CN108564104A - Product defects detection method, device, system, server and storage medium - Google Patents
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Abstract
本发明提出一种产品缺陷检测方法、装置、系统、服务器及计算机可读存储介质，其中产品缺陷检测方法包括：获取产品的图像数据；将所述图像数据转化为分类请求，根据在多个服务器上的分类预测模型的部署情况确定执行服务器，并将所述分类请求发送至所述执行服务器，以通过所述执行服务器上的分类预测模型给出缺陷类别的预测结果。本发明提供的实施例可随业务发展迭代模型，使模型能够适应生产环境的最新需求，在分类精度、可扩展性、规范化等方面为工业生产线带来显著的提升，且并行处理进一步提升了效率。
The present invention proposes a product defect detection method, device, system, server and computer-readable storage medium, wherein the product defect detection method includes: obtaining image data of the product; converting the image data into a classification request, according to the Deployment of the classification prediction model on the execution server is determined, and the classification request is sent to the execution server, so as to give the prediction result of the defect category through the classification prediction model on the execution server. The embodiment provided by the present invention can iterate the model along with business development, so that the model can adapt to the latest needs of the production environment, and bring significant improvements to industrial production lines in terms of classification accuracy, scalability, standardization, etc., and parallel processing further improves efficiency .
Description
Technical Field
The present invention relates to the field of information technologies, and in particular, to a method, an apparatus, a system, a server, and a computer-readable storage medium for detecting product defects.
Background
At present, in a quality inspection link in many production industries, defect detection is mainly performed on a product surface image in a visual mode. In the paper industry, for example, the quality of paper is measured primarily based on images of the surface of the paper. On the production line of the paper industry, the quality inspection is mostly assisted by a manual inspection or semi-automatic optical instrument, so that the efficiency is low, and misjudgment is easy to occur; in addition, industrial data generated in such a way is not easy to store, manage and secondarily mine for reuse. Under the condition of manual inspection, a service expert is required to perform inspection on a production field, and after defects are found, the defects are manually recorded and then are subjected to subsequent processing. The method has the advantages of low efficiency, easy judgment omission and misjudgment, difficult secondary utilization and excavation of data, severe production environment and adverse influence on the health and safety of personnel. The latter quality inspection method is mostly a quality inspection system based on a traditional expert system or characteristic engineering, characteristics and judgment rules are solidified into a machine based on experience and are difficult to iterate along with the development of business, so that the detection accuracy of the system is lower and lower along with the development of a production process, and even the system is reduced to a completely unavailable state. In addition, the characteristics of the traditional quality inspection system are pre-solidified in hardware by a third-party supplier, and the upgrading process not only needs to carry out great modification on a production line, but also is expensive. The traditional quality inspection system has obvious defects in the aspects of safety, standardization, expandability and the like, and is not beneficial to the optimization and upgrading of a production line.
Disclosure of Invention
Embodiments of the present invention provide a method, an apparatus, a system, a server, and a computer-readable storage medium for detecting product defects, so as to at least solve one or more technical problems in the prior art.
In a first aspect, an embodiment of the present invention provides a product defect detection method, including: acquiring image data of a product; and converting the image data into a classification request, determining an execution server according to the deployment condition of classification prediction models on a plurality of servers, and sending the classification request to the execution server so as to give a prediction result of the defect category through the classification prediction models on the execution server.
With reference to the first aspect, in a first implementation manner of the first aspect, the determining an execution server according to a deployment situation of the classification prediction model on a plurality of servers includes: inquiring a preset server resource configuration management table, wherein the server resource configuration management table is used for recording the load state of each server with the classification prediction model; and comparing the load states of the servers with the classification prediction models, and determining the server with the lowest load as an execution server.
With reference to the first aspect and the first implementation manner of the first aspect, the present invention, in a second implementation manner of the first aspect, receives a prediction result of a defect class returned by the execution server; making corresponding defect processing operation according to the prediction result of the defect category, wherein the defect processing operation comprises the following steps: alarm, tag, log, and/or shutdown.
With reference to the first implementation manner of the first aspect, the making a corresponding defect handling operation according to the prediction result of the defect category includes: making corresponding defect processing operation according to the preset corresponding relation between the prediction result of the defect type and the defect processing operation; or, according to the preset grade of the prediction result of the defect type and the corresponding relation between the grade of the prediction result of the defect type and the defect processing operation, making the corresponding defect processing operation.
In a second aspect, an embodiment of the present invention provides a product defect detection method, including: receiving a classification request for image data of a product; classifying and calculating the classification request through a pre-trained classification prediction model to give a prediction result of the defect classification; the classification prediction model comprises a feature extraction model and a defect positioning classification model; the feature extraction model is used for extracting features of the image data in the classification request; the defect positioning classification model is used for providing a prediction result of a defect category according to the extracted features of the image data, and the prediction result comprises: whether the image in the classification request has a defect or not and the category and the position coordinates of the defect.
With reference to the second aspect, in a first implementation manner of the second aspect, before performing a classification calculation on the classification request, the method further includes: and preprocessing the image data in the classification request, wherein the preprocessing comprises image denoising, background removing, image compression and/or format conversion.
With reference to the second aspect and the first embodiment of the second aspect, the present invention, in a second embodiment of the second aspect, further includes: and pre-training according to historical labeling data of image data of the product to obtain the classification prediction model.
With reference to the third implementation manner of the second aspect, the present invention provides in a third implementation manner of the second aspect, wherein the feature extraction model includes a deep convolutional neural network; the defect localization classification model comprises RCNN, SSD or Mask RCNN.
With reference to the second aspect and the first implementation manner of the second aspect, in a fourth implementation manner of the second aspect, after the predicting result of the defect class is given, the method further includes: making corresponding defect processing operation according to the prediction result of the defect category, wherein the defect processing operation comprises the following steps: alarm, tag, log, and/or shutdown.
With reference to the fourth implementation manner of the second aspect, the making of the corresponding defect handling operation according to the prediction result of the defect category includes: making corresponding defect processing operation according to the preset corresponding relation between the prediction result of the defect type and the defect processing operation; or, according to the preset grade of the prediction result of the defect type and the corresponding relation between the grade of the prediction result of the defect type and the defect processing operation, making the corresponding defect processing operation.
In a third aspect, an embodiment of the present invention provides a product defect detection apparatus, including: the data acquisition module is used for acquiring image data of a product; and the load balancing module is used for converting the image data into a classification request, determining an execution server according to the deployment condition of the classification prediction models on the plurality of servers, and sending the classification request to the execution server so as to give a prediction result of the defect category through the classification prediction models on the execution server.
With reference to the third aspect, in a first implementation manner of the third aspect, the load balancing module is further configured to: inquiring a preset server resource configuration management table, wherein the server resource configuration management table is used for recording the load state of each server with the classification prediction model; and comparing the load states of the servers with the classification prediction models, and determining the server with the lowest load as an execution server.
In a fourth aspect, an embodiment of the present invention provides a product defect detecting apparatus, including: the data receiving module is used for receiving a classification request of image data of a product; the classification prediction model is used for performing classification calculation on the classification request through a pre-trained classification prediction model to give a prediction result of the defect type; the classification prediction model comprises a feature extraction model and a defect positioning classification model; the feature extraction model is used for extracting features of the image data in the classification request; the defect positioning classification model is used for providing a prediction result of a defect category according to the extracted features of the image data, and the prediction result comprises: whether the image in the classification request has a defect or not and the category and the position coordinates of the defect.
With reference to the fourth aspect, the present invention, in a first implementation manner of the fourth aspect, further includes a control module configured to: making corresponding defect processing operation according to the prediction result of the defect category, wherein the defect processing operation comprises the following steps: alarm, tag, log, and/or shutdown.
In one possible design, the product defect detecting apparatus includes a processor and a memory, the memory is used for storing a program supporting the product defect detecting apparatus to execute the product defect detecting method in the first aspect or the second aspect, and the processor is configured to execute the program stored in the memory.
In a fifth aspect, an embodiment of the present invention provides a product defect detection system, including the apparatus in any one of the third aspect or the fourth aspect, and a production database, configured to store image data of a product, and a prediction result of a defect category corresponding to the image data of the product and a defect processing operation corresponding to the prediction result of the defect category; and the training database is used for storing historical marking data of the image data of the product, and the historical marking data is used for training the classification prediction model.
In a sixth aspect, an embodiment of the present invention provides a server, including: one or more processors; storage means for storing one or more programs; the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of any of the first or second aspects as described above.
In a seventh aspect, an embodiment of the present invention provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the computer program implements the method of any one of the first aspect and the second aspect.
One of the above technical solutions has the following advantages or beneficial effects: the embodiment provided by the invention is suitable for any scene for classifying the defects by using human eyes, photos or machine vision, can develop the iterative model along with the business, enables the model to adapt to the latest requirements of the production environment, and brings remarkable improvement to the industrial production line in the aspects of classification precision, expandability, standardization and the like.
Another technical scheme in the above technical scheme has the following advantages or beneficial effects: and the work efficiency is further improved by load balancing and scheduling and parallel processing.
The foregoing summary is provided for the purpose of description only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features of the present invention will be readily apparent by reference to the drawings and following detailed description.
Drawings
In the drawings, like reference numerals refer to the same or similar parts or elements throughout the several views unless otherwise specified. The figures are not necessarily to scale. It is appreciated that these drawings depict only some embodiments in accordance with the disclosure and are therefore not to be considered limiting of its scope.
FIG. 1 is an overall block diagram of a product defect detection method according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating steps of a method for detecting defects in a product according to a preferred embodiment of the present invention;
FIG. 3 is a flowchart of the operation of a preferred embodiment of the execution server side of the product defect detection method provided by the present invention;
FIG. 4 is a schematic workflow diagram of a preferred embodiment of a product defect detection method provided by the present invention;
FIG. 5 is a schematic diagram illustrating a preferred embodiment of a product defect detection method according to the present invention;
FIG. 6 is an overall block diagram of a product defect detecting apparatus according to an embodiment of the present invention;
FIG. 7 is a schematic structural diagram of an execution server side of the product defect detecting apparatus according to an embodiment of the present invention;
fig. 8 is a schematic structural diagram of another preferred embodiment of an execution server side of the product defect detecting apparatus provided by the present invention.
Detailed Description
In the following, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments may be modified in various different ways, all without departing from the spirit or scope of the present invention. Accordingly, the drawings and description are to be regarded as illustrative in nature, and not as restrictive.
The embodiment of the invention provides a product defect detection method. Fig. 1 is an overall framework diagram of a product defect detection method according to an embodiment of the present invention. As shown in fig. 1, the method for detecting product defects of the embodiment of the present invention includes: step S110, acquiring image data of a product; step S120, converting the image data into a classification request, determining an execution server according to the deployment conditions of the classification prediction models on the plurality of servers, and sending the classification request to the execution server so as to give a prediction result of the defect category through the classification prediction models on the execution server.
The existing quality inspection system has two main modes in defect classification application. The first is a pure manual quality inspection mode, namely, the judgment is given by depending on the observation of photos in the production environment by the naked eyes of an industry expert; the second is a machine-assisted manual quality inspection mode, which mainly filters out photos without defects by a quality inspection system with certain judgment capability, and detects and judges the photos suspected to have defects by an industry expert. The second mode is mainly developed by an expert system and a characteristic engineering system, and the expert solidifies the experience in the quality inspection system. In the two methods, the defect detection and positioning method depends heavily on expert knowledge, and has the defects of low accuracy, poor real-time performance, difficult expansion, difficult evolution along with the service, weak normalization and the like.
The embodiment of the invention is based on the application of an artificial intelligence technology in machine vision, utilizes images acquired by image acquisition equipment on a production line in real time, detects and judges the surface quality of a product through a pre-trained machine learning model, and judges the category corresponding to the quality problem if the quality problem exists on the product passing through the image acquisition equipment at present. The embodiment of the invention is suitable for all scenes of detecting the product quality according to the image data of the product surface, and compared with the detection method relying on manual work and expert experience in the prior art, the detection method has the advantages of high automation degree, high detection precision, capability of developing an iterative model along with the service, obvious improvement in the aspects of expandability, standardization and the like, and further improves the working efficiency through load balancing and scheduling and parallel processing.
FIG. 2 is a flowchart illustrating steps of a method for detecting defects in a product according to a preferred embodiment of the present invention. As shown in fig. 2, according to an embodiment of the product defect detecting method of the present invention, determining an execution server according to a deployment situation of a classification prediction model on a plurality of servers includes: step S210, inquiring a preset server resource allocation management table, wherein the server resource allocation management table is used for recording the load state of each server with the classification prediction model; step S220, comparing the load states of the servers with the classification prediction models, and determining the server with the lowest load as the execution server.
In this embodiment, image data of the product, that is, a picture generated in real time on the production line, is obtained, and then the image data of the product is converted into a sort request (query) and the sort request is sent to the execution server. The execution server is determined according to the deployment conditions of the classification prediction models on a plurality of servers, the classification prediction models of one version are copied to a plurality of machines in advance for parallel processing, load balancing and scheduling are carried out in real time according to the deployment conditions of the online classification prediction models, and classification requests are sent to the optimal server carrying the classification prediction models. A machine resource allocation management table can be set for recording the load state of each machine in real time, selecting a server with the least load, namely the fastest response, and sending the classification request to the server.
According to an embodiment of the product defect detection method of the present invention, after the prediction result of the defect category is given, the method further includes: making corresponding defect processing operation according to the prediction result of the defect category, wherein the defect processing operation comprises the following steps: alarm, tag, log, and/or shutdown.
According to an embodiment of the product defect detection method of the present invention, the corresponding defect handling operation is performed according to the prediction result of the defect category, including: making corresponding defect processing operation according to the preset corresponding relation between the prediction result of the defect type and the defect processing operation; or, according to the preset grade of the prediction result of the defect type and the corresponding relation between the grade of the prediction result of the defect type and the defect processing operation, making the corresponding defect processing operation.
On the other hand, the embodiment of the invention provides a product defect detection method. Fig. 3 is a flowchart of a preferred embodiment of the execution server side of the product defect detection method provided by the present invention. As shown in fig. 3, the method for detecting product defects at the execution server end according to the embodiment of the present invention includes: step S310, receiving a classification request of image data of a product; and step S320, performing classification calculation on the classification request through a pre-trained classification prediction model, and giving a prediction result of the defect type.
According to an embodiment of the product defect detecting method of the present invention, before performing the classification calculation on the classification request, the method further includes: and preprocessing the image data in the classification request, wherein the preprocessing comprises image denoising, background removing, image compression and/or format conversion. The image data is preprocessed, and the effective part is reserved for subsequent processing.
According to an embodiment of the product defect detecting method of the present invention, the method further includes: and pre-training according to historical labeling data of image data of the product to obtain the classification prediction model. Fig. 4 is a schematic workflow diagram of a product defect detection method according to a preferred embodiment of the present invention. As shown in fig. 4, the classification prediction model is obtained by training a training engine according to historical labeled data, the historical labeled data is stored in a training database, the training engine sends a data request to the training database, and the training database returns the training data to the training engine in response to the data request. In addition, the production database stores data including image data of recent products and prediction results of defect types corresponding to the image data of the products, the production database can provide data updating for the training database at any time, and if the production process development is updated, the training data in the training database can be iterated along with the development of business, so that the model can adapt to the latest requirements of a production environment.
According to one embodiment of the product defect detection method of the present invention, the classification prediction model includes a feature extraction model and a defect localization classification model; the feature extraction model is used for extracting features of the image data in the classification request; the defect positioning classification model is used for providing a prediction result of a defect category according to the extracted features of the image data, and the prediction result comprises: whether the image in the classification request has a defect or not and the category and the position coordinates of the defect.
According to one embodiment of the product defect detection method of the present invention, the feature extraction model includes a deep convolutional neural network; the defect localization classification model includes RCNN (Region-Based CNN), ssd (single Shot multi boxdetector), or Mask RCNN.
Fig. 5 is a schematic diagram illustrating a product defect detection method according to a preferred embodiment of the present invention. As shown in fig. 5, in one embodiment, a deep convolutional neural network is used as a feature extraction model, an object detection and image segmentation technology is used as a defect location classification network, an original picture on a production line is used as an input of the model, after preprocessing of input image data, an effective part is input into the neural network model, and the deep convolutional neural network extracts features in the original picture and inputs the features into the defect location classification network. The defect positioning and classifying network can adopt object detection and other image segmentation models such as RCNN, SSD, Mask RCNN and the like as a defect positioning and classifying model, judge whether a certain part in a picture has a defect according to the characteristics extracted by the deep convolutional neural network, and judge the category of the defect if the certain part has the defect. The final output of the model is the category of defects present in the picture and their relative position coordinates in the picture. If there are multiple defects in the picture, the model will give the category of each defect and its relative coordinates. Taking the paper production industry as an example, the types of defects include wrinkles, holes, tears, impurities, color differences, and the like.
The model trained each time can gradually replace the old model running on line in a small-flow online mode so as to achieve the purpose that the model expands and generalizes along with the service dynamic.
In another embodiment, a feature extraction network may be further performed based on a machine learning method other than a deep Convolutional Neural Network (CNN), and a model other than object detection and image segmentation may be used as a defect localization classification network model.
According to an embodiment of the product defect detection method of the present invention, after the prediction result of the defect category is given, the method further includes: making corresponding defect processing operation according to the prediction result of the defect category, wherein the defect processing operation comprises the following steps: alarm, tag, log, and/or shutdown.
Referring to fig. 4, after a prediction result representing the defect type is given, the prediction result is transmitted to the control module, the control module is designed in combination with a service scene, and can make a response meeting the scene requirements of the production environment, such as alarming, labeling, log storage and/or shutdown, to the prediction result given by the model according to the service requirements, and the control module stores the prediction result and the processing behavior of the response as an on-line production log in the production database.
According to an embodiment of the product defect detection method of the present invention, the corresponding defect handling operation is performed according to the prediction result of the defect category, including: making corresponding defect processing operation according to the preset corresponding relation between the prediction result of the defect type and the defect processing operation; or, according to the preset grade of the prediction result of the defect type and the corresponding relation between the grade of the prediction result of the defect type and the defect processing operation, making the corresponding defect processing operation.
The corresponding relationship between the predicted result of the defect type and the defect processing operation, the grade of the predicted result of the defect type, and the corresponding relationship between the grade of the predicted result of the defect type and the defect processing operation can be set by default of the system or by self-definition of a manufacturer. For example, the prediction results of defect categories may be ranked as severe, normal, or not severe; the manufacturer can set shutdown operation when the defects with serious levels are found, alarm operation when the defects with general levels are found, only labeling operation when the defects with non-serious levels are found, and the like.
On the other hand, the embodiment of the invention provides a product defect detection device. Fig. 6 is an overall frame diagram of the product defect detecting apparatus according to the embodiment of the present invention. As shown in fig. 6, the product defect detecting apparatus according to the embodiment of the present invention includes: a data acquisition module 100 for acquiring image data of a product; and the load balancing module 200 is configured to convert the image data into a classification request, determine an execution server according to deployment conditions of classification prediction models on multiple servers, and send the classification request to the execution server, so as to provide a prediction result of a defect category through the classification prediction models on the execution server.
According to an embodiment of the apparatus for detecting product defects of the present invention, the load balancing module 200 is further configured to: inquiring a preset server resource configuration management table, wherein the server resource configuration management table is used for recording the load state of each server with the classification prediction model; and comparing the load states of the servers with the classification prediction models, and determining the server with the lowest load as an execution server.
Referring to fig. 4 again, the product defect detecting apparatus in the embodiment of the present invention includes a prediction engine Predictor (i.e., a load balancing module), a classification prediction model Classifier, and a training engine Trainer. The prediction engine converts pictures generated in real time on a production line into a classification request (query), performs load balancing and scheduling in real time according to the deployment condition of the online prediction model, and sends the classification request to the optimal server carrying the prediction model. The server runs a real-time classification prediction model, and the classification prediction model is obtained by training a training engine according to historical labeling data.
According to one embodiment of the product defect detecting apparatus of the present invention, the apparatus further comprises: receiving a prediction result of the defect type returned by the execution server; making corresponding defect processing operation according to the prediction result of the defect category, wherein the defect processing operation comprises the following steps: alarm, tag, log, and/or shutdown.
According to an embodiment of the product defect detecting apparatus of the present invention, the corresponding defect processing operation is performed according to the prediction result of the defect type, including: making corresponding defect processing operation according to the preset corresponding relation between the prediction result of the defect type and the defect processing operation; or, according to the preset grade of the prediction result of the defect type and the corresponding relation between the grade of the prediction result of the defect type and the defect processing operation, making the corresponding defect processing operation.
In another aspect, an embodiment of the present invention provides a product defect detecting apparatus. Fig. 7 is a schematic structural diagram of an execution server of the product defect detecting apparatus according to a preferred embodiment of the present invention. As shown in fig. 7, the apparatus for detecting product defects at the execution server end according to the embodiment of the present invention includes: a data receiving module 300 for receiving a classification request of image data of a product; and the classification prediction model 400 is used for performing classification calculation on the classification request through a pre-trained classification prediction model to obtain a prediction result of the defect type.
According to an embodiment of the apparatus for detecting product defects of the present invention, the classification prediction model 400 is further configured to: preprocessing image data in the classification request before performing classification calculation on the classification request, wherein the preprocessing comprises image denoising, background removal, image compression and/or format conversion.
According to an embodiment of the apparatus for detecting product defects of the present invention, the apparatus further comprises a training engine, which is used for pre-training the historical annotation data of the image data of the product to obtain the classification prediction model.
According to one embodiment of the apparatus for detecting product defects of the present invention, the classification prediction model 400 includes a feature extraction model and a defect localization classification model; the feature extraction model is used for extracting features of the image data in the classification request; the defect positioning classification model is used for providing a prediction result of a defect category according to the extracted features of the image data, and the prediction result comprises: whether the image in the classification request has a defect or not and the category and the position coordinates of the defect.
According to one embodiment of the apparatus for detecting defects in products of the present invention, the feature extraction model includes a deep convolutional neural network; the defect localization classification model comprises RCNN, SSD or Mask RCNN.
Fig. 8 is a schematic structural diagram of another preferred embodiment of an execution server side of the product defect detecting apparatus provided by the present invention. As shown in fig. 8, an embodiment of the product defect detecting apparatus according to the present invention further includes a control module 600 for: making corresponding defect processing operation according to the prediction result of the defect category, wherein the defect processing operation comprises the following steps: alarm, tag, log, and/or shutdown.
According to an embodiment of the apparatus for detecting product defects of the present invention, the control module 600 is further configured to: making corresponding defect processing operation according to the preset corresponding relation between the prediction result of the defect type and the defect processing operation; or, according to the preset grade of the prediction result of the defect type and the corresponding relation between the grade of the prediction result of the defect type and the defect processing operation, making the corresponding defect processing operation.
Referring to fig. 4 again, the product defect detecting apparatus of the embodiment of the present invention mainly includes several main modules, namely, a prediction engine Predictor (i.e., a load balancing module), a classification prediction model Classifier, a training engine Trainer, a control module Controller, and a Database.
The prediction engine converts pictures generated in real time on a production line into a classification request (query), performs load balancing and scheduling in real time according to the deployment condition of the online prediction model, and sends the classification request to the optimal server carrying the prediction model. The server runs a real-time classification prediction model, which has been trained by a training engine. The model carries out classification calculation after carrying out preset preprocessing on the image data in the coming classification request, gives a prediction result representing the defect type, and transmits the result to the control module. The control module is designed in combination with the service scene, and can make a response meeting the requirements of the production environment scene, such as alarming, log storage and the like, on a prediction result given by the model according to the service requirements. The control module stores the predicted results and the responsive processing behavior as an on-line production log in a production database. The classification prediction model is obtained by training the training engine according to historical marking data.
In one possible design, the product defect detecting apparatus includes a processor and a memory, the memory is used for storing a program supporting the product defect detecting apparatus to execute the product defect detecting method in the first aspect or the second aspect, and the processor is configured to execute the program stored in the memory.
In yet another aspect, an embodiment of the present invention provides a product defect detecting system, including the apparatus in any one of the third aspect or the fourth aspect, and a production database, configured to store image data of a product, and a prediction result of a defect category corresponding to the image data of the product and a defect processing operation corresponding to the prediction result of the defect category; and the training database is used for storing historical marking data of the image data of the product, and the historical marking data is used for training the classification prediction model. Referring to fig. 4, the historic annotation data is stored in a training database, the training engine sends a data request to the training database, and the training database returns the training data to the training engine in response to the data request. In addition, the image data of the recent product and the prediction result of the defect type corresponding to the image data of the product are stored in the production database, the production database can provide data updating for the training database at any time, and if the development of the production process is updated, the training data in the training database can iterate along with the development of the business, so that the model can adapt to the latest requirements of the production environment.
In another aspect, an embodiment of the present invention provides a server, including: one or more processors; storage means for storing one or more programs; the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of any of the first or second aspects as described above.
In yet another aspect, an embodiment of the present invention provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the computer program implements the method of any one of the first aspect or the second aspect.
One of the above technical solutions has the following advantages or beneficial effects: the embodiment provided by the invention is suitable for any scene for classifying the defects by using human eyes, photos or machine vision, can develop the iterative model along with the business, enables the model to adapt to the latest requirements of the production environment, and brings remarkable improvement to the industrial production line in the aspects of classification precision, expandability, standardization and the like.
Another technical scheme in the above technical scheme has the following advantages or beneficial effects: and the work efficiency is further improved by load balancing and scheduling and parallel processing.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and alternate implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments. The embodiment of the device corresponds to the embodiment of the method, so that the description of the embodiment of the device is relatively simple, and the related description can refer to the description of the embodiment of the method.
In addition, functional units in the embodiments of the present invention may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a separate product, may also be stored in a computer readable storage medium. The storage medium may be a read-only memory, a magnetic or optical disk, or the like.
The above description is only for the specific embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive various changes or substitutions within the technical scope of the present invention, and these should be covered by the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (17)
1. A method for detecting product defects, comprising:
acquiring image data of a product;
and converting the image data into a classification request, determining an execution server according to the deployment condition of classification prediction models on a plurality of servers, and sending the classification request to the execution server so as to give a prediction result of the defect category through the classification prediction models on the execution server.
2. The method of claim 1, wherein determining the execution server based on a deployment of the classification predictive model across a plurality of servers comprises:
inquiring a preset server resource configuration management table, wherein the server resource configuration management table is used for recording the load state of each server with the classification prediction model;
and comparing the load states of the servers with the classification prediction models, and determining the server with the lowest load as an execution server.
3. The method of claim 1 or 2, further comprising:
receiving a prediction result of the defect type returned by the execution server;
making corresponding defect processing operation according to the prediction result of the defect category, wherein the defect processing operation comprises the following steps: alarm, tag, log, and/or shutdown.
4. The method of claim 3, wherein the performing the corresponding defect handling operation according to the prediction result of the defect category comprises:
making corresponding defect processing operation according to the preset corresponding relation between the prediction result of the defect type and the defect processing operation; or,
and making corresponding defect processing operation according to the preset grade of the prediction result of the defect type and the corresponding relation between the grade of the prediction result of the defect type and the defect processing operation.
5. A method for detecting product defects, comprising:
receiving a classification request for image data of a product;
classifying and calculating the classification request through a pre-trained classification prediction model to give a prediction result of the defect classification; the classification prediction model comprises a feature extraction model and a defect positioning classification model; the feature extraction model is used for extracting features of the image data in the classification request; the defect positioning classification model is used for providing a prediction result of a defect category according to the extracted features of the image data, and the prediction result comprises: whether the image in the classification request has a defect or not and the category and the position coordinates of the defect.
6. The method of claim 5, further comprising, prior to performing a classification calculation on the classification request: and preprocessing the image data in the classification request, wherein the preprocessing comprises image denoising, background removing, image compression and/or format conversion.
7. The method of claim 5 or 6, further comprising: and pre-training according to historical labeling data of image data of the product to obtain the classification prediction model.
8. The method of claim 5 or 6, wherein the feature extraction model comprises a deep convolutional neural network; the defect localization classification model comprises RCNN, SSD or Mask RCNN.
9. The method according to claim 5 or 6, further comprising, after giving the prediction result of the defect class:
making corresponding defect processing operation according to the prediction result of the defect category, wherein the defect processing operation comprises the following steps: alarm, tag, log, and/or shutdown.
10. The method of claim 9, wherein performing the corresponding defect handling operation according to the prediction result of the defect category comprises:
making corresponding defect processing operation according to the preset corresponding relation between the prediction result of the defect type and the defect processing operation; or,
and making corresponding defect processing operation according to the preset grade of the prediction result of the defect type and the corresponding relation between the grade of the prediction result of the defect type and the defect processing operation.
11. A product defect detecting apparatus, comprising:
the data acquisition module is used for acquiring image data of a product;
and the load balancing module is used for converting the image data into a classification request, determining an execution server according to the deployment condition of the classification prediction models on the plurality of servers, and sending the classification request to the execution server so as to give a prediction result of the defect category through the classification prediction models on the execution server.
12. The apparatus of claim 11, wherein the load balancing module is further configured to:
inquiring a preset server resource configuration management table, wherein the server resource configuration management table is used for recording the load state of each server with the classification prediction model;
and comparing the load states of the servers with the classification prediction models, and determining the server with the lowest load as an execution server.
13. A product defect detecting apparatus, comprising:
the data receiving module is used for receiving a classification request of image data of a product;
the classification prediction model is used for performing classification calculation on the classification request through a pre-trained classification prediction model to give a prediction result of the defect type; the classification prediction model comprises a feature extraction model and a defect positioning classification model; the feature extraction model is used for extracting features of the image data in the classification request; the defect positioning classification model is used for providing a prediction result of a defect category according to the extracted features of the image data, and the prediction result comprises: whether the image in the classification request has a defect or not and the category and the position coordinates of the defect.
14. The apparatus of claim 13, further comprising a control module to: making corresponding defect processing operation according to the prediction result of the defect category, wherein the defect processing operation comprises the following steps: alarm, tag, log, and/or shutdown.
15. A product defect detection system, characterized in that it comprises a device according to any of claims 11 or 12 and a device according to any of claims 13 or 14, and
the production database is used for storing image data of a product, a prediction result of a defect type corresponding to the image data of the product and a defect processing operation corresponding to the prediction result of the defect type;
and the training database is used for storing historical marking data of the image data of the product, and the historical marking data is used for training the classification prediction model.
16. A server, comprising:
one or more processors;
storage means for storing one or more programs;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of any of claims 1-4 or 5-10.
17. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-4 or 5-10.
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