CN113298760A - Quality detection method, device and system based on deep learning - Google Patents
Quality detection method, device and system based on deep learning Download PDFInfo
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Abstract
The application discloses a quality detection method based on deep learning, which comprises the following steps: acquiring image information of a product to be detected, wherein the image information is appearance imaging of the product to be detected; transmitting the image information to a preset quality classification model, wherein the preset quality classification model is obtained through deep learning training and is used for carrying out quality detection and classification on products to be detected; and acquiring the quality detection result of the preset quality classification model output aiming at the product to be detected. Based on the image and the quality detection model of the product to be detected, the quality of the product to be detected can be quickly identified, an accurate and quick product defect detection mode is realized, the automation of production quality detection is realized, the labor cost and the complex quality detection process are reduced, and the risk of unqualified product quality is reduced.
Description
Technical Field
The application relates to the field of quality detection, in particular to a quality detection method, device and system based on deep learning.
Background
The quality of the appearance of the product determines the first impression of the quality of the product, and therefore, quality inspection of the appearance of the product is one of important links of the quality inspection of the product.
The conventional appearance Quality inspection is based on the subjective judgment of QC (Quality Control) to determine whether a product has defects, the defect type of the product, the degree of the product defects, and the like. According to the subjective judgment of QC, the judgment basis can be different along with the fluctuation of subjective consciousness, defects with the same degree can be judged differently from front to back, can be judged differently at different places, can be judged differently due to different environmental factors, can be judged differently due to different fatigue degrees of QC personnel, and the like. Generally speaking, the external quality inspection depends on human subjective judgment, the inspection result is greatly influenced by human factors and environmental factors, and the quality inspection efficiency is low.
Disclosure of Invention
The present application is proposed to solve the above-mentioned technical problems. The embodiment of the application provides a quality detection method, a quality detection device, a quality detection system, a computer-readable storage medium and electronic equipment based on deep learning.
According to a first aspect of the present application, there is provided a quality detection method based on deep learning, including:
acquiring image information of a product to be detected, wherein the image information is appearance imaging of the product to be detected;
transmitting the image information to a preset quality classification model, wherein the preset quality classification model is obtained through deep learning training and is used for detecting and classifying the quality of a product to be detected;
and acquiring the quality detection result of the preset quality classification model output aiming at the product to be detected.
In some embodiments, the method further comprises:
and training a preset sample based on a deep learning algorithm to obtain the preset quality classification model.
In some embodiments, the step of training a preset sample based on a deep learning algorithm to obtain the preset quality classification model includes:
acquiring image information of the preset sample, wherein the preset sample is a product with a preset quality defect;
and performing model training through a deep learning algorithm based on the labeling information of the image information to obtain the preset quality classification model, wherein the labeling information comprises defect types and/or defect positions.
In some embodiments, the method further comprises:
and acquiring a mark symbol in the image information to determine the mark information of the image information, wherein the mark symbol is marked in a manual input mode.
In some embodiments, the method further comprises:
determining defect information on the image information aiming at the image information of any preset sample;
and marking a defect symbol based on the defect information to obtain marking information of the image information.
According to a second aspect of the present application, there is provided a quality detection apparatus based on deep learning, the apparatus including:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring image information of a product to be detected, and the image information is appearance imaging of the product to be detected;
the transmission module is used for transmitting the image information to a preset quality classification model, and the preset quality classification model is obtained through deep learning training and is used for detecting and classifying the quality of the product to be detected;
and the identification module is used for acquiring the quality detection result of the preset quality classification model output aiming at the product to be detected.
In some embodiments, the apparatus further comprises:
and the training module is used for training a preset sample based on a deep learning algorithm to obtain the preset quality classification model.
In some embodiments, the apparatus further comprises:
and the second acquisition module is used for acquiring the annotation symbol in the image information so as to determine the annotation information of the image information, wherein the annotation symbol is marked in a manual input mode.
In some embodiments, the apparatus further comprises:
the determining module is used for determining defect information on the image information aiming at the image information of any preset sample;
and the marking module is used for marking the defect symbol based on the defect information so as to obtain the marking information of the image information.
The device comprises an image acquisition module, a comparison module and a comparison module, wherein the image acquisition module is used for acquiring an image of a product to be detected, the image of the product to be detected comprises a preset label image, and the preset label is an identifier which is attached to the surface of the product to be detected and is used for representing the quality of the product;
the image identification module is used for identifying the identifier in the preset label image so as to obtain the quality data of the product to be detected;
and the data uploading module is used for uploading the quality data of the product to be detected to a preset quality management platform.
According to a third aspect of the present application, there is provided a quality detection system based on deep learning, comprising the quality detection device based on deep learning as the second aspect, a support bracket, a fixing platform of a product to be detected and an image acquisition device, wherein,
the deep learning based quality detection apparatus performs the method of the first aspect;
the supporting bracket is used for connecting the quality detection device based on deep learning;
the fixing platform of the product to be detected is fixedly connected with the supporting bracket and is used for placing the product to be detected when the image of the product to be detected is collected;
and the quality detection device based on deep learning is arranged in the image acquisition device and executes the method of the first aspect, or the quality detection device based on deep learning is connected with the image acquisition device and receives the image information of the product to be detected transmitted by the image acquisition device so as to execute the method of the first aspect.
According to a fourth aspect of the present application, there is provided a computer-readable storage medium storing a computer program for executing the deep learning based quality detection method according to the first aspect.
According to a fifth aspect of the present application, there is provided an electronic apparatus comprising:
a processor;
a memory for storing the processor-executable instructions;
the processor is configured to read the executable instructions from the memory and execute the instructions to implement the above-mentioned first aspect quality detection method based on deep learning.
The embodiment that this application provided, through a quality detection method, device and system based on degree of depth study, based on waiting to examine the image and the quality detection model of examining the product, can discern the quality of examining the product fast, realize an accurate quick product defect detection mode, realize the automation of production quality control, it is right to reduce cost of labor and loaded down with trivial details quality control process, reduces the unqualified risk of product quality.
Drawings
The above and other objects, features and advantages of the present application will become more apparent by describing in more detail embodiments of the present application with reference to the attached drawings. The accompanying drawings are included to provide a further understanding of the embodiments of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the principles of the application. In the drawings, like reference numbers generally represent like parts or steps.
Fig. 1 is a schematic flowchart of a quality detection method based on deep learning according to an exemplary embodiment of the present application.
Fig. 2 is a schematic structural diagram of a quality detection apparatus based on deep learning according to an exemplary embodiment of the present application.
Fig. 3 is a schematic structural diagram of a deep learning-based quality detection system according to an exemplary embodiment of the present application.
Fig. 4 is a block diagram of an electronic device provided in an exemplary embodiment of the present application.
Detailed Description
Hereinafter, example embodiments according to the present application will be described in detail with reference to the accompanying drawings. It should be understood that the described embodiments are only some embodiments of the present application and not all embodiments of the present application, and that the present application is not limited by the example embodiments described herein.
Exemplary method
Fig. 1 is a flowchart illustrating a deep learning-based quality detection method according to an exemplary embodiment of the present application. As shown in fig. 1, the quality detection method based on deep learning provided by the present invention includes the following steps:
The image information understanding of the product to be detected can be acquired by the image acquisition device, and can also be acquired by further carrying out denoising and image conversion after the image acquisition device finishes acquisition. And the image information can be a two-dimensional image of a certain appearance surface to be detected of the product to be detected or an appearance three-dimensional image of the product to be detected.
In this step, the image capturing device may be implemented, or the system or platform for quality detection connected to the image capturing device may be implemented, and the system or platform may acquire the image captured by the image capturing device.
And 102, transmitting the image information to a preset quality classification model, wherein the preset quality classification model is obtained through deep learning training and is used for performing quality detection and classification on the product to be detected.
In some embodiments, the obtained image information is transmitted to the preset quality classification model, for example, the image information may be sent to the preset quality classification model through a wired network transmission method, or may be sent to the preset quality classification model through a wireless network transmission method (for example, wifi, bluetooth, zigbee, etc.).
In other embodiments, a preset quality classification model may be provided in the image acquisition apparatus according to the embodiments of the present invention, so as to reduce resource consumption and time cost of data transmission.
The preset quality classification model can be obtained by training a preset sample based on a deep learning algorithm. For example, a large number of preset samples (for example, products with product quality defects) may be prepared, then image information of each preset sample is acquired, the defects in the images are labeled (for example, the defect types and the defect positions are labeled), and based on the labeled information of the image information, model training is performed through a deep learning algorithm to obtain a preset quality classification model. The labeling mode can be manual labeling or automatic labeling by adopting a computer program: if the annotation is manual annotation, the annotation symbol in the image information can be obtained to determine the annotation information of the image information, the annotation symbol is marked by a manual input method, for example, an annotation person can mark the defect type and position in a marking system in a clicking or inputting manner, the markable information can be obtained by obtaining the information clicked or input by the annotation person, or the defect information (for example, the defect type or the defect position) on the image information can be determined by a computer program (for example, image recognition software, positioning software and the like) aiming at the image information of any preset sample, and then the defect type or the defect position is matched with the preset defect type to obtain the defect type of the preset sample, and the defect position in the figure can be determined by the positioning software, and then the physical position conversion is carried out to obtain the defect position of the preset sample, so that the defect type and/or the defect position of the preset sample can be determined as the annotation information of the image information And (4) information.
And 103, acquiring a preset quality classification model and outputting a quality detection result aiming at the product to be detected.
After detection, the detection result of the product to be detected can be output on a display platform or in a mode of sending visual information to inspectors.
In some embodiments, quality labels may also be printed based on the quality test results and automatically affixed to the corresponding product. The quality label can be, for example, a print that can be applied and that identifies at least the type of defect and the location of the defect in the product.
Through a quality detection method, device and system based on deep learning, the quality of a product to be detected can be quickly identified based on an image and a quality detection model of the product to be detected, an accurate and quick product defect detection mode is realized, the automation of production quality inspection is realized, the labor cost and the complex quality inspection process are reduced, and the risk of unqualified product quality is reduced.
Exemplary devices
Fig. 2 is a schematic structural diagram of a quality detection apparatus based on deep learning according to an embodiment of the present application. The apparatus in this embodiment may include: a first acquisition module 21, a transmission module 22 and an identification module 23.
The first obtaining module 21 is used for obtaining image information of a product to be detected, wherein the image information is appearance imaging of the product to be detected.
The transmission module 22 is used for transmitting the image information to a preset quality classification model, and the preset quality classification model is used for detecting quality detection and classification of products to be detected.
The recognition module 23 is configured to obtain a quality detection result of the preset quality classification model output for the product to be detected.
The quality detection device based on deep learning can further comprise:
the training module (not shown in the figure) is used for training a preset sample based on a deep learning algorithm to obtain the preset quality classification model.
And/or the presence of a gas in the gas,
the second obtaining module (not shown in the figure) is configured to obtain a label symbol in the image information to determine the label information of the image information, where the label symbol is marked by a manual input method.
And/or the presence of a gas in the gas,
the determining module (not shown in the figure) is used for determining defect information on the image information aiming at the image information of any preset sample; and the labeling module (not shown in the figure) is used for labeling the defect symbol based on the defect information to obtain labeling information of the image information.
Through a quality detection device based on degree of depth study, based on the image and the quality detection model of waiting to examine the product, can discern the quality of waiting to examine the product fast, realize an accurate quick product defect detection mode, realize the automation of production quality control, it is right to reduce cost of labor and loaded down with trivial details quality control process, reduces the unqualified risk of product quality.
Exemplary System
Fig. 3 is a schematic structural diagram of a quality detection system based on deep learning according to an exemplary embodiment of the present application. The quality detection system based on deep learning can comprise the quality detection device based on deep learning, a supporting bracket 41, a fixing platform 42 of a product to be detected and an image acquisition device 43 shown in FIG. 2, wherein the quality detection based on deep learning is used for executing the method shown in FIG. 1; the support bracket 41 may be used to support the deep learning based quality detection apparatus shown in fig. 2; the product to be inspected fixing platform 42 is fixedly connected with the supporting bracket 41 for placing the product to be inspected when acquiring the image of the product to be inspected, and the quality detection device based on deep learning is arranged in the image acquisition device 43 for executing the method as shown in fig. 1, or the quality detection device based on deep learning is connected with the image acquisition device 43 for receiving the image information of the product to be inspected transmitted by the image acquisition device for executing the method as shown in fig. 1
In some embodiments, the quality detection system based on deep learning may further include a light source for providing illumination during image acquisition to improve the quality of the image and thereby improve the image recognition accuracy.
Through a quality detection system based on degree of depth study, based on the image and the quality detection model of waiting to examine the product, can discern the quality of waiting to examine the product fast, realize an accurate quick product defect detection mode, realize the automation of production quality control, it is right to reduce cost of labor and loaded down with trivial details quality control process, reduces the unqualified risk of product quality.
Exemplary electronic device
Next, an electronic apparatus according to an embodiment of the present application is described with reference to fig. 4. FIG. 4 illustrates a block diagram of an electronic device in accordance with an embodiment of the present application.
As shown in fig. 4, the electronic device 11 includes one or more processors 111 and memory 112.
The processor 111 may be a Central Processing Unit (CPU) or other form of processing unit having data processing capabilities and/or instruction execution capabilities, and may control other components in the electronic device 11 to perform desired functions.
Memory 112 may include one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. The volatile memory may include, for example, Random Access Memory (RAM), cache memory (cache), and/or the like. The non-volatile memory may include, for example, Read Only Memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium and executed by processor 111 to implement a deep learning based quality detection method of the various embodiments of the present application described above and/or other desired functionality. Various contents such as an input signal, a signal component, a noise component, etc. may also be stored in the computer-readable storage medium.
In one example, the electronic device 11 may further include: an input device 113 and an output device 114, which are interconnected by a bus system and/or other form of connection mechanism (not shown).
For example, when the electronic device is a first device or a second device, the input device 113 may be a microphone or a microphone array as described above for capturing an input signal of a sound source. When the electronic device is a stand-alone device, the input means 113 may be a communication network connector for receiving the acquired input signals from the first device and the second device.
The input device 113 may also include, for example, a keyboard, a mouse, and the like.
The output device 114 may output various information including the determined distance information, direction information, and the like to the outside. The output devices 114 may include, for example, a display, speakers, a printer, and a communication network and remote output devices connected thereto, among others.
Of course, for the sake of simplicity, only some of the components of the electronic device 11 relevant to the present application are shown in fig. 4, and components such as buses, input/output interfaces, and the like are omitted. In addition, the electronic device 11 may include any other suitable components, depending on the particular application.
Exemplary computer program product and computer-readable storage Medium
In addition to the above-described methods and apparatus, embodiments of the present application may also be a computer program product comprising computer program instructions that, when executed by a processor, cause the processor to perform the steps in a deep learning based quality detection method according to various embodiments of the present application described in the "exemplary methods" section of this specification above.
The computer program product may be written with program code for performing the operations of embodiments of the present application in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the first user computing device, partly on the first user device, as a stand-alone software package, partly on the first user computing device and partly on a remote computing device, or entirely on the remote computing device or server.
Furthermore, embodiments of the present application may also be a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, cause the processor to perform the steps in a deep learning based quality detection method according to various embodiments of the present application described in the "exemplary methods" section above in this specification.
The computer-readable storage medium may take any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may include, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The foregoing describes the general principles of the present application in conjunction with specific embodiments, however, it is noted that the advantages, effects, etc. mentioned in the present application are merely examples and are not limiting, and they should not be considered essential to the various embodiments of the present application. Furthermore, the foregoing disclosure of specific details is for the purpose of illustration and description and is not intended to be limiting, since the foregoing disclosure is not intended to be exhaustive or to limit the disclosure to the precise details disclosed.
The block diagrams of devices, apparatuses, systems referred to in this application are only given as illustrative examples and are not intended to require or imply that the connections, arrangements, configurations, etc. must be made in the manner shown in the block diagrams. These devices, apparatuses, devices, systems may be connected, arranged, configured in any manner, as will be appreciated by those skilled in the art. Words such as "including," "comprising," "having," and the like are open-ended words that mean "including, but not limited to," and are used interchangeably therewith. The words "or" and "as used herein mean, and are used interchangeably with, the word" and/or, "unless the context clearly dictates otherwise. The word "such as" is used herein to mean, and is used interchangeably with, the phrase "such as but not limited to".
It should also be noted that in the devices, apparatuses, and methods of the present application, the components or steps may be decomposed and/or recombined. These decompositions and/or recombinations are to be considered as equivalents of the present application.
The previous description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects without departing from the scope of the application. Thus, the present application is not intended to be limited to the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
The foregoing description has been presented for purposes of illustration and description. Furthermore, the description is not intended to limit embodiments of the application to the form disclosed herein. While a number of example aspects and embodiments have been discussed above, those of skill in the art will recognize certain variations, modifications, alterations, additions and sub-combinations thereof.
Claims (10)
1. A quality detection method based on deep learning is characterized by comprising the following steps:
acquiring image information of a product to be detected, wherein the image information is appearance imaging of the product to be detected;
transmitting the image information to a preset quality classification model, wherein the preset quality classification model is obtained through deep learning training and is used for carrying out quality detection and classification on products to be detected;
and acquiring the quality detection result of the preset quality classification model output aiming at the product to be detected.
2. The method of claim 1, further comprising:
and training a preset sample based on a deep learning algorithm to obtain the preset quality classification model.
3. The method according to claim 2, wherein the step of training a preset sample based on a deep learning algorithm to obtain the preset quality classification model comprises:
acquiring image information of the preset sample, wherein the preset sample is a product with a preset quality defect;
and performing model training through a deep learning algorithm based on the labeling information of the image information to obtain the preset quality classification model, wherein the labeling information comprises defect types and/or defect positions.
4. The method of claim 3, further comprising:
and acquiring a mark symbol in the image information to determine the mark information of the image information, wherein the mark symbol is marked in a manual input mode.
5. The method of claim 3, further comprising:
determining defect information on the image information aiming at the image information of any preset sample;
and marking a defect symbol based on the defect information to obtain marking information of the image information.
6. An apparatus for quality detection based on deep learning, the apparatus comprising:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring image information of a product to be detected, and the image information is appearance imaging of the product to be detected;
the transmission module is used for transmitting the image information to a preset quality classification model, and the preset quality classification model is used for detecting and classifying the quality of the product to be detected;
and the identification module is used for acquiring the quality detection result of the preset quality classification model output aiming at the product to be detected.
7. The apparatus of claim 6, further comprising:
and the training module is used for training a preset sample based on a deep learning algorithm to obtain the preset quality classification model.
8. The apparatus of claim 1, further comprising:
and the second acquisition module is used for acquiring the annotation symbol in the image information so as to determine the annotation information of the image information, wherein the annotation symbol is marked in a manual input mode.
9. The apparatus of claim 1, further comprising:
the determining module is used for determining defect information on the image information aiming at the image information of any preset sample;
and the marking module is used for marking the defect symbol based on the defect information so as to obtain the marking information of the image information.
10. A quality inspection system based on deep learning, which comprises the quality inspection apparatus based on deep learning of claims 6 to 9, a support frame, a fixing platform of a product to be inspected and an image acquisition apparatus, wherein,
the deep learning based quality detection apparatus performing the method of claims 1-5;
the supporting bracket is used for connecting the quality detection device based on deep learning;
the fixing platform of the product to be detected is fixedly connected with the supporting bracket and is used for placing the product to be detected when the image of the product to be detected is collected;
and the quality detection device based on deep learning is arranged in the image acquisition device and executes the method of the claims 1-5, or the quality detection device based on deep learning is connected with the image acquisition device and receives the image information of the product to be detected transmitted by the image acquisition device so as to execute the method of the claims 1-5.
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