure of the parallel detection device for insulating layer slicing based on a multi-view camera provided in Embodiment 1 of this application;

[0044] Figure 2 This is a schematic diagram of the structure of the parallel detection device for insulating layer slicing based on a multi-view camera provided in Embodiment 2 of this application;

[0045] Figure 3 This is a flowchart illustrating the parallel detection method for insulating layer slicing based on a multi-view camera provided in Embodiment 3 of this application;

[0046] Figure 4 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of this application clearer, specific embodiments of this application will be described in further detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely for explaining this application and not for limiting it. It should also be noted that, for ease of description, only the parts relevant to this application are shown in the drawings, not all of them. Before discussing exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe operations (or steps) as sequential processes, many of these operations can be performed in parallel, concurrently, or simultaneously. Furthermore, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but may also have additional steps not included in the drawings. The process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0048] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0049] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0050] The parallel detection device, method and equipment for insulating layer slicing based on multi-view cameras provided in this application will be described in detail below with reference to the accompanying drawings and through specific embodiments and application scenarios.

[0051] Example 1

[0052] Figure 1 This is a schematic diagram of the parallel detection device for insulating layer slicing based on a multi-view camera provided in Embodiment 1 of this application. Figure 1 As shown, it specifically includes the following:

[0053] The image acquisition module 101 is used to capture images of the slice sample using at least two cameras to obtain sample images of the slice sample;

[0054] The fusion boundary determination module 102 is used to determine the fusion boundary of each sample image;

[0055] The target parameter splitting module 103 is used to split the target parameters into a first type of parameter and a second type of parameter according to the fusion boundary;

[0056] The target parameter detection module 104 is used to detect the first type of parameter in the sample image before fusion;

[0057] Image fusion module 105 is used to fuse sample images into measurement images;

[0058] The target parameter detection module 104 is also used to detect a second type of parameter in the measured image.

[0059] Firstly, this solution can be used in scenarios requiring cross-sectional inspection of cable insulation layers, specifically, scenarios where inspection is performed based on the parameters of cable insulation layer cross-sections. By capturing images of the same cable insulation layer cross-section sample using multiple cameras, breaking down the target parameters, and separately inspecting the parameters before and after image fusion, the distribution of cable insulation layer cross-section samples can be detected, improving the accuracy and efficiency of the inspection.

[0060] Based on the above usage scenarios, it is understood that the executing entity of this application can be software or system platform with functions such as calculation and encoding, without further limitations here.

[0061] In this scheme, the sample image can be images of different parts of the same cable insulation layer slice sample. Specifically, there can be multiple sample images, and multiple sample images can be stitched together to form a complete slice sample. The sample image can be obtained by the image acquisition module 101 using at least two cameras to capture different parts of the same cable insulation layer slice sample simultaneously or at different times. For example, according to the measurement requirements, the cable insulation layer slice sample is divided into a central part and an edge part, etc. The central image is acquired by a central camera, and all edge images of the slice sample are acquired by one or more cameras in a clockwise or counterclockwise order. The central image and the edge images are the sample images.

[0062] In this scheme, the fusion boundary can be the boundary formed by stitching together the sample images; specifically, it can be the overlapping edges of the stitched sample images. The fusion boundary determination module 102 can stitch together each sample image based on the shape, inner and outer contours of the sliced ​​sample, and the cable type corresponding to the sliced ​​sample, thereby determining the fusion boundary of each sample image.

[0063] In this scheme, the first type of parameter can be a local parameter of the slice sample that can be directly measured from the sample image. For example, when the sample image is the edge image of the slice sample, the first type of parameter can be the thickness parameter of the slice sample. The second type of parameter can be the overall parameter of the slice sample that needs to be measured after the sample images are fused. For example, the inner and outer diameters, eccentricity, and area of ​​the slice sample. The target parameter splitting module 103 can split the target parameter to be monitored into the first type of parameter and the second type of parameter according to the fusion boundary.

[0064] In this scheme, the measured image can be the overall image of a slice sample obtained by fusing individual sample images through the image fusion module 105. Specifically, image fusion refers to extracting the advantageous information from each channel to the maximum extent through image processing and computer technology from image data of the same target collected from multiple sources, and finally synthesizing it into a high-quality image to improve the utilization rate of image information, improve the accuracy and reliability of computer interpretation, and enhance the spatial and spectral resolution of the original image, which is beneficial for monitoring. The data format of image fusion includes images containing brightness, color, temperature, distance, and other scene features. These images can be given as a single image or a series of images. Image fusion combines the information from two or more images into a single image, making the fused image contain more information and easier for humans to observe or for computers to process. The goal of image fusion is to reduce the uncertainty and redundancy of the output while maximizing the merging of relevant information under practical application objectives, expanding the temporal and spatial information contained in the image, reducing uncertainty, increasing reliability, and improving the robustness of the system.

[0065] Image fusion is divided into three levels from low to high: data-level fusion, feature-level fusion, and decision-level fusion. Data-level fusion, also known as pixel-level fusion, refers to the process of directly processing data acquired from sensors to obtain a fused image. It is the foundation of high-level image fusion and one of the key focuses of current image fusion research. The advantage of this fusion is that it preserves as much original on-site data as possible, providing subtle information that other fusion levels cannot provide. Pixel-level fusion includes spatial domain algorithms and transform domain algorithms. Spatial domain algorithms have various fusion rule methods, such as logistic filtering, gray-level weighted averaging, and contrast modulation. Transform domain algorithms include pyramid decomposition fusion and wavelet transform. Among these, wavelet transform is currently the most important and commonly used method. Feature-level fusion ensures that different images contain relevant information features, such as infrared light representing the heat of an object and visible light representing the brightness of an object. Decision-level fusion mainly relies on subjective requirements and also has some rules, such as Bayesian methods, DS evidence methods, and voting methods.

[0066] In this scheme, the target parameter detection module 104 can directly detect the first type of parameters in the sample image before fusion, and can also detect the second type of parameters in the measurement image fused by the image fusion module 105. Specifically, the target parameter detection method can be to use calculation software to perform image filtering, grayscale processing, binarization, edge detection, contour extraction, etc. on the sample image or measurement image, and then calculate it according to the set algorithm.

[0067] In this solution, optionally, the fusion boundary determination module 102 is specifically used for:

[0068] The fusion boundary of each sample image is determined based on the setting position and parameter information of the at least two cameras.

[0069] In this scheme, the fusion boundary determination module 102 can determine the distorted and undistorted regions of the sample images based on the parameter information of the at least two cameras. Specifically, since the camera lens is a convex lens, a distorted region will be generated when acquiring the image. Therefore, the range of the undistorted region of each sample image can be determined based on the camera parameter information. By dividing the range of the undistorted region of each sample image and the setting position of each camera, the fusion boundary of each sample image can be determined.

[0070] The distortion area is mainly caused by camera lens distortion, which is actually a general term for the inherent perspective distortion of optical lenses. It's distortion caused by perspective, but because this is an inherent characteristic of lenses (convex lenses converge light, concave lenses diverge light), it cannot be eliminated. For any lens, distortion will occur when very close to the subject, and the closer to the subject, the more severe the distortion. In fact, as the subject gets farther away, perspective distortion decreases, but the image becomes flattened, losing depth and detail. Two subjects far apart appear as if one is above the other. This is a reverse distortion, often occurring when shooting with telephoto lenses. Because the subject is very far from the camera, a flattened perspective effect is produced.

[0071] In this scheme, the fusion boundary of each sample image is determined by the camera's setting position and parameter information, which can achieve the purpose of distribution detection of cable insulation layer slice samples, thus improving the accuracy and efficiency of detection.

[0072] In this solution, optionally, the target parameter splitting module 103 is specifically used for:

[0073] Based on the fusion boundary, the effective range of each sample image is determined;

[0074] Identify a first type of parameter that performs individual detection based on the effective range of each sample image; and a second type of parameter that performs unified detection on the effective range of each sample image.

[0075] In this scheme, the effective range can be the range within the fusion boundary of the sample image where no distortion occurs. Specifically, it can be the image range where the image distortion is small and the image effect is close to that of the sliced ​​sample. The target parameter splitting module 103 can identify a first type of parameter that can be detected individually based on the effective range of each sample image; and a second type of parameter that can be detected uniformly based on the effective range of each sample image. The individual detection can be the processing of detecting parameters contained within the effective range of a single sample image, and the uniform detection can be the processing of detecting parameters contained within the fused effective range obtained after fusing the various sample images.

[0076] In this scheme, the effective range of each sample image is determined based on the fusion boundary, and a first type of parameter is identified for single detection based on the effective range of each sample image; and a second type of parameter is identified for unified detection of the effective range of each sample image. This achieves the goal of combining local detection and fusion detection of slice samples, thereby improving the accuracy and reliability of parameter detection of slice samples.

[0077] The technical solution provided in this application embodiment includes an image acquisition module for capturing images of the sliced ​​samples using at least two cameras to obtain sample images of the sliced ​​samples; a fusion boundary determination module for determining the fusion boundary of each sample image; a target parameter splitting module for splitting the target parameters into a first type of parameter and a second type of parameter according to the fusion boundary; a target parameter detection module for detecting the first type of parameter in the sample image before fusion; an image fusion module for fusing the sample images into a measurement image; and the target parameter detection module is also used to detect the second type of parameter in the measurement image. Through the above-described parallel detection device for insulation layer slices based on multi-cameras, the problems of small detection range, low processing efficiency, and low measurement accuracy of current cable insulation layer slice detection can be solved. By capturing sample slices with multiple cameras to obtain multiple sample images and fusion boundaries, and splitting the target parameters of the sliced ​​samples using the fusion boundaries, the parameters of the sliced ​​samples before and after fusion can be detected according to the split target parameters, thereby achieving the purpose of slice sample distribution detection and improving detection efficiency and accuracy.

[0078] Example 2

[0079] Figure 2 This is a schematic diagram of the parallel detection device for insulating layer slicing based on a multi-view camera provided in Embodiment 2 of this application. Figure 2 As shown, it specifically includes the following:

[0080] The target parameter detection module 204 is used to detect the first type of parameter in the sample image before fusion;

[0081] Image fusion module 205 is used to fuse sample images into measurement images;

[0082] The target parameter detection module 204 is also used to detect a second type of parameter in the measured image.

[0083] In this solution, optionally, the target parameter detection module 204 is specifically used for:

[0084] Based on the effective range of each sample image, determine the target parameters for a single detection supported by each sample image;

[0085] At least two CPUs are used to process each sample image separately to detect the target parameters supported by the current sample image.

[0086] In this scheme, the target parameter can be the target parameter that needs to be detected on the slice sample, such as: the diameter, perimeter, thickness, coordinate values ​​corresponding to different thickness points, eccentricity, slice type number, measuring device number, etc. of the slice. The target parameter detection module 204 can determine the target parameter supported by a single detection for each sample image based on the effective range of each sample image. For example, if the current sample image is the edge image of a slice sample, and the effective range of the sample includes the inner and outer edges of the slice sample, then the target parameter supported by a single detection for the sample image can be the thickness parameter of the slice sample in the current sample image.

[0087] In this scheme, the CPU (central processing unit) serves as the core of the computer system's computation and control, the final execution unit for information processing and program execution, and one of the main devices of an electronic computer, a core component of the computer. Its main functions are interpreting computer instructions and processing data in computer software. The CPU mainly consists of two parts: the control unit and the arithmetic logic unit (ALU), including high-speed cache memory and the data and control buses that connect them. The CPU's main functions are processing instructions, executing operations, controlling timing, and processing data. In computer architecture, the CPU is the core hardware unit that controls and allocates all hardware resources (such as memory and input / output units) and performs general-purpose calculations. All software layer operations in the computer system are ultimately mapped to CPU operations through the instruction set. The target parameter detection module 204 can use at least two CPUs to process each sample image separately to detect the target parameters supported by the current sample image.

[0088] In this scheme, based on the effective range of each sample image, the target parameters supported by each sample image for single detection are determined, and at least two CPUs are used to process each sample image to detect the target parameters supported by the current sample image. This can reduce the computing power borne by each CPU, improve the computing efficiency of each CPU, and improve the efficiency and accuracy of slice sample detection.

[0089] In this solution, optionally, the target parameter detection module 204 is specifically used for:

[0090] The target CPU for each sample image is determined based on the target parameters of the single detection supported by each sample image.

[0091] Each sample image is transmitted to its corresponding target CPU for processing.

[0092] In this scheme, the target parameter detection module 204 can determine the target CPU for each sample image based on the target parameters supported by each sample image for single detection, as well as the number of CPU cores or threads, operating frequency, architecture, and cache capacity. The sample images acquired by the image acquisition module 101 are then wirelessly transmitted to the corresponding target CPU for processing.

[0093] In this scheme, the target CPU for each sample image is determined based on the target parameters of the single detection supported by each sample image, and each sample image is transmitted to the corresponding target CPU for processing. This achieves the purpose of using different CPUs to process different sample images, thereby improving the computing speed of each CPU and the efficiency and accuracy of slice sample detection.

[0094] The technical solution provided in this application determines the target parameters for single detection supported by each sample image based on the effective range of each sample image, and uses at least two CPUs to process each sample image to detect the target parameters supported by the current sample image. Based on the target parameters for single detection supported by each sample image, the target CPU for each sample image is determined, and each sample image is transmitted to the corresponding target CPU for processing. This achieves the purpose of using different CPUs to process different sample images, reduces the computing power borne by each CPU, and improves the efficiency and accuracy of slice sample detection.

[0095] The parallel detection device for insulating layer slicing based on a multi-view camera in this application embodiment can be a device, or a component, integrated circuit, or chip in a terminal. The device can be a mobile electronic device or a non-mobile electronic device. For example, mobile electronic devices can be mobile phones, tablets, laptops, PDAs, in-vehicle electronic devices, wearable devices, ultra-mobile personal computers (UMPCs), netbooks, or personal digital assistants (PDAs), etc., while non-mobile electronic devices can be servers, network-attached storage (NAS), personal computers (PCs), televisions (TVs), ATMs, or self-service machines, etc. This application embodiment does not impose specific limitations.

[0096] The parallel detection device for insulating layer slicing based on a multi-view camera in this application embodiment can be a device with an operating system. This operating system can be Android, iOS, or other possible operating systems; this application embodiment does not specifically limit it.

[0097] Example 3

[0098] Figure 3 This is a flowchart illustrating the parallel detection method for insulating layer slicing based on a multi-view camera provided in Embodiment 3 of this application. Figure 3 As shown, it specifically includes the following:

[0099] S101, The slice sample is photographed by at least two cameras, and the sample image of the slice sample is obtained by the image acquisition module;

[0100] S102, the fusion boundary of each sample image is determined by the fusion boundary determination module;

[0101] S103, the target parameter is split into a first type of parameter and a second type of parameter according to the fusion boundary by the target parameter splitting module;

[0102] S104, The target parameter detection module performs the first type of parameter detection on the sample image before fusion;

[0103] S105, uses the image fusion module to fuse sample images into measurement images;

[0104] S106, the target parameter detection module detects the second type of parameters in the measurement image.

[0105] Furthermore, the step of splitting the target parameters into a first type of parameter and a second type of parameter according to the fusion boundary by the target parameter splitting module includes:

[0106] Based on the fusion boundary, the effective range of each sample image is determined;

[0107] Identify a first type of parameter that performs individual detection based on the effective range of each sample image; and a second type of parameter that performs unified detection on the effective range of each sample image.

[0108] Furthermore, the detection of the first type of parameter in the sample image before fusion by the target parameter detection module includes:

[0109] Based on the effective range of each sample image, determine the target parameters for a single detection supported by each sample image;

[0110] At least two CPUs are used to process each sample image separately to detect the target parameters supported by the current sample image.

[0111] Furthermore, the detection of the first type of parameter in the sample image before fusion by the target parameter detection module includes:

[0112] The target CPU for each sample image is determined based on the target parameters of the single detection supported by each sample image.

[0113] Each sample image is transmitted to its corresponding target CPU for processing.

[0114] Furthermore, the step of determining the fusion boundary of each sample image through the fusion boundary determination module includes:

[0115] The fusion boundary of each sample image is determined based on the setting position and parameter information of the at least two cameras.

[0116] In this embodiment, at least two cameras are used to capture images of the sliced ​​samples, and an image acquisition module is used to obtain sample images of the sliced ​​samples. A fusion boundary determination module determines the fusion boundary of each sample image. A target parameter splitting module splits the target parameters into a first type of parameter and a second type of parameter according to the fusion boundary. A target parameter detection module detects the first type of parameter in the sample image before fusion. An image fusion module fuses the sample images into a measurement image. A target parameter detection module detects the second type of parameter in the measurement image. This multi-camera-based parallel detection device for insulation layer slices solves the problems of small detection range, low processing efficiency, and low measurement accuracy in current cable insulation layer slice detection. By capturing multiple sample images and fusion boundaries using multiple cameras, and splitting the target parameters of the sliced ​​samples according to the fusion boundaries, the parameters of the sliced ​​samples before and after fusion are detected based on the split target parameters. This achieves the purpose of detecting the distribution of sliced ​​samples, improving detection efficiency and accuracy.

[0117] Example 4

[0118] like Figure 4 As shown, this application embodiment also provides an electronic device 400, including a processor 401, a memory 402, and a program or instructions stored in the memory 402 and executable on the processor 401. When the program or instructions are executed by the processor 401, they implement the various processes of the above-described embodiment of the parallel detection device for insulating layer slicing based on a multi-view camera and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0119] It should be noted that the electronic devices in the embodiments of this application include the mobile electronic devices and non-mobile electronic devices described above.

[0120] Example 5

[0121] This application also provides a readable storage medium storing a program or instructions. When the program or instructions are executed by a processor, they implement the various processes of the above-described parallel detection device for insulating layer slicing based on a multi-view camera, and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0122] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0123] Example 6

[0124] This application embodiment also provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement the various processes of the above-described embodiment of the parallel detection device for insulating layer slicing based on a multi-view camera, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0125] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0126] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0127] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0128] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

[0129] The above description is merely a preferred embodiment and the technical principles employed in this application. This application is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions that can be made by those skilled in the art will not depart from the scope of protection of this application. Therefore, although this application has been described in detail through the above embodiments, this application is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of this application, the scope of which is determined by the scope of the claims.

Claims

1. A multi-camera-based insulation layer slice parallel detection device, characterized in that, The device comprises: An image acquisition module configured to capture a slice sample by at least two cameras to obtain a sample image of the slice sample; A fusion boundary determination module configured to determine a fusion boundary of each sample image, and specifically configured to determine the fusion boundary of each sample image according to the setting positions and parameter information of the at least two cameras; A target parameter splitting module configured to determine an effective range of each sample image according to the fusion boundary, identify a first type of parameter for single detection according to the effective range of each sample image, and identify a second type of parameter for unified detection of the effective range of each sample image; A target parameter detection module configured to determine a target CPU of each sample image according to a target parameter supported by each sample image for single detection, and transmit each sample image to the corresponding target CPU for processing to detect the target parameter supported by the current sample image, wherein at least two CPUs are used to process each sample image to detect the target parameter supported by the current sample image; An image fusion module configured to fuse the sample images into a measurement image; The target parameter detection module is further configured to detect the second type of parameter of the measurement image.

2. A multi-camera-based insulation layer slice parallel detection method, characterized in that, The method comprises: Capturing a slice sample by at least two cameras to obtain a sample image of the slice sample by using an image acquisition module; Determining a fusion boundary of each sample image by using a fusion boundary determination module, and specifically determining the fusion boundary of each sample image according to the setting positions and parameter information of the at least two cameras; Determining an effective range of each sample image according to the fusion boundary, identifying a first type of parameter for single detection according to the effective range of each sample image, and identifying a second type of parameter for unified detection of the effective range of each sample image; Determining a target CPU of each sample image according to a target parameter supported by each sample image for single detection, and transmitting each sample image to the corresponding target CPU for processing to detect the target parameter supported by the current sample image, wherein at least two CPUs are used to process each sample image to detect the target parameter supported by the current sample image; Fusing the sample images into a measurement image by using an image fusion module; Detecting the second type of parameter of the measurement image by using a target parameter detection module.

3. An electronic device, comprising: The device comprises a processor, a memory, and a program or instruction stored in the memory and executable on the processor, and the program or instruction is executed by the processor to implement the steps of the multi-camera-based insulation layer slice parallel detection method according to claim 2.

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