CN112362674A - Infusion bag foreign matter identification method and detection method based on artificial intelligence vision technology - Google Patents
Infusion bag foreign matter identification method and detection method based on artificial intelligence vision technology Download PDFInfo
- Publication number
- CN112362674A CN112362674A CN202011269097.8A CN202011269097A CN112362674A CN 112362674 A CN112362674 A CN 112362674A CN 202011269097 A CN202011269097 A CN 202011269097A CN 112362674 A CN112362674 A CN 112362674A
- Authority
- CN
- China
- Prior art keywords
- infusion bag
- foreign matter
- artificial intelligence
- vision technology
- infusion
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000001802 infusion Methods 0.000 title claims abstract description 132
- 238000000034 method Methods 0.000 title claims abstract description 28
- 238000005516 engineering process Methods 0.000 title claims abstract description 27
- 238000013473 artificial intelligence Methods 0.000 title claims abstract description 17
- 238000001514 detection method Methods 0.000 title abstract description 19
- 238000012545 processing Methods 0.000 claims abstract description 20
- 239000007788 liquid Substances 0.000 claims abstract description 16
- 239000003814 drug Substances 0.000 claims abstract description 12
- 238000007781 pre-processing Methods 0.000 claims abstract description 11
- 238000012805 post-processing Methods 0.000 claims abstract description 7
- 230000033001 locomotion Effects 0.000 claims abstract description 6
- 230000000877 morphologic effect Effects 0.000 claims abstract description 6
- 239000012535 impurity Substances 0.000 claims description 13
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 12
- 238000004458 analytical method Methods 0.000 claims description 4
- 230000011218 segmentation Effects 0.000 claims description 3
- 230000001629 suppression Effects 0.000 claims description 3
- 230000000007 visual effect Effects 0.000 abstract description 9
- 239000002245 particle Substances 0.000 abstract description 7
- 238000005286 illumination Methods 0.000 description 12
- 239000011521 glass Substances 0.000 description 9
- 238000004519 manufacturing process Methods 0.000 description 6
- 239000004033 plastic Substances 0.000 description 6
- 210000004209 hair Anatomy 0.000 description 5
- 238000007689 inspection Methods 0.000 description 5
- 239000000835 fiber Substances 0.000 description 4
- 238000003384 imaging method Methods 0.000 description 4
- 239000000463 material Substances 0.000 description 3
- 230000036544 posture Effects 0.000 description 3
- 230000007547 defect Effects 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000018109 developmental process Effects 0.000 description 2
- 229940079593 drug Drugs 0.000 description 2
- 230000002708 enhancing effect Effects 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 239000005022 packaging material Substances 0.000 description 2
- 238000004806 packaging method and process Methods 0.000 description 2
- 238000003908 quality control method Methods 0.000 description 2
- XAGFODPZIPBFFR-UHFFFAOYSA-N aluminium Chemical compound [Al] XAGFODPZIPBFFR-UHFFFAOYSA-N 0.000 description 1
- 229910052782 aluminium Inorganic materials 0.000 description 1
- 208000003464 asthenopia Diseases 0.000 description 1
- 230000001580 bacterial effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 229960000074 biopharmaceutical Drugs 0.000 description 1
- 238000003763 carbonization Methods 0.000 description 1
- 239000003795 chemical substances by application Substances 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000003708 edge detection Methods 0.000 description 1
- 230000008451 emotion Effects 0.000 description 1
- 238000005538 encapsulation Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 238000009776 industrial production Methods 0.000 description 1
- 238000002347 injection Methods 0.000 description 1
- 239000007924 injection Substances 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 244000005700 microbiome Species 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000003909 pattern recognition Methods 0.000 description 1
- 230000002085 persistent effect Effects 0.000 description 1
- 239000000825 pharmaceutical preparation Substances 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 238000011895 specific detection Methods 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 238000003756 stirring Methods 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 230000001360 synchronised effect Effects 0.000 description 1
- 210000003462 vein Anatomy 0.000 description 1
- 239000012905 visible particle Substances 0.000 description 1
- 238000011179 visual inspection Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/90—Investigating the presence of flaws or contamination in a container or its contents
- G01N21/9081—Inspection especially designed for plastic containers, e.g. preforms
Landscapes
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Investigating Materials By The Use Of Optical Means Adapted For Particular Applications (AREA)
Abstract
The invention discloses an infusion bag foreign matter identification method and a detection method based on an artificial intelligence vision technology, and belongs to the technical field of infusion bag detection. The method comprises the following steps: step 1: pretreating the infusion bag; step 2: carrying out image preprocessing on the infusion bag; and step 3: carrying out image post-processing on the infusion bag; an infusion bag containing foreign matter is identified. According to the invention, a continuous frame image of the liquid medicine is obtained through a high-speed CCD industrial camera, then the motion characteristics and morphological characteristics of the particle insoluble foreign matters in the liquid are combined, the relevant invariant characteristics of the particle insoluble foreign matters in the image are excavated, the image processing algorithm is applied to reliably identify the foreign matters or the interference caused by the outside, and the visual online automatic identification of the visible foreign matters in the soft infusion bag is realized.
Description
Technical Field
The invention belongs to the field of infusion bag detection, and particularly relates to an infusion bag foreign matter identification method and an infusion bag foreign matter detection method based on an artificial intelligent vision technology.
Background
With the development of the society and the improvement of the living standard of people, the quality requirement of people on products is higher and higher, the quality control of the products is tighter and tighter, and more manpower is required to be put into the quality control of the products in larger-scale production. Traditional manual screening, not only efficiency is extremely low, and administrative cost, the human cost that bring from this rises sharply, and automatic detection replaces manual detection, and is especially important. The Kolun pharmaceutical industry is used as a leading enterprise of the biopharmaceutical industry, the product specifications are numerous, the annual output is extremely high, and the medicament needs to be detected in each bag/bottle. Therefore, in order to increase the weight of products, the Koran pharmaceutical industry is supposed to construct a product quality system service platform based on a visual technology, an intelligent light inspection machine is used as a core carrier, liquid medicine detection of a soft infusion bag is relied on, and the product quality system service platform is expanded to a shared quality service platform in multiple industries and multiple fields.
As an important infusion agent packaging mode, the soft infusion bag has great potential competitiveness due to the characteristics of material saving, small volume and low cost. The development of medical packaging encourages the use of new and high quality packaging materials for packaging materials, and requires the gradual adoption of high quality glass bottles and plastic bottles, soft bags instead of the traditionally used glass infusion bottles. According to statistics, in developed countries such as Europe and America in 2012, the infusion in plastic bottles and soft bags accounts for about 80%, the infusion in glass bottles accounts for 20%, and China mainly uses the infusion in glass bottles, and accounts for 70% of market share. It is predicted that the future large infusion bag will gradually replace the large infusion bottle, and will occupy more market share.
Although enterprises can avoid impurities from being mixed in the production process of the pharmaceutical preparation, glass chips, rubber plug chips, aluminum chips, color dots, white blocks, fibers, hairs and other small insoluble foreign matters are mixed in the finished product of the soft bag injection due to the fact that the filtering material is damaged, the inner wall of a pipeline is aged, carbonization is performed during encapsulation, operating personnel does not follow a purification program or the design layout of a factory building is unreasonable, particles of wall materials fall off, and the pipeline of a non-clean area and a clean area is not tightly connected. The white glass chips and plastic chips are the most. These insoluble foreign bodies from different sources often carry a huge number of bacterial microorganisms, which severely contaminate the drug. Moreover, if these tiny foreign substances are injected into the vein along with the medicine, they cause serious and persistent harm to the human body.
At present, domestic enterprises adopt manual light inspection methods for the detection of soft infusion bags. The method requires workers to slightly rotate under the light with the illuminance of 1000-4000 Lx, and judge whether visible foreign matters such as glass chips, fibers, color points, hairs and the like exist in the liquid medicine completely by naked eyes after turning over the container, and the inherent defects of the manual lamp inspection method are as follows:
1) the detection precision is low, and the maximum visible particle diameter of human eyes is 50-100 mu m.
2) The detection method and the standard are not uniform, and the repeatability is poor.
3) The production efficiency is low.
4) Workers are easy to have asthenopia and are influenced by thought and emotion, so that the false detection rate and the omission rate are high.
Disclosure of Invention
The invention aims to provide an infusion bag foreign matter identification method and an infusion bag foreign matter detection method based on an artificial intelligence vision technology, and aims to solve the problems in the background technology.
In order to achieve the purpose, the invention adopts the following technical scheme:
an infusion bag foreign matter identification method based on an artificial intelligence vision technology comprises the following steps:
step 1: pretreating the infusion bag;
step 2: carrying out image preprocessing on the infusion bag;
and step 3: carrying out image post-processing on the infusion bag; an infusion bag containing foreign matter is identified.
In the step 1, the pretreatment of the infusion bag comprises: removing air bubbles in the infusion bag, removing water on the surface of the infusion bag and moving impurities in the infusion bag.
Further, the removing air bubbles and moving impurities in the infusion bag comprises: the infusion bag is grabbed to a corresponding station through the manipulator and is displayed and suspended, the infusion bag is hit by the connecting rod carried by the cylinder moving back and forth, bubbles in the infusion bag are removed, and impurities in the infusion bag move.
Further, infusion bag surface is dispelled water, includes: and removing the water stain on the surface of the infusion bag by using an air knife.
In the step 2, the image preprocessing is performed on the infusion bag, and the method comprises the following steps:
calibrating an effective area of the infusion bag;
acquiring continuous frame images of the liquid medicine through a CCD industrial camera;
calculating a frame difference;
contrast enhancement processing;
self-adaptive binarization processing;
and noise analysis and suppression processing.
In the step 3, the infusion bag is subjected to image post-processing; the method for identifying the infusion bag containing the foreign matters comprises the following steps:
carrying out foreign matter, noise point and deviation segmentation on the infusion bag subjected to image preprocessing;
searching foreign matters and noise points;
and (5) extracting features.
Further, the features include statistical features, kinematic features, morphological features.
Compared with the prior art, the invention has the beneficial effects that:
the core of the invention is a machine vision technology, a continuous frame image of the liquid medicine is obtained by a high-speed CCD industrial camera, then the relevant invariant characteristics of the particle insoluble foreign matters in the liquid are excavated by combining the motion characteristics and the morphological characteristics of the particle insoluble foreign matters in the liquid, the foreign matters or the interference caused by the outside is reliably identified by applying an image processing algorithm, and the visual online automatic identification of the visible foreign matters in the soft infusion bag is realized.
Drawings
Fig. 1 is a flow chart of an infusion bag foreign matter identification method based on an artificial intelligence vision technology.
Fig. 2 is a specific flowchart of an intelligent image processing and decision algorithm of the infusion bag foreign matter identification method based on the artificial intelligence vision technology.
FIG. 3 is an intelligent recognition follow-on grab system of an embodiment of the present invention.
Detailed Description
The present invention will be further described with reference to the following examples, which are intended to illustrate only some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, other embodiments used by those skilled in the art without any creative effort belong to the protection scope of the present invention.
Example 1:
as shown in fig. 1, an infusion bag foreign matter identification method based on an artificial intelligence vision technology includes the following steps:
step 1: pretreating the infusion bag; pretreating an infusion bag, comprising: removing air bubbles in the infusion bag, removing water on the surface of the infusion bag and moving impurities in the infusion bag. The bubble in the infusion bag is got rid of and let impurity in the infusion bag move up, include: the infusion bag is grabbed to a corresponding station through the manipulator and is displayed and suspended, the infusion bag is hit by the connecting rod carried by the cylinder moving back and forth, bubbles in the infusion bag are removed, and impurities in the infusion bag move. Infusion bag surface is dispelled water includes: and removing the water stain on the surface of the infusion bag by using an air knife.
Infusion bag passes through the conveyer belt pan feeding, and intelligent recognition follows grasping system and snatchs infusion bag and carries out the array and hang above the circular shape carousel, sets up preliminary treatment station and visual identification station on the carousel. Before visual identification of the infusion bag, pretreatment operations including removal of bubbles in the infusion bag, water removal on the surface of the infusion bag and bag pinching to move impurities in liquid are firstly carried out on the infusion bag.
Infusion bag discernment needs to grab the infusion bag on the conveyer belt to corresponding station and discern, and basic operation flow is as shown in the following figure. The infusion bag is grabbed by adopting a rapid robot DLETA, the designed working speed is 75 bags/minute, namely one bag is grabbed every 0.8 s. The cylinder which moves back and forth quickly is adopted to carry the connecting rod to strike the infusion bag, so that bubbles in the infusion bag are removed, and the recognition interference is reduced.
Because the infusion bag is disinfected at high temperature before coming, a large amount of liquid traces exist on the surface of the infusion bag, and the water traces on the surface of the infusion bag need to be removed by an air knife. Since the vision recognition uses a kinematic recognition technique, it is necessary to stir water in the infusion bag before imaging the infusion bag and to move impurities possibly present in the infusion bag with the movement of the liquid, and therefore, it is necessary to perform a bag pinching operation before imaging the infusion bag. After the identification of the white background and the black background, whether impurities exist in the infusion bag or not is determined, and the good products and the non-good products of the infusion bag need to be sorted.
Step 2: carrying out image preprocessing on the infusion bag; the image preprocessing is carried out on the infusion bag, and the image preprocessing comprises the following steps:
calibrating an effective area of the infusion bag; the desired detection area is obtained.
Acquiring continuous frame images of the liquid medicine through a CCD industrial camera;
calculating a frame difference; the stationary interfering background is removed.
Contrast enhancement processing; so that the foreign matters in the infusion bag are more obvious.
Self-adaptive binarization processing; further enhancing the contrast, eliminating part of noise and enhancing the property of foreign matters.
And noise analysis and suppression processing. Removing a part of the noise left.
And step 3: carrying out image post-processing on the infusion bag; an infusion bag containing foreign matter is identified.
Carrying out image post-processing on the infusion bag; the method for identifying the infusion bag containing the foreign matters comprises the following steps:
carrying out foreign matter, noise point and deviation segmentation on the infusion bag subjected to image preprocessing;
searching foreign matters and noise points;
and (5) extracting features. The features include statistical features, kinematic features, morphological features.
The machine vision technology is an emerging technology which utilizes computer vision to replace artificial vision and provides product quality. Machine vision is a new artificial intelligence technology, comprehensively applies image processing and analysis, pattern recognition, artificial intelligence, precise instruments and other technologies, and has the advantages of high speed, high precision, non-contact and the like. The method is applied to the industrial field, can effectively overcome the defect of manpower, and improves the automation degree and the production efficiency of factory production. A general machine vision system can be divided into four parts, namely a detection system, an execution system, a control system and an image processing system.
Compared with the common computer vision or digital image processing, the industrial machine vision system has the following characteristics due to the clear engineering application background:
1) the working environment is known. The work environment of an industrial-oriented automated vision system is typically a particular industrial production line. The conditions of illumination, background, shooting position, etc. of the system need to be determined in advance before detection.
2) The system has special application. Automatic visual inspection systems basically target a specific type of inspection target. The corresponding algorithm is also designed for a certain type of specific detection target, and the software algorithm has high specificity.
3) The target type is determined and has obvious characteristics. Taking visible foreign matters in the soft infusion bag as an example, the common visible foreign matters include plastic scraps, fibers, hairs and color spots. Before detection, the characteristics of the target need to be deeply analyzed to design an automatic algorithm.
4) Industry-oriented automated vision systems also need to be adapted to the specific requirements of the industrial application environment. This dictates that the industrial vision system will include more subsystems and actuators in addition to the inspection system, control system and image processing system, for example: a communication system synchronous with the production line, auxiliary systems for distance measurement, speed measurement and the like, and a result feedback system for marking, alarming or mechanical operation and the like.
5) In some occasions with large data volume and high real-time requirement, the processing speed of a general-purpose computer cannot be sufficient, and a real-time image processing system based on an embedded processor is required. Such as using a digital signal processor DSP or the like.
The core of the project is a machine vision technology, a continuous frame image of a liquid medicine is obtained through a high-speed CCD industrial camera, then the relevant invariant characteristics of the particle insoluble foreign matters in the liquid are excavated by combining the motion characteristics and the morphological characteristics of the particle insoluble foreign matters in the liquid, the image processing algorithm is applied to reliably identify the foreign matters or the interference caused by the outside, the visual online automatic identification of the visible foreign matters in the soft infusion bag is realized, and the flow chart is shown in figure 1.
The intelligent identification following grabbing technology is formed by integrating a robot and a visual guide technology, infusion bags on a conveying belt can be followed in real time to be grabbed, ordered infusion bag hanging arrangement is achieved, and subsequent visual identification is facilitated. The core of the technology is that the robot automatically grabs and the vision guide is effectively combined, the robot grabs and adopts a high-speed DELTA robot, the infusion bag can be quickly carried, the vision guide is combined with the encoder to quickly position the speed and the posture of the infusion bag, and the robot quickly communicates with the robot to grab the infusion bag.
As shown in fig. 3, the basic intelligent recognition following capture system mainly comprises components such as a DELTA robot, an industrial personal computer, an industrial camera/lens, a coder, a matched visual light source and the like, wherein the industrial personal computer is connected with the camera through a USB (universal serial bus) or a network interface to capture photos; recognizing the posture of the current infusion bag through the processed picture, and picking up the transmission speed of the current infusion bag by an encoder; the control system of the DELTA robot extracts the postures and the conveying speeds of the infusion bags and carries out follow-up grabbing on the current infusion bag.
The quality of the lighting condition of the light source has very important influence on the imaging quality of the automatic identification system and the correct execution of the rear-end image processing algorithm. Is a key part of the whole automatic identification system. The main purpose of the illumination is to irradiate light on an object to be detected, so that the brightness of the target is increased; the other is to highlight the difference between the object and the background and increase the contrast of the object. The good illumination scheme can make the target to be detected larger and easier to identify, reduce the algorithm difficulty and increase the system reliability. On the contrary, improper selection of the illumination condition causes many problems, for example, overexposure will hide many important information, shadow will cause false edge detection, uneven illumination causes difficulty in selecting the image threshold, and the like.
Suitable illumination schemes are often obtained by a large number of experiments, and common illumination schemes in automatic detection systems based on machine vision include unidirectional illumination, glancing illumination, diffuse illumination, backlight illumination, polarized illumination and the like. Two shots with different lighting schemes are planned through investigation of relevant documents and statistical analysis of visible foreign bodies in the infusion bags. Aiming at white foreign matters such as plastic scraps, glass scraps and the like, the shooting is carried out by adopting a lighting mode of a black background and a white LED annular light source. For dark foreign matter such as hair, a white LED is used as a backlight source to perform imaging.
Common visible foreign matter mainly comprises glass scraps, plastic scraps, hair, fibers, insoluble medicines and the like. These visible foreign matter are generally deposited on the bottom of the bag when the bag is transported horizontally and are not easily identified. Therefore, pretreatment is needed before identification, and visible foreign matters in the infusion bag are in a moving state during shooting. The mechanical and motion mechanisms are matched with an optical lighting and shooting system in the whole process so as to improve the contrast and the recognition efficiency of the image.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (7)
1. An infusion bag foreign matter identification method based on an artificial intelligence vision technology is characterized by comprising the following steps:
step 1: pretreating the infusion bag;
step 2: carrying out image preprocessing on the infusion bag;
and step 3: carrying out image post-processing on the infusion bag; an infusion bag containing foreign matter is identified.
2. The method for identifying the foreign matter in the infusion bag based on the artificial intelligence vision technology according to claim 1, wherein in the step 1, the pretreatment of the infusion bag comprises: removing air bubbles in the infusion bag, removing water on the surface of the infusion bag and moving impurities in the infusion bag.
3. The method for identifying the foreign matter in the infusion bag based on the artificial intelligence vision technology according to claim 2, wherein the removing of the bubbles in the infusion bag and the movement of the impurities in the infusion bag comprises: the infusion bag is grabbed to a corresponding station through the manipulator and is displayed and suspended, the infusion bag is hit by the connecting rod carried by the cylinder moving back and forth, bubbles in the infusion bag are removed, and impurities in the infusion bag move.
4. The method for identifying the foreign matter in the infusion bag based on the artificial intelligence vision technology according to claim 2, wherein the step of removing water from the surface of the infusion bag comprises the following steps: and removing the water stain on the surface of the infusion bag by using an air knife.
5. The method for identifying the foreign matter in the infusion bag based on the artificial intelligence vision technology according to claim 1, wherein in the step 2, the image preprocessing is performed on the infusion bag, and the method comprises the following steps:
calibrating an effective area of the infusion bag;
acquiring continuous frame images of the liquid medicine through a CCD industrial camera;
calculating a frame difference;
contrast enhancement processing;
self-adaptive binarization processing;
and noise analysis and suppression processing.
6. The method for identifying the foreign matter in the infusion bag based on the artificial intelligence vision technology according to claim 1, wherein in the step 3, the infusion bag is subjected to image post-processing; the method for identifying the infusion bag containing the foreign matters comprises the following steps:
carrying out foreign matter, noise point and deviation segmentation on the infusion bag subjected to image preprocessing;
searching foreign matters and noise points;
and (5) extracting features.
7. The method for identifying the foreign matter in the infusion bag based on the artificial intelligence vision technology as claimed in claim 6, wherein the characteristics comprise statistical characteristics, kinematic characteristics and morphological characteristics.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011269097.8A CN112362674A (en) | 2020-11-13 | 2020-11-13 | Infusion bag foreign matter identification method and detection method based on artificial intelligence vision technology |
| PCT/CN2021/107618 WO2022100145A1 (en) | 2020-11-13 | 2021-07-21 | Infusion bag foreign matter identification method and detection method based on artificial intelligence visual technology |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011269097.8A CN112362674A (en) | 2020-11-13 | 2020-11-13 | Infusion bag foreign matter identification method and detection method based on artificial intelligence vision technology |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112362674A true CN112362674A (en) | 2021-02-12 |
Family
ID=74515556
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011269097.8A Pending CN112362674A (en) | 2020-11-13 | 2020-11-13 | Infusion bag foreign matter identification method and detection method based on artificial intelligence vision technology |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN112362674A (en) |
| WO (1) | WO2022100145A1 (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113960056A (en) * | 2021-10-18 | 2022-01-21 | 成都泓睿科技有限责任公司 | Soft bag lamp inspection method and lamp inspection machine |
| CN113960058A (en) * | 2021-10-18 | 2022-01-21 | 成都泓睿科技有限责任公司 | Soft bag heavy foreign matter detection method and light inspection machine |
| CN114261590A (en) * | 2022-01-19 | 2022-04-01 | 佛山果硕智能科技有限公司 | Method and device for analyzing dynamic bubbles after beer canning |
| WO2022100145A1 (en) * | 2020-11-13 | 2022-05-19 | 四川科伦药业股份有限公司 | Infusion bag foreign matter identification method and detection method based on artificial intelligence visual technology |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101165720A (en) * | 2007-09-18 | 2008-04-23 | 湖南大学 | Medical large transfusion machine vision on-line detection method |
| CN101859378A (en) * | 2010-06-11 | 2010-10-13 | 湖南大学 | Visual detection method for quality of liquid medicine on high-speed pharmaceutical production line |
| CN103308527A (en) * | 2013-05-30 | 2013-09-18 | 屈桢深 | Detection method and detection system for foreign matters in infusion bottle |
| CN104597058A (en) * | 2015-01-08 | 2015-05-06 | 中国科学院自动化研究所 | Device and method for automatically detecting foreign matters in white spirit bottle based on vision |
| CN105869154A (en) * | 2015-09-23 | 2016-08-17 | 长沙理工大学 | Visible foreign matter and bubble classification recognition detection method for medical (250ml) |
| WO2020090656A1 (en) * | 2018-10-31 | 2020-05-07 | ニプロ株式会社 | Foreign matter inspection system and foreign matter inspection method |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112362674A (en) * | 2020-11-13 | 2021-02-12 | 四川科伦药业股份有限公司 | Infusion bag foreign matter identification method and detection method based on artificial intelligence vision technology |
-
2020
- 2020-11-13 CN CN202011269097.8A patent/CN112362674A/en active Pending
-
2021
- 2021-07-21 WO PCT/CN2021/107618 patent/WO2022100145A1/en active Application Filing
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101165720A (en) * | 2007-09-18 | 2008-04-23 | 湖南大学 | Medical large transfusion machine vision on-line detection method |
| CN101859378A (en) * | 2010-06-11 | 2010-10-13 | 湖南大学 | Visual detection method for quality of liquid medicine on high-speed pharmaceutical production line |
| CN103308527A (en) * | 2013-05-30 | 2013-09-18 | 屈桢深 | Detection method and detection system for foreign matters in infusion bottle |
| CN104597058A (en) * | 2015-01-08 | 2015-05-06 | 中国科学院自动化研究所 | Device and method for automatically detecting foreign matters in white spirit bottle based on vision |
| CN105869154A (en) * | 2015-09-23 | 2016-08-17 | 长沙理工大学 | Visible foreign matter and bubble classification recognition detection method for medical (250ml) |
| WO2020090656A1 (en) * | 2018-10-31 | 2020-05-07 | ニプロ株式会社 | Foreign matter inspection system and foreign matter inspection method |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2022100145A1 (en) * | 2020-11-13 | 2022-05-19 | 四川科伦药业股份有限公司 | Infusion bag foreign matter identification method and detection method based on artificial intelligence visual technology |
| CN113960056A (en) * | 2021-10-18 | 2022-01-21 | 成都泓睿科技有限责任公司 | Soft bag lamp inspection method and lamp inspection machine |
| CN113960058A (en) * | 2021-10-18 | 2022-01-21 | 成都泓睿科技有限责任公司 | Soft bag heavy foreign matter detection method and light inspection machine |
| CN114261590A (en) * | 2022-01-19 | 2022-04-01 | 佛山果硕智能科技有限公司 | Method and device for analyzing dynamic bubbles after beer canning |
| CN114261590B (en) * | 2022-01-19 | 2023-09-22 | 佛山果硕智能科技有限公司 | Method and device for analyzing dynamic bubbles after beer canning |
Also Published As
| Publication number | Publication date |
|---|---|
| WO2022100145A1 (en) | 2022-05-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112362674A (en) | Infusion bag foreign matter identification method and detection method based on artificial intelligence vision technology | |
| CN109772724B (en) | Flexible detection and analysis system for major surface and internal defects of castings | |
| CN104992449B (en) | Information identification and surface defect online test method based on machine vision | |
| CN102539443B (en) | Bottle body defect automatic detection method based on machine vision | |
| CN109884070B (en) | Aluminum-aluminum blister packaging tablet defect detection method based on machine vision | |
| CN104118609B (en) | Labeling quality determining method and device | |
| CN102621156B (en) | Image-processing-based automatic micro part sorting system | |
| CN108896574B (en) | Bottled liquor impurity detection method and system based on machine vision | |
| Yazdi et al. | Feature extraction algorithm for fill level and cap inspection in bottling machine | |
| CN205253585U (en) | Lens flaw precision measurement system | |
| CN204564599U (en) | A kind of glass tube outward appearance automatic detection system | |
| CN113560219B (en) | Medicine blister packaging defect detection system and method based on machine vision | |
| CN104483320A (en) | Digitized defect detection device and detection method of industrial denitration catalyst | |
| CN106672345A (en) | Automatic sorting method and system of industrial robot | |
| CN111222445A (en) | Straw detection system and method thereof | |
| CN111665251A (en) | Visual detection method for surface defects | |
| CN111330874A (en) | Detection device and detection method for pollution or impurity defects of bottom area of medicine bottle | |
| CN111693535A (en) | Touch screen defect detection equipment and method based on machine vision analysis | |
| CN111323429A (en) | White spirit body foreign matter detection equipment and detection method | |
| CN114359155A (en) | Film laminating method and system | |
| CN203672800U (en) | Wine filling production line quality detecting system | |
| Yun et al. | Vertical scratch detection algorithm for high-speed scale-covered steel BIC (Bar in Coil) | |
| Huaiyuan et al. | The detection system for pharmaceutical bottle-packaging constructed by machine vision technology | |
| CN111805541B (en) | Deep learning-based traditional Chinese medicine decoction piece cleaning and selecting device and cleaning and selecting method | |
| Hashim et al. | Automated visual inspection for metal parts based on morphology and fuzzy rules |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |