EP3762866A1

EP3762866A1 - Method, device, system and program for detecting workpiece and storage medium

Info

Publication number: EP3762866A1
Application number: EP18722199.9A
Authority: EP
Inventors: Shuichi Misumi; Satoru Uchida; Jun Sasaki
Original assignee: Omron Corp; Omron Tateisi Electronics Co
Current assignee: Omron Corp
Priority date: 2018-03-05
Filing date: 2018-03-05
Publication date: 2021-01-13
Also published as: JP7103421B2; WO2019171122A1; JP2021516386A

Abstract

A method, a device, a system and a program for detecting a workpiece and a storage medium are disclosed in the present disclosure, the method for detecting a workpiece comprises: acquiring original training data related to the workpiece; acquiring a first training data satisfying a predetermined condition from the original training data; and processing part or all of the content of the first training data to generate new training data which is classified into the first category or the second category; using the original training data and the new training data to train a predetermined machine learning model, so as to obtain a machine learning model which has been trained, and using the machine learning model which has been trained to detect the workpiece. The new training data is obtained by processing the original training data such that the accuracy of a determination result of workpiece detection is effectively improved.

Description

METHOD, DEVICE, SYSTEM AND PROGRAM FOR DETECTING

WORKPIECE AND STORAGE MEDIUM

Technical Field

The present disclosure relates to the field of workpiece detection, and specifically, relates to a method, a device, a system and a program for detecting a workpiece, and a storage medium.

Background

In the field of workpiece detection, a machine learning model can be used to detect the quality of a workpiece.

However, a lot of training data is needed for training a machine learning model. Generally, the more training data there is, the higher the accuracy of the determination result of the machine learning model which has been trained is. Namely, the accuracy of the determination result of the machine learning model which has been trained can be improved by increasing training data.

However, the increase of training data will make a training process longer, and increase system load. When the training data reach a certain amount, the accuracy of the determination result cannot be effectively improved simply by increasing the training data, thereby failing to effectively improve the accuracy of detecting the quality of a workpiece.

With respect to the descried problem, no effective solution has been proposed yet.

Summary

^• The technical problem to be solved

For this, a method, a device, a system and a program for detecting a workpiece, and a storage medium are provided in the present disclosure, so as to solve the problem in the related art that the accuracy of a determination result cannot be effectively improved when a machine learning model is used to detect the quality of a workpiece.

^• Means of solving the technical problem

According to a first aspect of the present disclosure, a method for detecting a workpiece is provided, the method comprises: acquiring original training data related to the workpiece, the original training data comprising training data which is pre-classified into a first category and training data which is pre-classified into a second category; acquiring a first training data satisfying a predetermined condition from the original training data; and processing part or all of the content of the acquired first training data, to generate new training data which is classified into the first category or a second category; using the original training data and the new training data to train a predetermined machine learning model so as to obtained a machine learning model which has been trained; and using the machine learning model which has been trained to detect the workpiece.

By means of processing the training data satisfying a predetermined condition in the original training data to generate new training data, the total amount of the training data is increased, and the predetermined machine learning model is trained by using the original training data and the new training data, effectively improving the accuracy of the determination result of the machine learning model, thereby improving the accuracy of detecting the quality of a workpiece.

Further, acquiring the first training data satisfying the predetermined condition from the original training data comprises: determining the possibility of the training data in the first category being classified into the second category; and selecting the training data of which the possibility exceeds a predetermined threshold value as the first training data.

Further, acquiring the first training data satisfying the predetermined condition from the original training data comprises: determining the possibility of the training data in the second category being classified into the first category; and selecting the training data of which the possibility exceeds a predetermined threshold value as the first training data.

The possibility of the training data in the first category being classified into the second category exceeding the predetermined threshold value or the possibility of the training data in the second category being classified into the first category exceeding the predetermined threshold value indicates that the possibility of the training data locating in a border region between the first category and the second category is large, and selecting such training data which is in the border region as the first training data to be processed can effectively increase the total amount of the training data, thereby improving the accuracy of the determination result of the machine learning model.

Specifically, each training data is classified into a category in the plurality of categories according to the relationship between its own eigenvalue and at least one threshold value or a threshold value range; and the at least one threshold value or the threshold value range is the basis for distinguishing the borders of different categories, wherein, determining the possibility of the training data in the first category being classified into the second category comprises: determining, according to the relationship between the eigenvalue and the at least one threshold value or the threshold value range, the possibility of the training data in the first category being classified into the second category.

Specifically, each training data is classified into a category in the plurality of categories according to the relationship between its own eigenvalue and at least one threshold value or a threshold value range; and the at least one threshold value or the threshold value range is the basis for distinguishing the borders of different categories, wherein, determining the possibility of the training data in the second category being classified into the first category comprises: determining, according to the relationship between the eigenvalue and the at least one threshold value or the threshold value range, the possibility of the training data in the second category being classified into the first category.

According to the relationship between the eigenvalue of the training data and the threshold value or the threshold value range, it can be easily to determine whether the training data is located within the border region between the first category and the second category.

Optionally, acquiring the first training data satisfying the predetermined condition from the original training data comprises: extracting predetermined training data, of which the eigenvalue is in a predetermined range, from the training data in the first category as the first training data, wherein, the predetermined training data is the training data for which at least N training data among M training data of which the eigenvalues are in the same predetermined range as the eigenvalue of the predetermined training data belongs to the second category, wherein, M is an integer greater than or equal to 2, and N is an integer greater than or equal to 1.

Optionally, acquiring the first training data satisfying the predetermined condition from the original training data comprises: extracting predetermined training data, of which the eigenvalue is in a predetermined range, from the training data in the second category as the first training data, wherein, the predetermined training data is the training data for which at least N training data among M training data of which the eigenvalues are in the same predetermined range as the eigenvalue of the predetermined training data belongs to the first category, wherein, M is an integer greater than or equal to 2, and N is an integer greater than or equal to 1.

As another method for acquiring the training data within a border region, for the cases where the borderline of the border region between the first category and the second category is not obvious, the described method can effectively extract the training data within the border region.

Further, processing part or all of the content of the acquired first training data comprises: extracting training data from the training data belonging to the first category according to a predetermined rule, or calculating the average value or median of all the training data belonging to the first category, as reference training data; and processing, on the basis of the reference training data, part or all of the content of the acquired first training data.

Optionally, processing part or all of the content of the acquired first training data comprises: extracting training data from the training data belonging to the second category according to a predetermined rule, or calculating the average value or median of all the training data belonging to the second category, as reference training data; and processing, on the basis of the reference training data, part or all of the content of the acquired first training data.

Taking the training data of the first category or the training data of the second category as reference data to process the first training data can make the first training data after being processed definitely belongs to the first category or the second category.

Specifically, the original training data is image data, wherein, processing part or all of the content of the acquired first training data comprises: modifying, according to the image data belonging to the first category or the image data belonging to the second category, at least one characteristic region of the image data which is the acquired first training data.

For the training data embodied as image data, at least one characteristic region of the first training data is modified according to the image data belonging to the first category or belonging to the second category, such that the processing of the first training data is more intuitive.

According to a second aspect of the present disclosure, a device for detecting a workpiece is provided, the device comprises: an acquiring unit, acquiring original training data related to the workpiece, and acquiring a first training data satisfying a predetermined condition from the original training data, wherein, the original training data comprising training data which is pre-classified into a first category and training data which is pre-classified into a second category; and a processing unit, processing part or all of the content of the acquired first training data, to generate new training data which is classified into the first category or a second category; a training unit, using the original training data and the new training data to train a predetermined machine learning model so as to obtain a machine learning model which has been trained; and a detecting unit, using the machine learning model which has been trained to detect the workpiece.

Further, the acquiring unit comprises: a determining unit, determining the possibility of the training data in the first category being classified into the second category; and a selecting unit, selecting the training data of which the possibility exceeds a predetermined threshold value as the first training data.

Further, the acquiring unit comprises: a determining unit, determining the possibility of the training data in the second category being classified into the first category; and a selecting unit, selecting the training data of which the possibility exceeds a predetermined threshold value as the first training data.

Specifically, each training data is classified into a category in the plurality of categories according to the relationship between its own eigenvalue and at least one threshold value or a threshold value range; and the at least one threshold value or the threshold value range is the basis for distinguishing the borders of different categories, wherein, the determining unit determines, according to the relationship between the eigenvalue and the at least one threshold value or the threshold value range, the possibility of the training data in the first category being classified into the second category.

Specifically, each training data is classified into a category in the plurality of categories according to the relationship between its own eigenvalue and at least one threshold value or a threshold value range; and the at least one threshold value or the threshold value range is the basis for distinguishing the borders of different categories, wherein, the determining unit determines, according to the relationship between the eigenvalue and the at least one threshold value or the threshold value range, the possibility of the training data in the second category being classified into the first category. Optionally, the acquiring unit extracts predetermined training data, of which the eigenvalue is in a predetermined range, from the training data in the first category as the first training data, wherein, the predetermined training data is the training data for which at least N training data among M training data of which the eigenvalues are in the same predetermined range as the eigenvalue of the predetermined training data belongs to the second category, wherein, M is an integer greater than or equal to 2, and N is an integer greater than or equal to 1.

Optionally, the acquiring unit extracts predetermined training data, of which the eigenvalue is in a predetermined range, from the training data in the second category as the first training data, wherein, the predetermined training data is the training data for which at least N training data among M training data of which the eigenvalues are in the same predetermined range as the eigenvalue of the predetermined training data belongs to the first category, wherein, M is an integer greater than or equal to 2, and N is an integer greater than or equal to 1.

Further, the processing unit extracts training data from the training data belonging to the first category according to a predetermined rule, or calculates the average value or median of all the training data belonging to the first category, as reference training data; and processes, on the basis of the reference training data, part or all of the content of the acquired first training data.

Further, the processing unit extracts training data from the training data belonging to the second category according to a predetermined rule, or calculates the average value or median of all the training data belonging to the second category, as reference training data; and processes, on the basis of the reference training data, part or all of the content of the acquired first training data.

Specifically, the original training data is image data, wherein, the processing unit modifies, according to the image data belonging to the first category or the image data belonging to the second category, at least one characteristic region of the image data which is the acquired first training data.

According to a third aspect of the present disclosure, a method for generating training data is provided, the method comprising: acquiring a first training data satisfying a predetermined condition; and processing part or all of the content of the acquired first training data, to generate new training data which is classified into a first category or a second category.

By means of processing the training data satisfying the predetermined condition to classify the training data into the first category or the second category, the total amount of the training data is increased, and then such training data is used to train the machine learning model, such that the accuracy of the determination result of the machine learning model can be improved.

According to a fourth aspect of the present disclosure, a device for generating training data is provided, the device comprises: an acquiring unit, acquiring a first training data satisfying a predetermined condition; and a processing unit, processing part or all of the content of the acquired first training data, to generate new training data which is classified into the first category or a second category.

According to a fifth aspect of the present disclosure, a system for detecting a workpiece is provided, the system comprising: a processing unit, executing the method according to the first aspect of the present disclosure; and an output unit, outputting a detection result of the workpiece.

According to the sixth aspect of the present disclosure, a program for detecting a workpiece is provided, the program, when being executed, performing the method according to the first aspect of the present disclosure.

According to the seventh aspect of the present disclosure, a storage medium is provided, which is stored thereon with a program, the program, when being executed, performing the method according to the first aspect of the present disclosure.

^• Technical effect

In the present disclosure, the accuracy of the determination result of the machine learning model which has been trained is effectively improved, thereby improving the accuracy of detecting the quality of a workpiece.

Brief Description of the Drawings

The drawings described herein are used for providing a further understanding of the present disclosure, and form a portion of the present disclosure. The exemplary embodiments of the present disclosure and the description thereof are used for explaining the present disclosure, and not form inappropriate definitions on the present disclosure. In the drawings:

Figure 1 is a mode diagram showing the hardware structure of an information processing system according to an embodiment of the present disclosure.

Figure 2 shows a distribution diagram of training data in a case of determining whether a product is qualified according to an image;

Figure 3 shows a flow chart of detecting a workpiece according to the present disclosure;

Figure 4 shows an example of processing the training data within a border region according to the present disclosure; and

Figure 5 is a block diagram of a device for detecting a workpiece according to the present disclosure.

Detailed Description of the Embodiments

In order to allow a person skilled in the art to better understand the solution of the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described in conjunction with the drawings in the embodiments of the present disclosure in the following. Obviously, the embodiments described are only a part of the embodiments of the present disclosure, but not all the embodiments. On the basis of the embodiments in the present disclosure, all of the other embodiments obtained by a person skilled in the art without involving any inventive efforts shall fall within the scope of protection of the present disclosure.

In order to solve the problem that the accuracy of a determination result cannot be effectively improved when a machine learning model is used to detect the quality of a workpiece, the present disclosure increases the total amount of the training data, by means of generating new training data from the original training data, effectively improving the accuracy of a determination result of the machine learning model, thereby improving the accuracy of detecting the quality of a workpiece.

Specifically, according to the present disclosure, original training data related to a workpiece is acquired, and then the original training data is inputted into a machine learning model which has been trained, to obtain the values indicating the possibilities of the training data belonging to a first category and a second category, taking the training data for which the difference between the value of the first category and the value of the second category is below a predetermined value as the training data to be processed. The eigenvalue or characteristic region of the training data to be processed is modified using the training data definitely belonging to the first category or the training data definitely belonging to the second category, to form new training data. A machine learning model is trained using the original training data and the new training data, so as to be used to detect a workpiece, thereby improving the accuracy of the machine learning model detecting the quality of a workpiece.

Firstly, the hardware structure of an information processing system 100 according to an embodiment of the present disclosure is described.

Figure 1 is a mode diagram showing the hardware structure of an information processing system according to an embodiment of the present disclosure. As shown in figure 1 , for example, an information processing system 100 can be achieved by a general purpose computer of general purpose computer architecture. The information processing system 100 can include a processor 110, a main memory 112, a memory 114, an input interface 116, a display interface 118 and a communication interface 120. These parts, for example, can communicate with each other by an internal bus 122.

The processor 110 develops a program stored in the memory 114 on the main memory 112 so as to be executed, thereby achieving the functions and processing described later. The main memory 112 can be configured to be a volatile memory, acting as a work memory required by the processor 110 to execute the program.

The input interface 116 can be connected to input units, for example, a mouse and a keyboard, for receiving the instructions inputted by operating the input units by an operator.

The display interface 118 can be connected to a display, and a variety of processing results generated by the processor 110 executing the program can be outputted to the display.

The communication interface 120 is used for communicating with a PLC and a database device and so on by a network 200.

The memory 114 can be stored with a program which can allow a computer to function as the information processing system 100, for example, an information processing program, an OS (operating system), and so on.

The information processing program stored in the memory 114 can be installed into the information processing system 100 by an optical recording medium, for example, a Digital Versatile Disc ( DVD ), or a semiconductor recording medium, for example, a Universal Serial Bus ( USB ) memory. Or the information processing program can also be downloaded from a server device on the network and so on.

The information processing program according to the present embodiment can also be provided in the manner of combining with other programs. In this case, the information processing program itself does not include the modules included by the other programs of such combination described above, but performs processing in cooperation with the other programs. In this way, the information processing program according to the present embodiment can also be in the form of combining with the other programs.

Figure 1 shows an example of using a general purpose computer to achieve the information processing system 100, but the present disclosure is not limited thereto, and all or part of functions thereof can be achieved by dedicated circuits, for example, an Application Specific Integrated Circuit ( ASIC ) or a Field-Programmable Gate Array ( FPGA ) and so on. In addition, part of the processing of the information processing system 100 can also be performed by an external device connected by the network.

For better understanding the present disclosure, in the following, detecting whether a product is a qualified product is taken as an example to describe the basic principles of generating new training data in the process of using the machine learning model to detect the quality of a workpiece, wherein, the training data used for training a machine learning model is the training data used for training a machine learning model which can determine whether a product is qualified according to a product image, wherein, the number of the classification category of the training data is two. But, the present disclosure is not limited thereto, the training data can be training data used for any other purpose, the number of the classification category of the training data may be three or more.

In a case of determining whether a product is qualified according to images, training data is classified into two categories, in which, one category represents qualified products, which are represented by circles ( O :positive), and the other category represents unqualified products, which are represented by cross marks (c negative), as shown in figure 2.

In an ideal situation, there is a clear borderline L1 between qualified products and unqualified products, as shown in (a) of figure 2, the training data on the left side of line L1 represents unqualified products, and the training data on the right side of line L1 represents qualified products. Line L1 can represent a threshold value in relevant to an eigenvalue included in the training data. When the eigenvalue is on one side of the threshold value, the training data including the eigenvalue represents an unqualified product, and when the eigenvalue is on the other side of the threshold value, the training data including the eigenvalue represents a qualified product.

Flowever, in reality, there are“unqualified products which like qualified products” or “qualified products which like unqualified products”, such that the borderline between qualified products and unqualified products is changed, as shown in (b) of figure 2. The borderline between qualified products and unqualified products is changed from line L1 to line LT. Namely, in a certain region (between line L1 and line L2) on the left side of line L1 , there are both training data representing unqualified products and training data representing qualified products, and in a certain region (between line L1 and line L3) on the right side of line L1 , there are both training data representing qualified products and training data representing unqualified products. Namely, there is a “border region” L2L3. In the region, there are training data representing qualified products and training data representing unqualified products.

As another instance, “unqualified products which like qualified products” or “qualified products which like unqualified products” are dispersed near the borderline between qualified products and unqualified products, which makes the borderline between the qualified products and unqualified products is not clear, as shown in (c) of figure 2. Herein, the region in which there are both training data representing qualified products and training data representing unqualified products, i.e. the region between line L2 and line L3 is also called as“border region”.

In the present disclosure, in the process of detecting whether a product is a qualified product, the training data within the described border region is acquired, and then, after the characteristic thereof is modified, classified into a first category or a second category, so as to acquire new training data, and meanwhile, the training data within the border region is retained. In this way, on one hand, the amount of the training data is increased, and on the other hand, the new training data is acquired from the border region of the original training data, such that the accuracy of a determination result of the machine learning model can be effectively improved, thereby improving the accuracy of detecting the quality of a product.

According to the embodiment of the present disclosure, an embodiment of the method for detecting a workpiece is provided. Figure 3 shows a flow chart of detecting a workpiece according to the present disclosure, the workpiece here is a specific product, e.g. a coil and so on, and the specific details will be described in conjunction with figure 3 in the following.

The flow starts with step S301. In step S301 , original training data related to the workpiece is acquired.

The original training data here can be any type of training data, including but not being limited to image data, vector data and so on. The number of the classification category of the original training data is two or more. For simplicity, it is assumed herein that the number of the classification category of the acquired original training data is two. Taking the training data used for determining whether a product is qualified as an example, the classification category of the original training data can be“qualified product” and“unqualified product”. But the present disclosure is not limited thereto, the original training data is not limited to the training data for determining whether a product is qualified, and number of the classification category of the original training data can be three or more.

The way for acquiring original training data can be varied, and this is known in the art. In order to avoid unnecessarily obscuring the present disclosure, the way for acquiring original training data will be not redundantly described here.

In step S303, training data satisfying a predetermined condition is acquired from the original training data.

In the described example of determining whether a product is qualified, as shown in (b) and (c) of figure 2, there is a border region L2L3 between training data representing qualified products and training data representing unqualified products. In combination with figure 2, in this step, acquiring the training data satisfying a predetermined condition is acquiring the training data in the border region L2L3.

For each training data, it can consist of one or more eigenvalues and a label data. For example, for the training data used for determining whether a product is qualified, the eigenvalues of each training data can include: the shape of a product (round, oval), the color of a product (red, blue), the coil connectivity of a product and the like. For the training data of which the eigenvalue satisfies a predetermined threshold value or threshold value range, the label data thereof can be set to“1”, representing that the training data is pre-classified into“qualified product”, hereinafter referred to as “first category”. For the training data which does not satisfy the described predetermined threshold value or threshold value range, the label data thereof can be set to“0”, representing that the training data is pre-classified into “unqualified product”, hereinafter referred to as“second category”. The relationship between the eigenvalue of the training data and the threshold value or threshold value range corresponding to the corresponding eigenvalue distinguishes “first category” from“second category”.

As described above, in the process classifying the training data, there are the training data which can be definitely classified into a first category or a second category, and the training data which falls within a border region. For example, for the training data which is classified into the first category, the difference between the likelihood of it being classified into the first category and the likelihood of it being classified into the second category is calculated, so as to determine the possibility of the training data in the first category being classified into the second category. For example, in a case where the training data is a training data representing an qualified product or a unqualified product, it is assumed that the value obtained from the training data through the operation of the machine learning model is [a, b], wherein a represents the likelihood of the training data being classified into qualified products, and b represents the likelihood of the training data being classified into unqualified products. For training data A of which the value obtained by operation is [0.7, 0.1 ], it is definitely classified into qualified products, and the possibility of it being classified into unqualified products is relatively small, and in this case, the difference between a and b is 0.6. On the other hand, for training data B of which the value is [0.6, 0.3], it is also classified into qualified products, but the possibility of it being classified into unqualified products is larger than that of training data A, and in this case, the difference between a and b is 0.3. That is, the larger the difference between a and b is, the smaller the possibility of the training data in a qualified product category being classified into unqualified products is; and the smaller the difference between a and b is, the larger the possibility of the training data in a qualified product category being classified into unqualified products is. Therefore, a predetermined threshold value for a border region can be set, and when the described possibility exceeds the predetermined threshold value, it shows that the training data is in the border region between the first category and the second category, the training data like this is extracted as the training data to be processed. For the training data which is classified into the second category, a similar method as the method described above can be used to determine the possibility of the training data in the second category being classified into the first category.

The method for determining the possibility of the training data in the first category being classified into the second category or the method for determining the possibility of the training data in the second category being classified into the first category is not limited to the described method. For example, in a case where each training data includes a plurality of eigenvalues, the plurality of eigenvalues are, for example, used for representing the shape feature, the color feature and the like of a workpiece. Each eigenvalue has at least one threshold value or threshold value range corresponding to it and whether the feature of the product represented by the eigenvalue is quanlified or unqualified is determined according to the threshold value or threshold value range. That is, the at least one threshold value or threshold value range can be taken as the basis for distinguishing the borders of the training data of different categories. In this way, each training data is classified into the first category or the second category according to the relationship between its own eigenvalue and at least one threshold value or threshold value range, for example, the qualified product category and the unqualified product category. For the training data in the qualified product category, if the difference between its eigenvalue and the threshold value or threshold value range is relatively small, it shows that the training data is close to the border and the possibility of the training data being classified into unqualified products is relatively large; while for the training data in the qualified product category, if the difference between its eigenvalue and the threshold value or threshold value range is relatively large, it shows that the training data is far away from the border and thus the possibility of the training data being classified into unqualified products is relatively small. Similarly, for the training data in the unqualified product category, if the difference between its eigenvalue and the threshold value or threshold value range is relatively small, it shows that the training data is close to the border and the possibility of the training data being classified into qualified products is relatively large; while for the training data in the unqualified product category, if the difference between its eigenvalue and the threshold value or threshold value range is relatively large, it shows that the training data is far away from the border and thus the possibility of the training data being classified into qualified products is relatively small.

As another example, acquiring the training data belonging to the border region can also be achieved by the following way.

As shown in (c) of figure 2, in border region L2L3, there are both training data representing qualified products (first category) and the training data representing unqualified products (second category). That is, in border region L2L3, there is a plurality of training data representing unqualified products around the training data representing qualified products. With the distance far away from the border region, for example, in the right direction as shown in (c) of figure 1 , the training data representing unqualified products around the training data representing qualified products becomes fewer. Therefore, the number of the training data representing unqualified products around the training data representing qualified products can be used to characterize whether the training data representing qualified products is within the border region. For example, for each training data D in the qualified product category, M training data of which the eigenvalue is in same predetermined range as the eigenvalue thereof is selected, and if part or all of the training data in the M training data, for example, N training data, belongs to the training data in the unqualified product category, the training data D is considered to be in the border region and can be extracted as the training data to be processed, wherein, M is an integer larger than or equal to 2, N is an integer larger than or equal to 1 , and the predetermined range can be set according to actual needs. In a similar way, the training data in the border region can also be extracted from the training data representing unqualified products so as to take these training data as the training data to be processed.

In step S305, the acquired training data is processed to generate new training data.

For the training data to be processed which is acquired from the border region, the training data can be processed in various ways so as to allow the processed training data to be classified into the first category or the second category, thereby forming new training data.

Specifically, training data which is extracted from the training data in the first category according to a predetermined rule is taken as reference training data, wherein, the predetermined rule, for example, can be the likelihood generated from the training data in the first category through the operation of the machine learning model meet the following requirement, i.e. the difference of the likelihood of the training data belonging to the first category and the likelihood of the training data belonging to the second category is larger than a predetermined value. Optionally, the predetermined rule, for example, can be that the difference between the eigenvalue of the training data in the first category and the corresponding threshold value or threshold value range is larger than a predetermined value. In other words, the training data extracted from the training data in the first category, which definitely belongs to the first category, is taken as reference training data. Part or all of the content of the training data to be processed which is acquired from the border region is processed according to the reference training data. For example, the eigenvalue of the training data to be processed is modified according to the threshold value or threshold value range corresponding to one or more eigenvalues of the reference training data, such that the processed training data is classified into the first category, thereby forming new training data.

Optionally, training data is extracted from the training data in the second category in a similar way as described above as reference training data. The eigenvalue of the training data to be processed is modified according to the threshold value or threshold value range corresponding to one or more eigenvalues of the reference training data, such that the processed training data is classified into the second category, thereby forming new training data.

As another example, the average value or median of all the training data belonging to the first category is calculated as reference training data. Specifically, the same eigenvalues corresponding to all the training data in the first category are averaged or the median thereof is calculated, and the training data of which the corresponding eigenvalue is the average value or median is formed as reference training data. The eigenvalue of the training data to be processed is modified according to the eigenvalue of the reference training data, such that the processed training data is classified into the first category, thereby forming new training data.

Or the average value or median of all the training data belonging to the second category is calculated as reference training data. Specifically, the same eigenvalues corresponding to all the training data in the second category are averaged or the median thereof is calculated, and the training data of which the corresponding eigenvalue is the average value or median is formed as reference training data. The eigenvalue of the training data to be processed is modified according to the eigenvalue of the reference training data, such that the processed training data is classified into the second category, thereby forming new training data.

In step S307, the original training data and the new training data is used to train a predetermined machine learning model, so as to obtain a machine learning model which has been trained.

The new training data is obtained after the training data acquired from the border region is processed, without replacing the original training data in the border region, namely, the new training data is generated while retaining the original training data.

Therefore, the overall amount of the training data is increased, and as the new training data is generated by processing the training data in the border region between the first category and the second category, the new training data is definitely classified into the first category or the second category, thereby effectively improving the accuracy of a determination result of the machine learning model.

Step 309: the machine learning model which has been trained is used to detect the quality of the workpiece.

The detection data of the workpiece, e.g. image data is inputted into the machine learning model which has been trained, and then whether the product is a qualified product or an unqualified product can be determined according to an output result of the machine learning model.

After that, the flow of figure 3 is ended.

Figure 4 shows an example of processing the training data within a border region according to the present disclosure. In these examples, the training data is the image data of coils. The examples here are merely exemplary examples, and the present disclosure is not limited thereto. The following description is made in conjunction with figure 4.

Firstly, original image data of a coil is inputted into a machine learning model which has been trained to determine whether the coil is a qualified product, and whether the image data represents a qualified coil or an unqualified can be determined according to an output result of the machine learning model. For example, after the image data of the coil is inputted into the machine learning model which has been trained, the output result being obtained is [c, d], wherein, c is the likelihood of the image data representing that the coil is a qualified product, and d is the likelihood of the image data representing that the coil is an unqualified product. According to a predetermined threshold value, for example, c0=0.5, for the image data which has the output result that c is greater than or equal to cO, the coil represented by the image data is classified into qualified products, and for the image data which has the output result that c is less than cO, the coil represented by the image data is classified into unqualified products.

By means of the described method, as shown in (a) and (b) of figure 4, two training data belonging to an “unqualified product” category (second category) is acquired..

As shown in (a) of figure 4, the likelihoods generated from the training data through the operation of the machine learning model are shown in the bottom of (a) of figure 4, which are the likelihoods of the training data belonging to“unqualified products” or“qualified products”. As shown in (a) of figure 4, the likelihood of the training data belonging to“unqualified products” is 0.31 , and the likelihood of the training data belonging to“qualified products” is 0.14. Namely, the likelihood of the training data belonging to“unqualified products” is higher than the likelihood of the training data belonging to“qualified products”.

As shown in (a) of figure 4, the coil is disconnected, and the two joints of the disconnected part are staggered in the up and down direction and thud are in different heights. In the training data, the coil with such a disconnection will be easily to be determined as“unqualified products”.

As shown in (b) of figure 4, although the coil is disconnected, the two joints of the disconnected part are in the same height and not staggered in the up and down direction. Therefore, for the likelihood generated from the training data through the operation of the machine learning model, the likelihood of the training data belonging to“qualified products” increases. As shown in (b) of figure 4, the likelihood of the training data belonging to “unqualified products” is 0.15, and the likelihood of belonging to“qualified products” is 0.28.

As to the two training data in (a) and (b) of figure 4, the difference (0.13) between the likelihood of the latter belonging to “unqualified products” and the likelihood of the latter belonging to“qualified products” is less than the difference (0.17) between the likelihood of the former belonging to“unqualified products” and the likelihood of the former belonging to“qualified products”.

The training data of which the difference in likelihood is less than or equal to a predetermined threshold can be processed. For example, the predetermined threshold can be set to 0.15, for the training data in (b) of figure 4, the difference (0.13) between the likelihood of it belonging to “unqualified products” and the likelihood of it belonging to “qualified products” is less than the predetermined threshold 0.15, the training can be processed to increase the likelihood of it belonging to “qualified products” or the likelihood of it belonging to “unqualified products”. While for the training data in (a) of figure 4, the difference (0.17) between the likelihood of it belonging to “unqualified products” and the likelihood of it belonging to“qualified products” is greater than the predetermined threshold 0.15, the training will not be processed.

As an example, the training data in (b) of figure 4 can be extracted and image processing is performed on the extracted training data. For example, the disconnected part is connected together, so as to increase the likelihood of it belonging to “qualified products”, and then the modified training data can be classified into“qualified product” category, thereby obtaining new training data that represents“qualified products”.

As another example, the training data in (b) of figure 4 can be extracted and image processing is performed on the extracted training data. For example, the two joints of the disconnected part is made to be at different heights, or the training data is made to have several disconnected parts which are the same as shown in (b) of figure 4, improving the likelihood of it belonging to“unqualified products”, and then the modified training data is definitely classified into“unqualified product” category, thereby obtaining new training data that represents“unqualified products”.

The disconnected parts in (a) and (b) of figure 4 can be detected by known methods such as edge detecting. Or the disconnected part can be confirmed by the human eyes.

Figure 4 shows that the training data (images in (b) of figure 4) in the border region between a first category (qualified products) and a second category (unqualified products) is extracted and the training data is modified so that the disconnected parts of the coils are connected together or the characteristics of the disconnected parts of the coils become more obvious, such that the modified image data is definitely classified into the first category or the second category, thereby forming new image data.

According to the embodiment of the present disclosure, the embodiment of a device for detecting a workpiece is provided. Figure 5 is a block diagram of a device for detecting a workpiece according to the present disclosure.

As shown in figure 5, the device for detecting comprises an acquiring unit 502 and a processing unit 504, a training unit 506, and a detecting unit 508, wherein, the acquiring unit 502 is configured to acquiring original training data related to the workpiece, and acquiring the training data in a border region from the original training data; and a processing unit 504 is configured to process part or all of the content of the acquired training data in the border region, so as to generate new training data which is classified into a first category or a second category; a training unit 506, using the original training data and the new training data to train a predetermined machine learning unit so as to obtain a machine learning unit which has been trained; and a detecting unit 508, using the machine learning unit which has been trained to detect the workpiece.

The acquiring unit 502 further comprises a determination unit and a selection unit (not shown). In an example of acquiring the training data in the border region, the determination unit can determine the possibility of the training data in the first category being classified into the second category or the possibility of the training data in the second category being classified into the first category, and the selection unit can select the training data of which the possibility exceeds a predetermined threshold value as the training data in the border region.

Furthermore, a variety of implementation methods for acquiring the training data in the border region and for processing the training data to be processed which is acquired from the border region have been described above in detail, these implementation methods can be respectively performed by the acquiring unit 502 and the processing unit 504. In order to avoid repetition, the variety of implementation methods of the acquiring unit 502 acquiring the training data within the border region and the processing unit 504 processing the training data acquired from the border region will not be redundantly described.

As to the device for detecting a workpiece using a machine learning model according to the present embodiment, the new training data is obtained after the training data acquired from the border region is processed and classified, without replacing the existing training data in the border region, namely, the new training data is generated while retaining the existing training data.

Therefore, the overall amount of the training data is increased, and the increased training data is generated by processing and classifying the training data in the border region between the first category and the second category, thereby effectively improving the accuracy of a determination result of the machine learning model, thereby improving the accuracy of detecting the quality of a workpiece.

According to the embodiment of the present disclosure, a method for generating training data is provided, the method comprises: acquiring a first training data satisfying a predetermined condition; and processing part or all of the content of the acquired first training data, to generate new training data which is classified into the first category or a second category.

A variety of implementation methods for acquiring the first training data of the border region and for processing the to-be-processed first training data acquired from the border region have been described above in detail. Therefore, for the avoidance of repetition, the described content will not be described here redundantly.

According to the embodiment of the present disclosure, a device for generating training data is provided, the device comprises: an acquiring unit, acquiring a first training data satisfying a predetermined condition; and a processing unit, processing part or all of the content of the acquired first training data, to generate new training data which is classified into the first category or a second category.

A variety of implementation methods for acquiring the first training data of the border region and for processing the to-be-processed first training data acquired from the border region have been described above in detail. These implementation methods can respectively performed by the acquiring unit and the processing unit. Therefore, for the avoidance of repetition, the acquiring unit acquiring the first training data in the border region and the processing unit processing the to-be-processed training data acquired from the border region will not be described here redundantly.

According the embodiment of the present disclosure, a system for detecting a workpiece is provided, the system comprising a processing unit and an output unit, wherein, the processing unit performs the method for detecting a workpiece described herein, and the output unit outputs a detection result of the workpiece.

According to the embodiment of the present disclosure, a program for detecting a workpiece is provided, the program, when being executed, performing the method for detecting a workpiece described herein.

According the embodiment of the present disclosure, a storage medium is provided, which is stored thereon with a program, the program, when being executed, performing the method described in embodiment 1.

Although the method, device and program for detecting a workpiece and generating training data and the storage medium in the present disclosure have been described above in conjunction with the determination of qualified products/unqualified products, the present disclosure is not limited thereto, and the present disclosure can be applied to the determination of the state or posture of human and so on. For example, in a process of using a machine learning model to determine a posture of a person, for image data representing an ambiguous posture, such image data can be extracted according the method described in the present disclosure, and the extracted image data can be processed, such that the image data can be classified into a category representing a definite posture.

In the above embodiments of the present disclosure, the description to each embodiment has different emphasis, and those not described in detail in a certain embodiment may make reference to relevant descriptions of the other embodiments.

In the several embodiments provided in the present disclosure, it should be understood that the disclosed technical content may be implemented in other ways. The described device embodiments are merely exemplary. For example, the unit division can be logical function division and may be other division in actual implementation. For example, a plurality of units or components may be merged or integrated into another system, or some features may be ignored or not performed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections may be implemented through some interfaces. The indirect couplings or communication connections between the units or modules may be implemented in electronic or other forms.

Furthermore, functional units in each embodiment of the present disclosure may be integrated into one processing unit, or each of the units may exist alone physically, or two or more units are integrated into one unit. The integrated unit may be implemented in the form of hardware or software functional unit.

The above descriptions are only preferred embodiments of the present disclosure. It should be noted that an ordinary skilled person in the art can also make a number of improvements and modifications without departing from the technical principle of the present disclosure, and these improvements and modifications should also be considered as within the scope of protection of the present disclosure.

Reference signs

100 information processing system

110 processor

112 main memory

114 memory

116 input interface

118 display interface

120 communication interface

122 internal bus

200 network

502 acquiring unit

504 processing unit

506 training unit 508 detecting unit.

Claims

1. A method for detecting a workpiece, characterized in that, the method comprises:

acquiring original training data related to the workpiece, the original training data comprising training data which is pre-classified into a first category and training data which is pre-classified into a second category;

acquiring a first training data satisfying a predetermined condition from the original training data;

processing part or all of the content of the acquired first training data, to generate new training data which is classified into the first category or the second category;

using the original training data and the new training data to train a predetermined machine learning model, so as to obtain a machine learning model which has been trained, and

using the machine learning model which has been trained to detect the workpiece.

2. The method of claim 1 , wherein, acquiring the first training data satisfying the predetermined condition from the original training data comprises:

determining the possibility of the training data in the first category being classified into the second category; and

selecting the training data of which the possibility exceeds a predetermined threshold value as the first training data.

3. The method of claim 1 , wherein, acquiring the first training data satisfying the predetermined condition from the original training data comprises:

determining the possibility of the training data in the second category being classified into the first category; and

4. The method of claim 2, wherein, each training data is classified into a category in a plurality of categories according to the relationship between its own eigenvalue and at least one threshold value or a threshold value range; and the at least one threshold value or the threshold value range is the basis for distinguishing the borders of different categories, wherein, determining the possibility of the training data in the first category being classified into the second category comprises:

determining, according to the relationship between the eigenvalue and the at least one threshold value or the threshold value range, the possibility of the training data in the first category being classified into the second category.

5. The method of claim 3, wherein, each training data is classified into a category in the plurality of categories according to the relationship between its own eigenvalue and at least one threshold value or a threshold value range; and the at least one threshold value or the threshold value range is the basis for distinguishing the borders of different categories, wherein, determining the possibility of the training data in the second category being classified into the first category comprises:

determining, according to the relationship between the eigenvalue and the at least one threshold value or the threshold value range, the possibility of the training data in the second category being classified into the first category.

6. The method of claim 1 , wherein, acquiring the first training data satisfying the predetermined condition from the original training data comprises:

extracting predetermined training data, of which the eigenvalue is in a predetermined range, from the training data in the first category as the first training data, wherein, the predetermined training data is the training data for which at least N training data among M training data of which the eigenvalues are in the same predetermined range as the eigenvalue of the predetermined training data belongs to the second category, wherein, M is an integer greater than or equal to 2, and N is an integer greater than or equal to 1.

7. The method of claim 1 , wherein, acquiring the first training data satisfying the predetermined condition from the original training data comprises:

extracting predetermined training data, of which the eigenvalue is in a predetermined range, from the training data in the second category as the first training data, wherein, the predetermined training data is the training data for which at least N training data among M training data of which the eigenvalues are in the same predetermined range as the eigenvalue of the predetermined training data belongs to the first category, wherein, M is an integer greater than or equal to 2, and N is an integer greater than or equal to 1.

8. The method of any one of claims 1 to 7, wherein, processing part or all of the content of the acquired first training data comprises:

extracting training data from the training data belonging to the first category according to a predetermined rule, or calculating the average value or median of all the training data belonging to the first category, as reference training data; and

processing, on the basis of the reference training data, part or all of the content of the acquired first training data.

9. The method of any one of claims 1 to 7, wherein, processing part or all of the content of the acquired first training data comprises:

extracting training data from the training data belonging to the second category according to a predetermined rule, or calculating the average value or median of all the training data belonging to the second category, as reference training data; and

10. The method of claim 1 , wherein, the original training data is image data, wherein, processing part or all of the content of the acquired first training data comprises:

modifying, according to the image data belonging to the first category or the image data belonging to the second category, at least one characteristic region of the image data which is the acquired first training data.

11. A device for detecting a workpiece, characterized in that, the device comprises:

an acquiring unit, acquiring original training data related to the workpiece, and acquiring a first training data satisfying a predetermined condition from the original training data, wherein, the original training data comprising training data which is pre-classified into a first category and training data which is pre-classified into a second category ; and a processing unit, processing part or all of the content of the acquired first training data, to generate new training data which is classified into the first category or the second category;

a training unit, using the original training data and the new training data to train a predetermined machine learning model so as to obtain a machine learning model which has been trained; and

a detecting unit, using the machine learning model which has been trained to detect the workpiece.

12. The device of claim 11 , wherein, the acquiring unit comprises:

a determining unit, determining the possibility of the training data in the first category being classified into the second category; and

a selecting unit, selecting the training data of which the possibility exceeds a predetermined threshold value as the first training data.

13. The device of claim 11 , wherein, the acquiring unit comprises:

a determining unit, determining the possibility of the training data in the second category being classified into the first category; and

14. The device of claim 12, wherein, each training data is classified into a category in a plurality of categories according to the relationship between its own eigenvalue and at least one threshold value or a threshold value range; and the at least one threshold value or the threshold value range is the basis for distinguishing the borders of different categories, wherein, the determining unit:

determines, according to the relationship between the eigenvalue and the at least one threshold value or the threshold value range, the possibility of the training data in the first category being classified into the second category.

15. The device of claim 13, wherein, each training data is classified into a category in a plurality of categories according to the relationship between its own eigenvalue and at least one threshold value or a threshold value range; and the at least one threshold value or the threshold value range is the basis for distinguishing the borders of different categories, wherein, the determining unit:

determines, according to the relationship between the eigenvalue and the at least one threshold value or the threshold value range, the possibility of the training data in the second category being classified into the first category.

16. The device of claim 11 , wherein, the acquiring unit:

extracts predetermined training data, of which the eigenvalue is in a predetermined range, from the training data in the first category as the first training data, wherein, the predetermined training data is the training data for which at least N training data among M training data of which the eigenvalues are in the same predetermined range as the eigenvalue of the predetermined training data belongs to the second category, wherein, M is an integer greater than or equal to 2, and N is an integer greater than or equal to 1.

17. The device of claim 11 , wherein, the acquiring unit:

extracts predetermined training data, of which the eigenvalue is in a predetermined range, from the training data in the second category as the first training data, wherein, the predetermined training data is the training data for which at least N training data among M training data of which the eigenvalues are in the same predetermined range as the eigenvalue of the predetermined training data belongs to the first category, wherein, M is an integer greater than or equal to 2, and N is an integer greater than or equal to 1.

18. The device of any one of claims 11 to 17, wherein, the processing unit:

extracts training data from the training data belonging to the first category according to a predetermined rule, or calculates the average value or median of all the training data belonging to the first category, as reference training data; and

processes, on the basis of the reference training data, part or all of the content of the acquired first training data.

19. The device of any one of claims 11 to 17, wherein, the processing unit:

extracts training data from the training data belonging to the second category according to a predetermined rule, or calculates the average value or median of all the training data belonging to the second category, as reference training data; and processes, on the basis of the reference training data, part or all of the content of the acquired first training data.

20. The device of claim 11 , wherein, the original training data is image data, wherein, the processing unit:

modifies, according to the image data belonging to the first category or the image data belonging to the second category, at least one characteristic region of the image data which is the acquired first training data.

21. A method for generating training data, characterized in that, the method comprises:

acquiring a first training data satisfying a predetermined condition; and processing part or all of the content of the acquired first training data, to generate new training data which is classified into a first category or a second category.

22. A device for generating training data, characterized in that, the device comprises:

an acquiring unit, acquiring a first training data satisfying a predetermined condition; and

a processing unit, processing part or all of the content of the acquired first training data, to generate new training data which is classified into a first category or a second category.

23. A system for detecting a workpiece, characterized in that, the system comprises:

a processing unit, performing the method of any one of claims 1 to 10; and

an output unit, outputting a detection result of the workpiece.

24. A program for detecting a workpiece, characterized in that the program, when being executed, performs the method of any one of claims 1 to 10.

25. A storage medium, stored thereon with a program, the program, when being executed, performs the method of any one of claims 1 to 10.