Learning completion model generation system for component image recognition and learning completion model generation method for component image recognition
阅读说明:本技术 元件图像识别用学习完成模型生成系统及元件图像识别用学习完成模型生成方法 (Learning completion model generation system for component image recognition and learning completion model generation method for component image recognition ) 是由 小林贵纮 鬼头秀一郎 横井勇太 于 2018-02-09 设计创作,主要内容包括:元件图像识别用学习完成模型生成系统以吸附于元件安装机(12)的吸嘴(31)的元件或安装于电路基板(11)的元件为拍摄对象,通过相机(18、22)来拍摄该拍摄对象并生成在进行图像识别时使用的学习完成模型,元件图像识别用学习完成模型生成系统具备取得在成为基准的元件的图像识别中使用的基准学习完成模型的计算机(23)。该计算机针对成为基准的元件具有预定的类似关系的元件的每个种类收集样本元件图像,并针对该元件的每个种类追加该样本元件图像作为上述基准学习完成模型的教师数据而进行再学习,从而针对该元件的每个种类生成用于该元件的图像识别的按元件类别学习完成模型。(A learning completion model generation system for component image recognition is provided with a computer (23) for acquiring a reference learning completion model used for image recognition of a component to be used as a reference, the learning completion model generation system being configured to take an image of a component attached to a suction nozzle (31) of a component mounting machine (12) or a component mounted on a circuit board (11) as an image pickup object and to generate a learning completion model used for image recognition by cameras (18, 22). The computer collects a sample element image for each type of element having a predetermined similarity relationship with respect to a reference element, adds the sample element image to each type of element as teacher data of the reference learning completion model, and re-learns the sample element image, thereby generating a learning completion model for each element type for image recognition of the element.)
1. A learning model generation system for recognizing component images, which takes a component adsorbed on a suction nozzle of a component mounting machine or a component mounted on a circuit board as an imaging object, images the imaging object by a camera and generates a learning model used for recognizing images,
the learning model generation system for component image recognition includes a computer for acquiring a reference learning model used for image recognition of a component to be a reference,
the computer collects a sample element image for each type of an element having a predetermined similarity relationship with the element serving as the reference, adds the sample element image to each type of the element as teacher data of the reference learning completion model, and re-learns the element, thereby generating a learning completion model for each element type for image recognition of the element.
2. The learning completion model generation system for component image recognition according to claim 1,
the element-by-element type learning completion model generated for each type of the element by the computer is included in the element shape data for image processing prepared for each type of the element.
3. The learning completion model generation system for component image recognition according to claim 1 or 2, wherein,
the element having a predetermined similar relationship with the element serving as the reference means an element having the same or similar shape although any one of the size, color, raw material, manufacturing company, and manufacturing lot is different from the element serving as the reference.
4. The learning completion model generation system for component image recognition according to any one of claims 1 to 3,
the computer collects, as the sample component image, an image obtained by photographing the photographic subject by a camera of a component mounter or a camera of an inspection machine in production.
5. The learning completion model generation system for component image recognition according to any one of claims 1 to 4,
the reference learning completion model and the component type learning completion model are learning completion models for determining whether the suction posture of the component sucked to the suction nozzle is normal suction or abnormal suction.
6. The learning completion model generation system for component image recognition according to any one of claims 1 to 4,
the reference learning completion model and the component-by-component learning completion model are learning completion models for determining whether or not a component attached to the suction nozzle is present.
7. The learning completion model generation system for component image recognition according to any one of claims 1 to 4,
the reference learning completion model and the learning completion model for each component type are learning completion models for determining whether or not a component mounted on the circuit board is present.
8. The learning completion model generation system for component image recognition according to any one of claims 1 to 7,
the computer transfers the generated component-by-component classification learning completion model to a component mounting machine or inspection machine using the component-by-component classification learning completion model.
9. A learning completion model generation method for component image recognition, which takes a component adsorbed on a suction nozzle of a component mounting machine or a component mounted on a circuit board as an imaging object, images the imaging object by a camera and generates a learning completion model used for image recognition,
the method for generating a learning completion model for component image recognition includes the steps of:
acquiring a reference learning completion model used for image recognition of a reference element;
collecting a sample element image for each kind of element having a predetermined similar relationship with the element that becomes the reference; and
the acquired sample element image is added to the teacher data of the reference learning completion model for each type of the element and relearning is performed, and a learning completion model for each element type for image recognition of the element is generated for each type of the element.
Technical Field
The present specification discloses a technique for a learning completion model generation system for component image recognition and a learning completion model generation method for component image recognition, in which a component attached to a suction nozzle of a component mounting machine or a component mounted on a circuit board is taken as an imaging target, and the imaging target is imaged by a camera to generate a learning completion model used for image recognition.
Background
As for the suction posture of the component sucked to the suction nozzle of the component mounting machine, the component is sucked horizontally if it is normally sucked, but there is a case where abnormal suction in which the component is sucked in an abnormal posture such as inclination occurs due to some cause. Since such abnormal suction causes a defective component mounting, a camera for picking up an image of a component sucked by a suction nozzle is mounted in a component mounting machine, and an image picked up by the camera is processed to determine whether the suction posture of the component is normal suction or abnormal suction, and the component determined to be abnormally sucked is discarded, and only the component determined to be normally sucked is mounted on a circuit board.
In the conventional general image processing, the normal suction/abnormal suction is discriminated using the image processing component shape data including the size of the component, but in the case where the component to be sucked to the suction nozzle is a minute component, it is sometimes difficult to discriminate the normal suction/abnormal suction by the image processing using the conventional image processing component shape data.
Therefore, as described in patent document 1 (japanese patent application laid-open No. 2008-130865), a learning completion model for discriminating normal suction/abnormal suction is generated in advance by using a mechanical learning method such as a neural network, a component image captured by a camera of a component mounter is processed during production, and normal suction/abnormal suction is discriminated by using the learning completion model.
Disclosure of Invention
Problems to be solved by the invention
For example, even if the elements have the same electrical specification, there may be a difference in size, color, material, manufacturing company, manufacturing lot, and the like, and the image recognition result may be different depending on the difference. However, in the case of elements having the same electrical specifications before the start of production, if the types of elements are refined in accordance with the size, color, material, manufacturing company, manufacturing lot, and the like and a learning completion model is to be generated by a mechanical learning method for all the types, a very large number of learning completion models must be generated, and the learning completion model generation operation takes a large number of man-hours.
Therefore, for elements of a type similar to the elements for which the learning completion model has already been generated, such as the shape, there are cases where the normal adsorption/abnormal adsorption is discriminated using the existing learning completion model, but in this case, the discrimination accuracy expected in production cannot be obtained in some cases. In this case, it is necessary to quickly generate a learning completion model dedicated to the element, but it takes a long time to generate the learning completion model from the beginning by a conventional method.
Means for solving the problems
In order to solve the above problems, a learning model creation system for component image recognition takes a component attached to a suction nozzle of a component mounter or a component mounted on a circuit board as an imaging target, the photographic subject is photographed by a camera and a learning completion model used in performing image recognition is generated, the learning model generation system for component image recognition includes a computer for acquiring a reference learning model used for image recognition of a component to be a reference, the computer collects a sample element image for each kind of element having a predetermined similar relationship with the element that becomes the reference, and the sample element image is added for each type of the element as teacher data of the reference learning completion model for relearning, thereby generating a learning completion model by component category for image recognition of the component for each category of the component.
In short, for an element having a predetermined similarity relationship with an element serving as a reference for generating a reference learning completion model, a sample element image is collected for each type of the element, and the sample element image is added as teacher data of the reference learning completion model for each type of the element and relearning is performed, thereby generating a learning completion model for each element type for image recognition of the element. In this way, it is possible to relatively easily generate a learning completion model for each component type for image recognition of a component having a predetermined similarity relationship with a component serving as a reference from the reference learning completion model.
Drawings
Fig. 1 is a block diagram showing a configuration example of a component mounting line according to an embodiment.
Fig. 2 is a front view illustrating normal adsorption.
Fig. 3 is a front view illustrating the inclined adsorption.
Fig. 4 is a flowchart showing a flow of processing of the component suction posture determination program.
Fig. 5 is a flowchart showing a flow of processing of the learning completion model generation program for each component type.
Detailed Description
One embodiment is explained below.
First, the structure of the
The
Each of the
The
A learning computer 23 is connected to the
The
On the other hand, the learning computer 23 executes a component-by-component learning completion model generation program shown in fig. 5, which will be described later, to classify and collect sample component images of normal suction/abnormal suction delivered from the
Here, the reference learning completion model is a learning completion model used for image recognition of a reference element, and may be generated by the learning computer 23 collecting a sample element image of normal adsorption/abnormal adsorption of the reference element as teacher data and performing learning by mechanical learning such as a neural network or a support vector machine, or may be a learning completion model generated by an external computer being taken into the learning computer 23. The reference element is not limited to a specific element, and an element in which a learning completion model is generated in advance may be referred to as a "reference element".
The
The
In this case, the elements having a predetermined similar relationship are, for example, elements whose shapes are the same or similar although any one of the size, color, raw material, manufacturing company, manufacturing lot, and the like of the elements is different. If the elements have a predetermined similarity relationship with each other, even if the image recognition of one element is performed using the learning completion model for the other element, the image recognition can be performed with a certain degree of accuracy (generally, at least the minimum accuracy required for production). In other words, if the image recognition of one element can be performed with a certain degree of accuracy using the learning completion model for the other element, it can be said that the two elements have a predetermined similarity relationship.
Next, the flow of processing of the component suction posture determination program of fig. 4 and the component type learning completion model generation program of fig. 5 will be described.
[ Process for determining the orientation of component suction ]
The component suction posture determination program of fig. 4 is executed by the
When the
On the other hand, if there is no learning completion model for the captured component among the learning completion models stored in the storage device for each type of component, the process proceeds to step 104, and a learning completion model for a component having a predetermined similarity relationship with the captured component is selected as the learning completion model for the current image recognition from among the learning completion models stored in the storage device for each type of component.
As described above, after the learning completion model for the current image recognition is selected, the process proceeds to step 105, the current captured image is processed by the image processing function of the
Then, the routine proceeds to step 106, where it is determined whether or not the determination result of the suction posture is normal suction, and if normal suction is performed, the routine proceeds to step 107, where the current captured image is transmitted to the learning computer 23 as a normally-sucked sample element image, and the routine is ended. On the other hand, if the determination result of the suction posture is not normal suction but abnormal suction, the routine proceeds to step 108, and the present captured image is transferred to the learning computer 23 as a sample element image of abnormal suction, and the routine is ended. Thus, the learning computer 23 collects sample component images of normal suction/abnormal suction from the
The
[ learning by component class to complete model creation program ]
The learning computer 23 repeatedly executes the learning completion model generation program for each component type shown in fig. 5 at predetermined cycles. When the learning computer 23 starts the program, first, in step 201, a sample component image of normal suction/abnormal suction is collected for each component type from the
Then, the process proceeds to step 203, and a sample component image obtained by imaging a component determined to be improperly mounted by the
Then, the process proceeds to step 204, where a mounting failure occurrence rate is calculated for each component type based on information of the inspection result obtained from the
On the other hand, if there is a component whose mounting failure occurrence rate exceeds the determination threshold, it is determined that the accuracy of image recognition is not secured for the component (generation of a learning completion model for each component type is necessary), and the process proceeds to step 206, where a sample component image collected for the component and normally adsorbed/abnormally adsorbed is added as teacher data of a reference learning completion model used for image recognition of the component and is relearned, thereby generating a learning completion model for each component type for the component. Then, the routine proceeds to step 207, and the generated learning completion model for each component type is transferred to the
According to the present embodiment described above, since the element-by-element type learning completion model for image recognition of the element is generated for each type of the element by collecting the sample element image for each type of the element and adding the sample element image for each type of the element as the teacher data of the reference learning completion model and performing relearning, with respect to the element having a predetermined similarity relationship with respect to the element serving as the reference for which the reference learning completion model is generated, the element-by-element type learning completion model for image recognition of the element having the predetermined similarity relationship with respect to the element serving as the reference can be relatively easily generated from the reference learning completion model, and the number of steps for generating the learning completion model can be reduced.
In addition, in the present embodiment, since the image processing component shape data prepared for each type of the component includes the component-by-component type learning completion model generated for each type of the component, the same image recognition using the component-by-component type learning completion model can be performed even in a component mounter of another component mounting line that can use the image processing component shape data, and there is an advantage that the production quality is improved and stabilized.
However, the learning completion model for each component type may not be associated with the image processing component shape data and may be managed separately.
In the present embodiment, the component held by the
However, the method of collecting the sample component images is not limited to the method of collecting the sample component images during production, and for example, the component
In the present embodiment, when a component having a mounting failure occurrence rate exceeding a predetermined determination threshold value is produced during production, a sample component image of normal suction/abnormal suction collected with respect to the component is added to teacher data of a reference learning completion model used for image recognition of the component and is relearned, and a component-by-component type learning completion model for the component is generated and transmitted to the
However, the generation of the learning completion model for each component type may be performed before the start of production or after the end of production. Alternatively, the learning completion model by element type may be generated at a point in time when the number of collected normal adsorption/abnormal adsorption sample element images exceeds a predetermined number.
The learning completion model of the present embodiment is a learning completion model for determining whether the suction posture of the component sucked by the
The
It is needless to say that the present invention can be implemented by changing the configuration of the
Description of the reference numerals
10. A component mounting production line; 11. a circuit substrate; 12. a component mounting machine; 14. an inspection machine; 17. a control device of the component mounting machine; 18. a camera for shooting the component; 19. a feeder; 20. a control device of the inspection machine; 21. a computer for production management; 22. an inspection camera; 23. a learning computer; 31. a suction nozzle.
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