CN112184701A - Method, device and system for determining detection result - Google Patents
Method, device and system for determining detection result Download PDFInfo
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- CN112184701A CN112184701A CN202011139621.XA CN202011139621A CN112184701A CN 112184701 A CN112184701 A CN 112184701A CN 202011139621 A CN202011139621 A CN 202011139621A CN 112184701 A CN112184701 A CN 112184701A
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Abstract
The application provides a method, a device and a system for determining a detection result, and relates to the technical field of industrial detection. The method comprises the following steps: the first server acquires an image (corresponding to a target image in the application) of a to-be-detected region containing a target object, and then determines a first detection result of the target image according to a preset rule. Thereafter, a second detection result for the target image is acquired from the MEC server. And finally, determining whether the target object has defects or not by combining the first detection result and the second detection result. The first detection result comprises normal target images or abnormal target images, and the second detection result comprises normal target images or abnormal target images.
Description
Technical Field
The present application relates to the field of industrial detection technologies, and in particular, to a method, an apparatus, and a system for determining a detection result.
Background
At present, in the industrial production field, in order to improve the appearance detection efficiency of production equipment, a machine vision detection mode is adopted to replace manual detection. The existing machine vision detection mode is that an acquisition device acquires an image signal of an object to be detected and transmits the image signal to an image acquisition card, and the image acquisition card can convert the acquired image signal into a digital signal and then transmit the digital signal to a special server. Finally, the special server can process the received digital signals according to preset rules to determine the appearance detection result of the object to be detected.
Although the existing machine vision detection method has higher efficiency than manual detection, the accuracy of detection still needs to be improved.
Disclosure of Invention
The application provides a method, a device and a system for determining a detection result, which can improve the accuracy of machine vision detection.
In order to achieve the purpose, the technical scheme is as follows:
in a first aspect, the present application provides a method for determining a detection result, which is applied to a first server. The method comprises the following steps: the first server acquires an image (corresponding to a target image in the application) of a to-be-detected region containing a target object, and then determines a first detection result of the target image according to a preset rule. Thereafter, a second detection result for the target image is acquired from the MEC server. And finally, determining whether the target object has defects or not by combining the first detection result and the second detection result. The first detection result comprises normal target images or abnormal target images, and the second detection result comprises normal target images or abnormal target images.
According to the determination method of the detection result, after the first server determines the first detection result of the target image, the second detection result of the target image can be obtained from the MEC server, and finally whether the target object has defects or not is determined through the two detection results. The first detection result and the second detection result are detected by different detection modes, so that the first detection result and the second detection result can mutually verify whether the detection results are correct, and the accuracy of the detection results determined by the detection result determining method provided by the application is higher than that of the detection results determined by the existing detection modes.
Optionally, in a possible design, the "determining whether the target object has the defect by combining the first detection result and the second detection result" may include: when the first detection result is that the target image is abnormal or the second detection result is that the target image is abnormal, determining that the target object has defects; and when the first detection result is that the target image is normal and the second detection result is that the target image is normal, determining that the target object has no defects.
Optionally, in a possible design, the "determining the first detection result for the target image according to the preset rule" may include: determining target information of a target image; and determining a first detection result for the target image according to the target information.
The target information may include one or more of pixel distribution information, brightness, and color, among others.
Optionally, in a possible design, the target image includes an identification number, and correspondingly, the "acquiring the second detection result for the target image from the MEC server" may include: sending a first request including an identification number to an MEC server; and receiving a second detection result sent by the MEC server.
The first request is used for requesting to acquire a second detection result of the target image.
Alternatively, in one possible design, when the first server determines that the target object has a defect, the target image may be stored in a defect product library.
In a second aspect, the present application provides a method for determining a detection result, which may be applied to an MEC server. The method comprises the following steps: the MEC server firstly obtains an image to be detected, the image to be detected is an image of a region to be detected containing an object to be detected, and the image to be detected comprises a target image. And then, the MEC server determines a third detection result of the image to be detected according to a preset training model. Then, the MEC server receives a first request sent by the first server and used for requesting to acquire a second detection result of the target image, wherein the first request comprises the identification number of the target image. And finally, the MEC server determines a second detection result of the target image from a third detection result of the image to be detected according to the identification number, and sends the second detection result to the first server. The third detection result comprises that the image to be detected is normal or abnormal, and the second detection result comprises that the target image is normal or abnormal.
It can be seen that the MEC server determines a third detection result for the image to be detected according to the preset training model, which is different from the visual detection manner of the first server. In addition, the MEC server may determine a second detection result for the target image from the third detection results that have been detected according to the identification number of the target image sent by the first server and send the second detection result to the first server. In this way, the first server can determine whether the target object has defects by combining the second detection result and the first detection result determined by the first server. Therefore, compared with the existing detection mode, the method for determining the detection result has higher accuracy.
In a third aspect, the present application provides a first server, including: the device comprises an acquisition module and a determination module. The acquisition module is used for acquiring a target image, and the target image is an image of a to-be-detected area containing a target object. The determining module is used for determining a first detection result of the target image acquired by the acquiring module according to a preset rule, wherein the first detection result comprises that the target image is normal or the target image is abnormal. The obtaining module is further configured to obtain a second detection result for the target image from the MEC server, where the second detection result includes that the target image is normal or that the target image is abnormal. The determining module is further used for determining whether the target object has defects according to the first detection result obtained by the obtaining module and the second detection result obtained by the obtaining module.
In a fourth aspect, the present application provides an MEC server, comprising: the device comprises an acquisition module, a determination module, a receiving module and a sending module. The device comprises an acquisition module, a detection module and a display module, wherein the acquisition module is used for acquiring an image to be detected; the image to be detected is an image of a region to be detected containing an object to be detected; the image to be detected comprises a target image. The determining module is used for determining a third detection result of the image to be detected acquired by the acquiring module according to a preset training model; the third detection result comprises that the image to be detected is normal or the image to be detected is abnormal. The receiving module is used for receiving a first request sent by a first server; the first request is used for requesting to acquire a second detection result of the target image; the first request includes an identification number of the target image. The determining module is further used for determining a second detection result of the target image from a third detection result of the image to be detected according to the identification number received by the receiving module; the second detection result includes that the target image is normal or the target image is abnormal. And the sending module is used for sending the second detection result determined by the determining module to the first server.
In a fifth aspect, the present application provides a device for determining a detection result, including a processor, coupled to a memory, for reading and executing instructions in the memory to implement the method for determining a detection result provided in the first aspect or the method for determining a detection result provided in the second aspect.
Optionally, the determination means of the detection result may further comprise a memory for storing program instructions and data of the determination means of the detection result. Further optionally, the determination device of the detection result may further comprise a transceiver for performing the step of transceiving data, signaling or information under the control of the processor of the determination device of the detection result, for example, the determination device of the detection result acquires the target image.
Alternatively, the determination device of the detection result may be a physical machine, or may be a part of a device in the physical machine, for example, a system on chip in the physical machine. The system-on-chip is configured to support the determining means for determining the detection result to implement the functions referred to in the first aspect or the second aspect, for example, to receive, transmit or process data and/or information referred to in the determining method for determining the detection result. The chip system includes a chip and may also include other discrete devices or circuit structures.
In a sixth aspect, the present application provides a computer-readable storage medium having instructions stored therein, which when executed by a computer, implement the method for determining the detection result as provided in the first aspect or the second aspect.
In a seventh aspect, the present application provides a computer program product comprising computer instructions which, when run on a computer, cause the computer to perform the method of determining a detection result as provided in the first or second aspect.
In an eighth aspect, the present application provides a detection result determining system, including the first server provided in the third aspect and the MEC server provided in the fourth aspect.
It should be noted that all or part of the computer instructions may be stored on the computer readable storage medium. The computer readable storage medium may be packaged with the processor of the device for determining the detection result, or may be packaged separately from the processor of the device for determining the detection result, which is not limited in this application.
For the descriptions of the third to eighth aspects in the present application, reference may be made to the detailed descriptions of the first and second aspects; in addition, for the beneficial effects described in the third to eighth aspects, reference may be made to the beneficial effect analysis of the first and second aspects, and details are not repeated here.
In the present application, the names of the above-mentioned determination means of the detection result do not limit the devices or the functional modules themselves, and in actual implementation, the devices or the functional modules may appear by other names. Insofar as the functions of the respective devices or functional blocks are similar to those of the present invention, they are within the scope of the claims of the present application and their equivalents.
These and other aspects of the present application will be more readily apparent from the following description.
Drawings
Fig. 1 is a schematic structural diagram of a detection result determination system according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of another detection result determination system provided in the embodiment of the present application;
fig. 3 is a schematic flowchart of a method for determining a detection result according to an embodiment of the present disclosure;
fig. 4 is a schematic flowchart of another method for determining a detection result according to an embodiment of the present disclosure;
fig. 5 is a schematic flowchart of another method for determining a detection result according to an embodiment of the present disclosure;
fig. 6 is a schematic flowchart of another method for determining a detection result according to an embodiment of the present disclosure;
fig. 7 is a schematic flowchart of another method for determining a detection result according to an embodiment of the present disclosure;
fig. 8 is a schematic flowchart of another method for determining a detection result according to an embodiment of the present disclosure;
fig. 9 is a schematic data interaction diagram of a method for determining a detection result according to an embodiment of the present application;
fig. 10 is a schematic structural diagram of a first server according to an embodiment of the present disclosure;
fig. 11 is a schematic structural diagram of an MEC server according to an embodiment of the present disclosure;
fig. 12 is a schematic structural diagram of a device for determining a detection result according to an embodiment of the present application.
Detailed Description
The following describes in detail a method, an apparatus, and a system for determining a detection result provided in an embodiment of the present application with reference to the drawings.
The term "and/or" herein is merely an association describing an associated object, meaning that three relationships may exist, e.g., a and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone.
The terms "first" and "second" and the like in the description and drawings of the present application are used for distinguishing different objects or for distinguishing different processes for the same object, and are not used for describing a specific order of the objects.
Furthermore, the terms "including" and "having," and any variations thereof, as referred to in the description of the present application, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements but may alternatively include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be noted that in the embodiments of the present application, words such as "exemplary" or "for example" are used to mean serving as examples, illustrations or descriptions. Any embodiment or design described herein as "exemplary" or "e.g.," is not necessarily to be construed as preferred or advantageous over other embodiments or designs. Rather, use of the word "exemplary" or "such as" is intended to present concepts related in a concrete fashion.
In the description of the present application, the meaning of "a plurality" means two or more unless otherwise specified.
At present, in the industrial production field, the appearance of an object to be detected is mostly detected by adopting a machine vision detection mode. The machine vision detection mode uses a machine to replace human eyes for measurement and judgment, and solves the problem of low efficiency in manual detection. The existing machine vision detection mode generally includes that an acquisition device acquires an image signal of an object to be detected and transmits the image signal to an image acquisition card, and the image acquisition card can convert the acquired image signal into a digital signal and then transmit the digital signal to a special server. Finally, the special server can process the received digital signals according to preset rules to determine the appearance detection result of the object to be detected.
Although the existing machine vision detection mode is higher in efficiency than manual detection, due to the limitation of early-stage algorithm development, the false detection rate is higher, and particularly for some enterprises with higher quality requirements, the machine vision detection mode cannot completely meet the requirements of the enterprises. Therefore, the detection accuracy of the existing machine vision detection method still needs to be improved.
In view of the above problems in the prior art, an embodiment of the present application provides a method for determining a detection result. The first detection result and the second detection result can be mutually verified, whether the target object has defects is finally determined, and the accuracy of the detection result determined by the method is higher compared with that determined by the existing detection mode.
The method for determining the detection result provided by the embodiment of the present application may be applied to a system architecture schematic diagram shown in fig. 1, where the system architecture includes a first server 01 and a Mobile Edge Computing (MEC) server 02.
The first server 01 is configured to determine a first detection result for the target image according to a preset rule.
The first server 01 is further configured to obtain a second detection result for the target image from the MEC server 02, and determine whether the target object has a defect according to the first detection result and the second detection result.
And the MEC server 02 is used for determining a third detection result of the image to be detected according to the preset training model.
The MEC server 02 is further configured to determine, when receiving the first request sent by the first server 01, a second detection result of the target image from the third detection result of the image to be detected according to the identification number of the target image in the first request, and send the second detection result to the first server 01.
The first server 01 may be an enterprise local server and the MEC server 02 may be a server deployed at an enterprise edge node.
It is to be understood that, in practical applications, the system for determining the detection result may further include other devices, and only the devices that may be used in the embodiments of the present application are described herein, and do not form a specific limitation on the system for determining the detection result.
Exemplarily, as shown in fig. 2, the system for determining the detection result may further include an acquisition device 03. The acquisition device 03 is connected to the first server 01 and the MEC server 02, respectively.
The acquisition device 03 may be an industrial camera or other device for acquiring an image to be detected (including a target image). The collection equipment 03 can send the collected image to be detected to the MEC server 02 through a communication module (for example, a 5G communication module) after collecting the image to be detected, and can transmit the collected image to be detected to the first server 01 through an image collection module.
The following describes a method for determining a detection result provided in an embodiment of the present application with reference to the system for determining a detection result shown in fig. 1 or fig. 2.
Referring to fig. 3, a method for determining a detection result provided in the embodiment of the present application may include S101 to S104:
s101, the first server acquires a target image.
The target image is an image of a to-be-detected region including a target object, and the to-be-detected region may be a complete appearance image of the target object or a partial appearance image of the target object.
Generally, the first server may obtain the target image from the acquisition device.
S102, the first server determines a first detection result of the target image according to a preset rule.
In a possible implementation manner, the first server may determine target information of the target image, and then determine a first detection result for the target image according to the target information. The first detection result may include a target image being normal or a target image being abnormal.
The target information may include one or more of pixel distribution information, brightness, and color, for example, the first server may determine brightness of the target image according to an image processing method, and when the brightness of the target image is within a range of preset values, the first server may determine that a first detection result of the target image is that the target image is normal, otherwise, the first server may determine that the first detection result of the target image is that the target image is abnormal.
The preset value may be a range of brightness values of the image determined in advance by human.
For example, the first server may determine a pixel value of each pixel point of the target image according to an image processing method, then determine a pixel distribution curve of the target image, analyze whether the pixel distribution curve of the target image meets a preset condition, when it is determined that the pixel distribution curve of the target image meets the preset condition, the first server may determine that a first detection result of the target image is that the target image is normal, otherwise, the first server may determine that the first detection result of the target image is that the target image is abnormal.
It is understood that, in practical applications, the first detection result of the target image may also be determined by combining target information such as pixel distribution information, brightness, and color. For example, the first server may determine the brightness of the target image according to an image processing method, and determine a pixel distribution curve of the target image. And then, when the first server determines that the brightness of the target image is within the range of the preset value and the pixel distribution curve of the target image meets the preset condition, the first server can determine that the first detection result of the target image is that the target image is normal, otherwise, the first server can determine that the first detection result of the target image is that the target image is abnormal.
S103, the first server acquires a second detection result of the target image from the MEC server.
Optionally, the target image may include an identification number. For example, the identification number may be a serial number generated by the acquisition device for the target image when the target image is acquired, the serial number being used to uniquely indicate the target image.
In one possible implementation manner, the first server may send a first request including an identification number of the target image to the MEC server, where the first request is used to request to obtain a second detection result for the target image. Thereafter, the first server may receive a second detection result sent by the MEC server.
S104, the first server determines whether the target object has defects according to the first detection result and the second detection result.
After the first server obtains the second detection result sent by the MEC server, it may determine whether the target object has a defect according to the first detection result for the target image determined by the first server and the obtained second detection result.
In a possible implementation manner, when the first server determines that the first detection result is the target image abnormality, or the obtained second detection result is the target image abnormality, the first server may determine that the target object has a defect. When the first server determines that the first detection result is that the target image is normal and the obtained second detection result is that the target image is normal, the first server can determine that the target object has no defects.
Optionally, as shown in fig. 4, after step S104, the method for determining the detection result provided in the embodiment of the present application may further include step S105:
and S105, when the first server determines that the target object has defects, storing the target image into a defect product library.
In a possible implementation manner, when the first server determines that the target object has a defect, the first server may label the target image, for example, may label a defect type of the target image, and then store the labeled target image in a defect product library. The data of the defective product library can be synchronously updated to the MEC server as new training data of the training model of the MEC server. For example, when the defect product library in the first server is updated with the labeled first image, the MEC server may iteratively train the training model using the first image as new training data.
According to the method for determining the detection result, after the first server determines the first detection result of the target image, the second detection result of the target image can be obtained from the MEC server, and finally whether the target object has the defect or not is determined according to the two detection results. The first detection result and the second detection result are detected by different detection modes, so that the first detection result and the second detection result can mutually verify whether the detection results are correct, and the accuracy of the detection results determined by the detection result determining method provided by the application is higher than that of the detection results determined by the existing detection modes.
In summary, as shown in fig. 5, step S102 in fig. 3 may be replaced with steps S1021 to S1022:
s1021, the first server determines the target information of the target image.
S1022, the first server determines a first detection result of the target image according to the target information.
As shown in fig. 6, step S103 in fig. 3 may be replaced with S1031-S1032:
s1031, the first server sends the first request to the MEC server.
S1032, the first server receives the second detection result sent by the MEC server.
As shown in fig. 7, step S104 in fig. 3 may be replaced with S1041:
s1041, when the first detection result is that the target image is abnormal or the second detection result is that the target image is abnormal, the first server determines that the target object has defects; and when the first detection result is that the target image is normal and the second detection result is that the target image is normal, the first server determines that the target object has no defects.
Referring to fig. 8, an embodiment of the present application further provides a method for determining a detection result, which may be applied to the MEC server 02 in the system for determining a detection result shown in fig. 1 or fig. 2, where the method includes: S201-S205:
s201, the MEC server acquires an image to be detected.
The image to be detected is an image of a region to be detected containing an object to be detected, and the image to be detected comprises a target image.
S202, the MEC server determines a third detection result of the image to be detected according to a preset training model.
The preset training model can be any one of a neural network model, a convolutional neural network model and a deep neural network model. For example, the MEC server may classify the images with defects according to the defect types, and then train the deep neural network model according to the images with defects, so as to obtain a preset training model. The input of the preset training model can be an image to be detected, and the output of the preset training model is a third detection result of the image to be detected, wherein the third detection result comprises that the image to be detected is normal or the image to be detected is abnormal.
In addition, the MEC server can acquire data of a defective product library of the first server in real time and is used for updating the iterative training preset training model.
S203, the MEC server receives a first request sent by the first server.
The first request is used for requesting to acquire a second detection result of the target image, and the first request comprises an identification number of the target image.
And S204, the MEC server determines a second detection result of the target image from a third detection result of the image to be detected according to the identification number.
S205, the MEC server sends the second detection result to the first server.
It can be seen that the MEC server determines a third detection result for the image to be detected according to the preset training model, which is different from the visual detection manner of the first server. In addition, the MEC server may determine a second detection result for the target image from the third detection results that have been detected according to the identification number of the target image sent by the first server and send the second detection result to the first server. In this way, the first server can determine whether the target object has defects by combining the second detection result and the first detection result determined by the first server. Therefore, compared with the existing detection mode, the method for determining the detection result has higher accuracy.
For example, an embodiment of the present application further provides a data interaction schematic diagram of a system architecture shown in fig. 1 or fig. 2, where the method for determining a detection result provided by the foregoing embodiment is applied, as shown in fig. 9, the data interaction schematic diagram may include S1-S8, and specific data interaction content description may refer to description in the foregoing embodiment, and is not repeated here.
It should be noted that, in the embodiment of the present application, the sequence of S1 and S2 and S3 and S4 in fig. 9 is not limited, and S5 may be after S4.
Fig. 10 is a schematic diagram illustrating a possible structure of the first server 01 in the detection result determination system according to the above embodiment. The first server 01 includes: an acquisition module 11 and a determination module 12.
The obtaining module 11 executes S101 and S103 in the above method embodiment, and the determining module 12 executes S102 and S104 in the above method embodiment.
Specifically, the acquiring module 11 is configured to acquire a target image; the target image is an image of a region to be detected including the target object.
A determining module 12, configured to determine, according to a preset rule, a first detection result for the target image acquired by the acquiring module 11; the first detection result comprises that the target image is normal or abnormal.
The obtaining module 11 is further configured to obtain a second detection result for the target image from the MEC server; the second detection result includes that the target image is normal or the target image is abnormal.
The determining module 12 is further configured to determine whether the target object has a defect according to the first detection result obtained by the obtaining module 11 and the second detection result obtained by the obtaining module 11.
Optionally, the determining module 12 is specifically configured to: when the first detection result is that the target image is abnormal or the second detection result is that the target image is abnormal, determining that the target object has defects; and when the first detection result is that the target image is normal and the second detection result is that the target image is normal, determining that the target object has no defects.
Optionally, the determining module 12 is further specifically configured to: determining target information of a target image; the target information includes one or more of pixel distribution information, brightness, and color; and determining a first detection result for the target image according to the target information.
Optionally, the target image includes an identification number; the obtaining module 11 is specifically configured to: sending a first request to an MEC server; and receiving a second detection result sent by the MEC server. The first request is used for requesting to acquire a second detection result of the target image; the first request includes an identification number.
Optionally, the first server 01 provided in the embodiment of the present application further includes a storage module, where the storage module is configured to store a defective product library or a program code of the first server 01. The determination module 12 is further configured to store the target image in a defect product library in the storage module when the target object is determined to have a defect.
Fig. 11 shows a schematic diagram of a possible structure of the MEC server 02 in the determination system of the detection result according to the above embodiment. The MEC server 02 includes: the device comprises an acquisition module 21, a determination module 22, a receiving module 23 and a sending module 24.
The obtaining module 21 executes S201 in the above method embodiment, the determining module 22 executes S202 and S204 in the above method embodiment, the receiving module 23 executes S203 in the above method embodiment, and the determining module 24 executes S205 in the above method embodiment.
In particular, the acquisition module 21 is used for acquiring an image to be detected. The image to be detected is an image of a region to be detected containing an object to be detected; the image to be detected comprises a target image.
A determining module 22, configured to determine, according to a preset training model, a third detection result for the image to be detected acquired by the acquiring module 21; the third detection result comprises that the image to be detected is normal or abnormal;
a receiving module 23, configured to receive a first request sent by a first server; the first request is used for requesting to acquire a second detection result of the target image; the first request includes an identification number of the target image.
The determining module 22 is further configured to determine a second detection result of the target image from a third detection result of the image to be detected according to the identification number received by the receiving module 23; the second detection result includes that the target image is normal or the target image is abnormal.
A sending module 24, configured to send the second detection result determined by the determining module 22 to the first server.
Optionally, the MEC server 02 further comprises a storage module. The storage module is used for program codes and the like of the MEC server 02.
As shown in fig. 12, an embodiment of the present application further provides a device for determining a detection result, including a memory 41, a processor 42, a bus 43, and a communication interface 44; the memory 41 is used for storing computer execution instructions, and the processor 42 is connected with the memory 41 through a bus 43; when the detection result determination means is operated, the processor 42 executes computer-executable instructions stored in the memory 41 to cause the detection result determination means to perform the determination method of the detection result applied to the first server or the determination method of the detection result of the MEC server as provided in the above embodiments.
In particular implementations, processor 42(42-1 and 42-2) may include one or more Central Processing Units (CPUs), such as CPU0 and CPU1 shown in FIG. 12, as one example. And as an example, the means for determining the detection result may comprise a plurality of processors 42, such as processor 42-1 and processor 42-2 shown in fig. 12. Each of the processors 42 may be a single-Core Processor (CPU) or a multi-Core Processor (CPU). Processor 42 may refer herein to one or more devices, circuits, and/or processing cores that process data (e.g., computer program instructions).
The memory 41 may be, but is not limited to, a read-only memory 41 (ROM) or other type of static storage device that can store static information and instructions, a Random Access Memory (RAM) or other type of dynamic storage device that can store information and instructions, an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compact disc, laser disc, optical disc, digital versatile disc, blu-ray disc, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. The memory 41 may be self-contained and coupled to the processor 42 via a bus 43. The memory 41 may also be integrated with the processor 42.
In a specific implementation, the memory 41 is used for storing data in the present application and computer-executable instructions corresponding to software programs for executing the present application. The processor 42 may detect various functions of the device by running or executing software programs stored in the memory 41, and calling up data stored in the memory 41.
The communication interface 44 is any device, such as a transceiver, for communicating with other devices or communication networks, such as a control system, a Radio Access Network (RAN), a Wireless Local Area Network (WLAN), and the like. The communication interface 44 may include a receiving unit implementing a receiving function and a transmitting unit implementing a transmitting function.
The bus 43 may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an extended ISA (enhanced industry standard architecture) bus, or the like. The bus 43 may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 12, but this is not intended to represent only one bus or type of bus.
As an example, in connection with fig. 11, the function implemented by the receiving module in the MEC server is the same as the function implemented by the receiving unit in fig. 12, and the function implemented by the storing module in the MEC server is the same as the function implemented by the storage in fig. 12.
For the explanation of the related contents in this embodiment, reference may be made to the above method embodiments, which are not described herein again.
Through the above description of the embodiments, it is clear to those skilled in the art that, for convenience and simplicity of description, the foregoing division of the functional modules is merely used as an example, and in practical applications, the above function distribution may be completed by different functional modules according to needs, that is, the internal structure of the device may be divided into different functional modules to complete all or part of the above described functions. For the specific working processes of the system, the apparatus and the unit described above, reference may be made to the corresponding processes in the foregoing method embodiments, and details are not described here again.
An embodiment of the present application further provides a computer-readable storage medium, where instructions are stored in the computer-readable storage medium, and when the instructions are executed by a computer, the computer is enabled to execute the method for determining the detection result applied to the first server or the method for determining the detection result applied to the MEC server, which are provided in the foregoing embodiments.
The computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a RAM, a ROM, an erasable programmable read-only memory (EPROM), a register, a hard disk, an optical fiber, a CD-ROM, an optical storage device, a magnetic storage device, any suitable combination of the foregoing, or any other form of computer readable storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. Of course, the storage medium may also be integral to the processor. The processor and the storage medium may reside in an Application Specific Integrated Circuit (ASIC). In embodiments of the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The above description is only an embodiment of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions within the technical scope of the present disclosure should be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (11)
1. A method for determining a detection result is applied to a first server, and is characterized by comprising the following steps:
acquiring a target image; the target image is an image of a to-be-detected area containing a target object;
determining a first detection result for the target image according to a preset rule; the first detection result comprises that the target image is normal or abnormal;
acquiring a second detection result for the target image from a Mobile Edge Computing (MEC) server; the second detection result comprises that the target image is normal or abnormal;
and determining whether the target object has defects according to the first detection result and the second detection result.
2. The method for determining the detection result according to claim 1, wherein the determining whether the target object has the defect according to the first detection result and the second detection result comprises:
when the first detection result is that the target image is abnormal or the second detection result is that the target image is abnormal, determining that the target object has defects;
and when the first detection result is that the target image is normal and the second detection result is that the target image is normal, determining that the target object has no defects.
3. The method for determining the detection result according to claim 1 or 2, wherein the determining the first detection result for the target image according to a preset rule comprises:
determining target information of the target image; the target information includes one or more of pixel distribution information, brightness, and color;
and determining a first detection result of the target image according to the target information.
4. The method for determining the detection result according to claim 3, wherein the target image includes an identification number; the obtaining, from the MEC server, a second detection result for the target image includes:
sending a first request to the MEC server; the first request is used for requesting to acquire a second detection result of the target image; the first request includes the identification number;
and receiving the second detection result sent by the MEC server.
5. The method for determining the detection result according to claim 4, further comprising:
and when the target object is determined to have defects, storing the target image into a defective product library.
6. A method for determining a detection result is applied to an MEC server, and is characterized by comprising the following steps:
acquiring an image to be detected; the image to be detected is an image of a region to be detected containing an object to be detected; the image to be detected comprises a target image;
determining a third detection result of the image to be detected according to a preset training model; the third detection result comprises that the image to be detected is normal or the image to be detected is abnormal;
receiving a first request sent by a first server; the first request is used for requesting to acquire a second detection result of the target image; the first request includes an identification number of the target image;
determining a second detection result of the target image from a third detection result of the image to be detected according to the identification number; the second detection result comprises that the target image is normal or abnormal;
and sending the second detection result to the first server.
7. A first server, comprising:
the acquisition module is used for acquiring a target image; the target image is an image of a to-be-detected area containing a target object;
the determining module is used for determining a first detection result of the target image acquired by the acquiring module according to a preset rule; the first detection result comprises that the target image is normal or abnormal;
the acquisition module is further used for acquiring a second detection result of the target image from the MEC server; the second detection result comprises that the target image is normal or abnormal;
the determining module is further configured to determine whether the target object has a defect according to the first detection result obtained by the obtaining module and the second detection result obtained by the obtaining module.
8. An MEC server, comprising:
the acquisition module is used for acquiring an image to be detected; the image to be detected is an image of a region to be detected containing an object to be detected; the image to be detected comprises a target image;
the determining module is used for determining a third detection result of the image to be detected acquired by the acquiring module according to a preset training model; the third detection result comprises that the image to be detected is normal or the image to be detected is abnormal;
the receiving module is used for receiving a first request sent by a first server; the first request is used for requesting to acquire a second detection result of the target image; the first request includes an identification number of the target image;
the determining module is further configured to determine a second detection result of the target image from a third detection result of the image to be detected according to the identification number received by the receiving module; the second detection result comprises that the target image is normal or abnormal;
a sending module, configured to send the second detection result determined by the determining module to the first server.
9. The device for determining the detection result is characterized by comprising a memory, a processor, a bus and a communication interface; the memory is used for storing computer execution instructions, and the processor is connected with the memory through the bus;
when the determination device of the detection result is operated, the processor executes the computer-executable instructions stored in the memory to cause the determination device of the detection result to execute the determination method of the detection result according to any one of claims 1 to 5 or execute the determination method of the detection result according to claim 6.
10. A computer-readable storage medium having stored therein instructions, which when executed by a computer, cause the computer to execute the method for determining a detection result according to any one of claims 1 to 5 or the method for determining a detection result according to claim 6.
11. A detection result determination system comprising the first server of claim 7 and the MEC server of claim 8.
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