CN109765238A - A kind of product quality detection method of mask fully-automatic production detection device - Google Patents
A kind of product quality detection method of mask fully-automatic production detection device Download PDFInfo
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Abstract
A kind of product quality detection method of mask fully-automatic production detection device of the present invention, the mask fully-automatic production detection device includes that sequentially connected ontology machine, ear wearing machine, mask automatic production line, mask automatic detecting machine and mask collect counting apparatus automatically；The mask automatic detecting machine is equipped with high-definition camera and processing chip；Detection method includes the following steps for the product quality: treating detection mask using high-definition camera and takes pictures；Extract the features such as the profile in the mask image to be detected；It extracts in the edge image, the outer peripheral length and width of mask to be detected, ear strip length, bridge of the nose line length and the bridge of the nose line information such as at a distance from mask top edge；Judge whether the outer peripheral length of the mask to be detected and wide angulation are 88 ° -90 °；The total deviation rate P of mask is calculated according to following formula；Judgement, if so, being determined as the product of passing.The detection method is adapted to the display case of mask different location, and detection accuracy is more preferable.
Description
Technical field
The present invention relates to mask detection field, the product quality of especially a kind of mask fully-automatic production detection device is detected
Method.
Background technique
In general, the detection of mask manufacturing quality relies on artificial detection more in mask production process, small part be will use certainly
The method of dynamicization processing, but existing automatic processing method are mostly that the method compared using shape is detected, will and compare
The identical product of image is determined as qualification, is not overlapped, and is determined as unqualified.This judgment method precision is lower, and implement when pair
The difficulty of image procossing is larger, because the placement position of mask can be very different in actual production, is carrying out shape comparison
When, there can be very big technical difficulty, increase drain on manpower and material resources.
It is, therefore, desirable to provide a kind of mask quality determining method for being adapted to various placement positions.
Summary of the invention
In view of technical problem present in background technique, the purpose of the present invention is to overcome the shortcomings of the existing technology, provides
A kind of mask quality determining method being adapted to various placement positions.
In order to solve the above technical problems, present invention employs following technical measures:
A kind of product quality detection method of mask fully-automatic production detection device, the mask fully-automatic production detection are set
Standby includes that sequentially connected ontology machine, ear wearing machine, mask automatic production line, mask automatic detecting machine and mask collect points automatically
Machine；The mask automatic detecting machine is equipped with high-definition camera and processing chip；The high-definition camera is for treating detection mask
Carry out Image Acquisition；The processing chip is for handling described image and judging whether the mask to be detected is qualified；
Detection method includes the following steps for the product quality:
S1, using high-definition camera treat detection mask take pictures, obtain mask image to be detected；
S2, the features such as the profile in the mask image to be detected are extracted using the method that gradient detects, obtains edge
Image；
S3, it extracts in the edge image, the outer peripheral length and width of mask to be detected, ear strip length, bridge of the nose line length and nose
The beam line information such as at a distance from mask top edge；
S4, judge whether the outer peripheral length of the mask to be detected and wide angulation are 88 ° -92 °, if so, into
Step S5, if it is not, being then determined as non-qualifying product；
S5, the total deviation rate that mask is calculated according to following formula:
In formula, P is the total deviation rate of mask, and Li1, Wi, Li2, Li3, Hi are followed successively by the outer edge of preset standard mask
Length, outer peripheral width, ear strip length, bridge of the nose line length and bridge of the nose line at a distance from mask top edge, corresponding L1, W, L2,
L3, H are respectively outer peripheral length, outer peripheral width, ear strip length, bridge of the nose line length and the bridge of the nose line and mask of mask to be detected
The distance of top edge.
S6, judge P≤A, if so, being determined as the product of passing, if it is not, being then determined as non-qualifying product, A is product overall quality
Qualified judgment threshold.
As a further improvement, the value range of A is 3%-5%.
As a further improvement, the value of A is 3%.
As a further improvement, further comprising between the step S5 and step S6:
S51: judgementIf so, S52 is entered step, if it is not, being then determined as non-qualifying product；
S52: judgementIf so, S53 is entered step, if it is not, being then determined as non-qualifying product；
S53: judgementIf so, S54 is entered step, if it is not, being then determined as non-qualifying
Product；
S54: judgementIf so, S55 is entered step, if it is not, being then determined as non-qualifying product；
S55: judgementIf so, S6 is entered step, if it is not, being then determined as non-qualifying product；
Wherein, B is the qualified threshold value of product individual event parameter.
As a further improvement, the value range of B is 1%-1.5%.
As a further improvement, the value of B is 1%.
As a further improvement, the step S2 is specifically included:
S21, binary conversion treatment is carried out to the mask image to be detected；
S22, first time noise reduction process is carried out to the image after binary conversion treatment；
S23, expansion process is carried out to the image after first time noise reduction process；
S24, second of noise reduction process is carried out to the image after expansion process；
S25, corrosion treatment is carried out to the image after second of noise reduction process；
S26, third time noise reduction process is carried out to the image after corrosion treatment；
S27, gradient detection is carried out to the image after third time noise reduction process and extracts profile information therein.
Compared with prior art, the invention has the following advantages that
1, a kind of product quality detection method of mask fully-automatic production detection device of the present invention is not needed by carrying out shape
Shape comparison determines whether qualification, avoids influence of the placement position to testing result.
2, a kind of product quality detection method of mask fully-automatic production detection device of the present invention, it is total to be respectively provided with product
The judgment threshold of weight qualification and the qualified threshold value of product individual event parameter, the two threshold values mutually restrict, can effectively prevent working as
In product individual event parameter one it is excessive, when other equal very littles, the total deviation value of product is made to reach criterion of acceptability and product individual event
To generate erroneous judgement in the case that parameter is qualified, but population deviation value is excessive.And keep detection accuracy higher.
3, a kind of product quality detection method of mask fully-automatic production detection device of the present invention, product overall quality are qualified
Judgment threshold and the qualified threshold value of product individual event parameter can freely be arranged, to adapt to the productions that different manufactures require.
4, a kind of product quality detection method of mask fully-automatic production detection device of the present invention is carrying out contours extract
Before, noise reduction-expansion-noise reduction-corrosion-noise reduction operation is successively carried out, the noise in image can be made to substantially reduce, and pass through shape
State processing, precision is higher when extracting edge contour, and error is avoided to become larger.
Detailed description of the invention
Attached drawing 1 is a kind of flow chart of the product quality detection method of mask fully-automatic production detection device of the present invention.
Attached drawing 2 is that a kind of mask of the product quality detection method of mask fully-automatic production detection device of the present invention is full-automatic
Produce the schematic diagram of detection device.
Attached drawing 3 is step S5 and step in a kind of product quality detection method of mask fully-automatic production detection device of the present invention
Rapid S6 intermediate step flow chart.
Attached drawing 4 is the tool of step S2 in a kind of product quality detection method of mask fully-automatic production detection device of the present invention
Body flow chart.
Main element symbol description
Ontology machine 1, ear wearing machine 2, mask automatic production line 3, mask automatic detecting machine 4 and mask collect automatically counting apparatus 5,
High-definition camera 41, processing chip 42,
Specific embodiment
To keep the purposes, technical schemes and advantages of embodiment of the present invention clearer, implement below in conjunction with the present invention
The technical solution in embodiment of the present invention is clearly and completely described in attached drawing in mode, it is clear that described reality
The mode of applying is some embodiments of the invention, rather than whole embodiments.Based on the embodiment in the present invention, ability
Domain those of ordinary skill every other embodiment obtained without creative efforts, belongs to the present invention
The range of protection.Therefore, the detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit below and is wanted
The scope of the present invention of protection is sought, but is merely representative of selected embodiment of the invention.Based on the embodiment in the present invention,
Every other embodiment obtained by those of ordinary skill in the art without making creative efforts belongs to this
Invent the range of protection.
In the description of the present invention, it is to be understood that, the orientation or positional relationship of the instructions such as term " on ", "lower" is base
In orientation or positional relationship shown in the drawings, it is merely for convenience of description of the present invention and simplification of the description, rather than indication or suggestion
Signified equipment or element must have a particular orientation, be constructed and operated in a specific orientation, therefore should not be understood as to this
The limitation of invention.
In addition, term " first ", " second " are used for descriptive purposes only and cannot be understood as indicating or suggesting relative importance
Or implicitly indicate the quantity of indicated technical characteristic.Define " first " as a result, the feature of " second " can be expressed or
Implicitly include one or more of the features.In the description of the present invention, the meaning of " plurality " is two or more,
Unless otherwise specifically defined.
In the present invention unless specifically defined or limited otherwise, term " installation ", " connected ", " connection ", " fixation " etc.
Term shall be understood in a broad sense, for example, it may be being fixedly connected, may be a detachable connection, or integral；It can be mechanical connect
It connects, is also possible to be electrically connected；It can be directly connected, can also can be in two elements indirectly connected through an intermediary
The interaction relationship of the connection in portion or two elements.It for the ordinary skill in the art, can be according to specific feelings
Condition understands the concrete meaning of above-mentioned term in the present invention.
Present invention is further described in detail with specific embodiment with reference to the accompanying drawing:
Referring to FIG. 1, a kind of product quality detection method of mask fully-automatic production detection device is described in embodiment
Mask fully-automatic production detection device includes that sequentially connected ontology machine 1, ear wearing machine 2, mask automatic production line 3, mask are automatic
Detection machine 4 and mask collect counting apparatus 5 automatically；The mask automatic detecting machine 4 is equipped with high-definition camera 41 and processing chip 42；
The high-definition camera 41 carries out Image Acquisition for treating detection mask；
The processing chip 42 is used to judge whether the mask to be detected is qualified using the product quality detection method；
Detection method includes the following steps for the product quality:
S1, using high-definition camera treat detection mask take pictures, obtain mask image to be detected；
S2, the features such as the profile in the mask image to be detected are extracted using the method that gradient detects, obtains edge
Image；
S3, it extracts in the edge image, the outer peripheral length and width of mask to be detected, ear strip length, bridge of the nose line length and nose
The beam line information such as at a distance from mask top edge；
S4, judge whether the outer peripheral length of the mask to be detected and wide angulation are 88 ° -92 °, if so, into
Step S5, if it is not, being then determined as non-qualifying product；
S5, the total deviation rate that mask is calculated according to following formula:
In formula, P is the total deviation rate of mask, and Li1, Wi, Li2, Li3, Hi are followed successively by the outer edge of preset standard mask
Length, outer peripheral width, ear strip length, bridge of the nose line length and bridge of the nose line at a distance from mask top edge, corresponding L1, W, L2,
L3, H are respectively outer peripheral length, outer peripheral width, ear strip length, bridge of the nose line length and the bridge of the nose line and mask of mask to be detected
The distance of top edge.
S6, judge P≤A, if so, being determined as the product of passing, if it is not, being then determined as non-qualifying product, A is product overall quality
Qualified judgment threshold, the value range of A are 3%-5%, and in the present embodiment, the value of A is 3%.
Further comprise between the step S5 and step S6:
S51: judgementIf so, S52 is entered step, if it is not, being then determined as non-qualifying product；
S52: judgementIf so, S53 is entered step, if it is not, being then determined as non-qualifying product；
S53: judgementIf so, S54 is entered step, if it is not, being then determined as non-qualifying
Product；
S54: judgementIf so, S55 is entered step, if it is not, being then determined as non-qualifying
Product；
S55: judgementIf so, S6 is entered step, if it is not, being then determined as non-qualifying product；
Wherein, B is the qualified threshold value of product individual event parameter, and the value range of B is 1%-1.5%, in the present embodiment, B's
Value is 1%.
The step S2 is specifically included:
S21, binary conversion treatment is carried out to the mask image to be detected；
S22, first time noise reduction process is carried out to the image after binary conversion treatment；
S23, expansion process is carried out to the image after first time noise reduction process；
S24, second of noise reduction process is carried out to the image after expansion process；
S25, corrosion treatment is carried out to the image after second of noise reduction process；
S26, third time noise reduction process is carried out to the image after corrosion treatment；
S27, gradient detection is carried out to the image after third time noise reduction process and extracts profile information therein.
The present embodiment is when carrying out mask detection, because directly being calculated by detecting the data of edge isoline,
It does not need to determine whether qualification by carrying out shape comparison, avoids influence of the placement position to testing result.
Meanwhile when being made whether qualified differentiation, it is respectively provided with judgment threshold and the production of product overall quality qualification
The qualified threshold value of product individual event parameter, the two threshold values can be restricted mutually, can be effectively prevent when a mistake in product individual event parameter
Greatly, when other equal very littles, make that the total deviation value of product reaches criterion of acceptability and product individual event parameter is qualified, but it is overall partially
To generate erroneous judgement in the case that difference is excessive.And keep detection accuracy higher.And the judgment threshold of product overall quality qualification and
The qualified threshold value of product individual event parameter can be freely arranged, to adapt to the production that different manufactures require.
The present embodiment successively carries out noise reduction-expansion-noise reduction-corrosion-noise reduction operation before carrying out contours extract, can be with
Substantially reduce the noise in image, and by Morphological scale-space, precision is higher when extracting edge contour, and error is avoided to become
Greatly.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all in essence of the invention
Within mind and principle, any modification, equivalent substitution, improvement and etc. done be should be included within the scope of the present invention.
Claims (7)
1. a kind of product quality detection method of mask fully-automatic production detection device, the mask fully-automatic production detection device
Points are collected automatically including sequentially connected ontology machine, ear wearing machine, mask automatic production line, mask automatic detecting machine and mask
Machine；The mask automatic detecting machine is equipped with high-definition camera and processing chip；The high-definition camera is for treating detection mask
Carry out Image Acquisition；The processing chip is for handling described image and judging whether the mask to be detected is qualified；
Detection method includes the following steps for the product quality:
S1, using high-definition camera treat detection mask take pictures, obtain mask image to be detected；
S2, the contour feature in the mask image to be detected is extracted using the method that gradient detects, obtains edge image；
S3, it extracts in the edge image, the outer peripheral length and width of mask to be detected, ear strip length, bridge of the nose line length and bridge of the nose line
With the range information of mask top edge；
S4, judge whether the outer peripheral length of the mask to be detected and wide angulation are 88 ° -92 °, if so, entering step
S5, if it is not, being then determined as non-qualifying product；
S5, the total deviation rate that mask is calculated according to following formula:
In formula, P be mask total deviation rate, Li1, Wi, Li2, Li3, Hi be followed successively by preset standard mask outer peripheral length,
Outer peripheral width, ear strip length, bridge of the nose line length and bridge of the nose line are at a distance from mask top edge, and corresponding L1, W, L2, L3, H points
It Wei not the outer peripheral length of mask to be detected, outer peripheral width, ear strip length, bridge of the nose line length and bridge of the nose line and mask top edge
Distance；
S6, judge P≤A, if so, being determined as the product of passing, if it is not, being then determined as non-qualifying product, A is that product overall quality is qualified
Judgment threshold.
2. product quality detection method according to claim 1, which is characterized in that the value range of A is 3%-5%.
3. product quality detection method according to claim 2, which is characterized in that the value of A is 3%.
4. product quality detection method according to claim 1, which is characterized in that between the step S5 and step S6 into
One step includes:
S51: judgementIf so, S52 is entered step, if it is not, being then determined as non-qualifying product；
S52: judgementIf so, S53 is entered step, if it is not, being then determined as non-qualifying product；
S53: judgementIf so, S54 is entered step, if it is not, being then determined as non-qualifying product；
S54: judgementIf so, S55 is entered step, if it is not, being then determined as non-qualifying product；
S55: judgementIf so, S6 is entered step, if it is not, being then determined as non-qualifying product；
Wherein, B is the qualified threshold value of product individual event parameter.
5. product quality detection method according to claim 4, which is characterized in that the value range of B is 1%-1.5%.
6. product quality detection method according to claim 4, which is characterized in that the value of B is 1%.
7. product quality detection method according to claim 1, which is characterized in that the step S2 is specifically included:
S21, binary conversion treatment is carried out to the mask image to be detected；
S22, first time noise reduction process is carried out to the image after binary conversion treatment；
S23, expansion process is carried out to the image after first time noise reduction process；
S24, second of noise reduction process is carried out to the image after expansion process；
S25, corrosion treatment is carried out to the image after second of noise reduction process；
S26, third time noise reduction process is carried out to the image after corrosion treatment；
S27, gradient detection is carried out to the image after third time noise reduction process and extracts profile information therein.
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Cited By (6)
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| CN111369551A (en) * | 2020-03-12 | 2020-07-03 | 广东利元亨智能装备股份有限公司 | Mask ear band welding detection method |
| CN111642833A (en) * | 2020-04-22 | 2020-09-11 | 王峰 | Medical mask production equipment and quality rapid detection method of medical mask |
| CN112070738A (en) * | 2020-09-03 | 2020-12-11 | 广东高臻智能装备有限公司 | Method and system for detecting nose bridge of mask |
| CN112083009A (en) * | 2020-07-23 | 2020-12-15 | 广州超音速自动化科技股份有限公司 | Method and device for detecting quality of mask through video and storage medium |
| CN112215828A (en) * | 2020-10-20 | 2021-01-12 | 广东高臻智能装备有限公司 | Automatic mask cutting position adjusting method and system based on intelligent visual detection |
| CN115876804A (en) * | 2023-02-20 | 2023-03-31 | 深圳市信仪旺实业有限公司 | Visual detection method and system for defects of mask |
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Application publication date: 20190517 |