CN111209950B - Capsule identification and detection method and system based on X-ray imaging and deep learning - Google Patents
Capsule identification and detection method and system based on X-ray imaging and deep learning Download PDFInfo
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Abstract
The invention relates to a capsule identification and detection method based on X-ray imaging and deep learning, which comprises the following steps: collecting various capsule sample images through X-ray imaging equipment; classifying and marking various capsule samples in the capsule sample image, and performing type analysis training on the marked capsule samples by using a convolutional neural network to generate a main recognition model; performing region segmentation and type analysis training on a medicine region and an air region in each capsule sample to generate a sub-detection model; obtaining a pseudo-color image of the capsule to be detected under X-ray; identifying and detecting the capsule to be detected; the detection result is output, on the basis of the existing capsule appearance detection, the interior of the capsule is imaged through the X-ray imaging equipment, whether the dosage in the opaque capsule meets the requirement can be judged, the simultaneous detection inside and outside the capsule is realized, the long-time repeated labor of workers is avoided, the automation degree is high, the batch detection of the capsules can be carried out, and the detection efficiency of defective capsules can be effectively improved.
Description
Technical Field
The invention relates to the technical field of capsule production detection, in particular to a capsule identification and detection method and system based on X-ray imaging and deep learning.
Background
The capsule is prepared by wrapping the effective medicine components by using the gelatin, and the edible gelatin is slowly melted after the capsule enters the human body, so that the effective medicine is slowly released, the capsule is favorable for full absorption by the human body, and meanwhile, the problem of poor medicine taking taste of people is solved by the appearance of the capsule, so that the medicine has a good absorption effect. The capsule can be divided into liquid capsule, powder capsule and granule capsule according to the medicine liquid, powder and granule, and the medicine is filled into the capsule, which not only protects the medicine property from being destroyed, but also protects digestive organ and respiratory tract. In the capsule production process, the detection is needed, whether the content of the detection item is whether the medicine is filled in the capsule or not, whether the medicine amount in the capsule meets the requirement or not is judged, the existing detection mode is required to be manually detected, the degree of automation is low, the capsule detection cannot be carried out in batches, and some capsules are opaque capsules, the current detection requirement cannot be met through appearance detection, so that the traditional detection mode is low in measurement precision, unstable in measurement state and incapable of meeting the production detection requirement.
Disclosure of Invention
The invention aims to solve the technical problem of overcoming the defects in the prior art and providing a capsule identification and detection method and system based on X-ray imaging and deep learning.
The invention is realized by the following technical scheme:
the capsule identification and detection method based on X-ray imaging and deep learning is characterized by comprising the following steps of: a. collecting various capsule sample images through X-ray imaging equipment; b. classifying and marking various capsule samples in the capsule sample image, and performing type analysis training on the marked capsule samples by using a convolutional neural network to generate a main recognition model; c. performing region segmentation and type analysis training on the medicine region and the air region in each type of the capsule sample to generate a sub-detection model; d. obtaining a pseudo-color image of the capsule to be detected under X-ray; e. identifying and detecting the capsule to be detected; f. outputting the detection result.
According to the above technical solution, preferably, the capsule sample includes a liquid capsule, a powder capsule and a granule capsule.
According to the above technical solution, preferably, the step a includes: passing the capsule sample through an X-ray imaging device; adjusting the penetrability to enable the image reaction area of the X-ray imaging equipment to be adjusted to the imaging area of liquid, medicine surface and particles in the capsule sample, and carrying out normalization treatment; and mapping the normalized penetrability to a pseudo-color space to generate a capsule sample image.
According to the above technical solution, preferably, step c includes: classifying and marking bubbles in the liquid capsule, and performing type analysis training by using an FRCNN model; marking the medicine area and the air area in the medicine powder capsule and the particle capsule, and performing area segmentation and category analysis training by using a mask-rcnn.
According to the above technical solution, preferably, step e includes: loading the main identification model, and judging the type of the capsule to be detected; loading the sub-detection model, judging whether the air bubble ratio in the liquid capsule exceeds a threshold value or judging whether the air area ratio in the medicine powder capsule and the particle capsule exceeds a preset ratio.
The invention also discloses a capsule identification and detection system based on X-ray imaging and deep learning, which comprises: the sampling unit is used for collecting various capsule sample images through the X-ray imaging equipment; the first training unit is used for classifying and marking various capsule samples in the capsule sample image, and performing type analysis training on the marked capsule samples by using a convolutional neural network to generate a main recognition model; the second training unit is used for carrying out region segmentation and type analysis training on the medicine regions and the air regions in the capsule samples to generate a sub-detection model; the extraction unit is used for obtaining a pseudo-color image of the capsule to be detected under X-ray; and the detection unit is used for identifying and detecting the capsule to be detected and outputting a detection result.
According to the above technical solution, preferably, the sampling unit includes: the normalization processing module is used for adjusting the penetration of the capsule sample through the X-ray imaging equipment, enabling the image reaction area of the X-ray imaging equipment to be adjusted to the imaging area of liquid, medicine surface and particles in the capsule sample, carrying out normalization processing, mapping the normalized penetration to a pseudo-color space, and generating a capsule sample image.
According to the above technical solution, preferably, the second training unit includes: the liquid capsule detection module is used for classifying and marking bubbles in the liquid capsule and performing type analysis training by using an FRCNN model; and the medicine powder capsule and particle capsule detection module is used for marking the medicine areas and the air areas in the medicine powder capsule and the particle capsule, and performing area segmentation and category analysis training by using a mask-rcnn.
According to the above technical solution, preferably, the detecting unit includes: the main identification module is used for loading the main identification model and judging the type of the capsule to be detected; and the sub-detection module is used for loading the sub-detection model, judging whether the air bubble ratio in the liquid capsule exceeds a threshold value or judging whether the air area ratio in the medicine powder capsule and the particle capsule exceeds a preset ratio.
The beneficial effects of the invention are as follows:
along with the development of image recognition technology, on the basis of existing capsule appearance detection, the interior of a capsule is imaged through X-ray imaging equipment, whether the dosage in an opaque capsule meets the requirement can be judged, the simultaneous detection inside and outside the capsule is realized, the long-time repeated labor of workers is avoided, the degree of automation is high, the capsules can be detected in batches, the detection efficiency of defective capsules can be effectively improved, and the aim of rapid detection is fulfilled.
Drawings
Fig. 1 is a schematic of the workflow of the present invention.
Detailed Description
The present invention will be described in further detail below with reference to the drawings and preferred embodiments, so that those skilled in the art can better understand the technical solutions of the present invention.
As shown, the present invention includes the steps of: a. collecting various capsule sample images through X-ray imaging equipment; b. classifying and marking various capsule samples in the capsule sample image, and performing type analysis training on the marked capsule samples by using a convolutional neural network to generate a main recognition model; c. performing region segmentation and type analysis training on the medicine region and the air region in each type of the capsule sample to generate a sub-detection model; d. obtaining a pseudo-color image of the capsule to be detected under X-ray; e. identifying and detecting the capsule to be detected; f. outputting the detection result. In capsule extraction and classification, contour extraction and classification can be achieved simultaneously using a network such as frcnn, yolo, etc. Along with the development of image recognition technology, on the basis of existing capsule appearance detection, the interior of a capsule is imaged through X-ray imaging equipment, whether the dosage in an opaque capsule meets the requirement can be judged, the simultaneous detection inside and outside the capsule is realized, the long-time repeated labor of workers is avoided, the degree of automation is high, the capsules can be detected in batches, the detection efficiency of defective capsules can be effectively improved, and the aim of rapid detection is fulfilled.
According to the above embodiment, preferably, the capsule sample includes a liquid capsule, a powder capsule and a granule capsule, and the capsule is classified into a liquid capsule, a powder capsule and a granule capsule according to the liquid medicine, the powder and the granule enclosed in the capsule.
According to the above embodiment, preferably, the step a includes: passing the capsule sample through an X-ray imaging device; adjusting the penetrability to enable the image reaction area of the X-ray imaging equipment to be adjusted to the imaging area of liquid, medicine surface and particles in the capsule sample, and carrying out normalization treatment; and mapping the normalized penetrability to a pseudo-color space to generate a capsule sample image. In actual operation, multiple capsule samples can be collected, and the minimum, maximum and average values of most capsules are detected as normalization standards, namely, more value ranges of the X-rays on the drug penetration are adjusted, so that the detection requirements of different capsules are met.
According to the above embodiment, preferably, the step c includes: classifying and marking bubbles in the liquid capsule, and performing type analysis training by using an FRCNN model; marking the medicine area and the air area in the medicine powder capsule and the particle capsule, and performing area segmentation and category analysis training by using a mask-rcnn. When the medicine area and the air area in the medicine powder capsule and the particle capsule are segmented, the whole outline of the air area and the medicine area needs to be sketched out along the outline, and the medicine area and the air area in the capsule to be detected can be automatically segmented by a computer after training.
According to the above embodiment, preferably, the step e includes: loading the main identification model, and judging that the type of the capsule to be detected belongs to a liquid capsule, a medicine powder capsule, a particle capsule or an empty capsule; loading the sub-detection model, judging whether the air bubble ratio in the liquid capsule exceeds a threshold value or judging whether the air area ratio in the medicine powder capsule and the particle capsule exceeds a preset ratio. After the bubbles are identified, the positions of the bubbles are positioned, the area and the number of the bubbles are calculated, and when the ratio of the bubbles exceeds a preset threshold value, the positions are judged to be unqualified when the detection result is output; when the air area ratio exceeds a preset ratio, the judgment position is failed when the detection result is output.
The invention also discloses a capsule identification and detection system based on X-ray imaging and deep learning, which comprises: the sampling unit is used for collecting various capsule sample images through the X-ray imaging equipment; the first training unit is used for classifying and marking various capsule samples in the capsule sample image, and performing type analysis training on the marked capsule samples by using a convolutional neural network to generate a main recognition model; the second training unit is used for carrying out region segmentation and type analysis training on the medicine regions and the air regions in the capsule samples to generate a sub-detection model; the extraction unit is used for obtaining a pseudo-color image of the capsule to be detected under X-ray; and the detection unit is used for identifying and detecting the capsule to be detected and outputting a detection result.
According to the above embodiment, preferably, the sampling unit includes: the normalization processing module is used for adjusting the penetration of the capsule sample through the X-ray imaging equipment, enabling the image reaction area of the X-ray imaging equipment to be adjusted to the imaging area of liquid, medicine surface and particles in the capsule sample, carrying out normalization processing, mapping the normalized penetration to a pseudo-color space, and generating a capsule sample image.
According to the above embodiment, preferably, the second training unit includes: the liquid capsule detection module is used for classifying and marking bubbles in the liquid capsule and performing type analysis training by using an FRCNN model; and the medicine powder capsule and particle capsule detection module is used for marking the medicine areas and the air areas in the medicine powder capsule and the particle capsule, and performing area segmentation and category analysis training by using a mask-rcnn.
According to the above embodiment, preferably, the detection unit includes: the main identification module is used for loading the main identification model and judging the type of the capsule to be detected; and the sub-detection module is used for loading the sub-detection model, judging whether the air bubble ratio in the liquid capsule exceeds a threshold value or judging whether the air area ratio in the medicine powder capsule and the particle capsule exceeds a preset ratio.
Along with the development of image recognition technology, on the basis of existing capsule appearance detection, the interior of a capsule is imaged through X-ray imaging equipment, whether the dosage in an opaque capsule meets the requirement can be judged, the simultaneous detection inside and outside the capsule is realized, the long-time repeated labor of workers is avoided, the degree of automation is high, the capsules can be detected in batches, the detection efficiency of defective capsules can be effectively improved, and the aim of rapid detection is fulfilled.
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention.
Claims (5)
1. The capsule identification and detection method based on X-ray imaging and deep learning is characterized by comprising the following steps of:
a. collecting various capsule sample images through X-ray imaging equipment;
b. classifying and marking various capsule samples in the capsule sample image, and performing type analysis training on the marked capsule samples by using a convolutional neural network to generate a main recognition model;
c. performing region segmentation and type analysis training on the medicine region and the air region in each type of the capsule sample to generate a sub-detection model;
d. obtaining a pseudo-color image of the capsule to be detected under X-ray;
e. identifying and detecting the capsule to be detected;
f. outputting a detection result;
the capsule samples comprise liquid capsules, powder capsules and granule capsules;
step a comprises: passing the capsule sample through an X-ray imaging device; adjusting the penetrability to enable the image reaction area of the X-ray imaging equipment to be adjusted to the imaging area of liquid, medicine surface and particles in the capsule sample, and carrying out normalization treatment; mapping the normalized penetration to a pseudo-color space to generate a capsule sample image;
step c comprises: classifying and marking bubbles in the liquid capsule, and performing type analysis training by using an FRCNN model; marking the medicine areas and the air areas in the medicine powder capsules and the particle capsules, and performing area segmentation and category analysis training by using a mask-rcnn;
step e comprises: loading the main identification model, and judging the type of the capsule to be detected; loading the sub-detection model, judging whether the air bubble ratio in the liquid capsule exceeds a threshold value or judging whether the air area ratio in the medicine powder capsule and the particle capsule exceeds a preset ratio.
2. A capsule recognition and detection system based on X-ray imaging and deep learning, using a capsule recognition and detection method based on X-ray imaging and deep learning as claimed in claim 1, comprising:
the sampling unit is used for collecting various capsule sample images through the X-ray imaging equipment;
the first training unit is used for classifying and marking various capsule samples in the capsule sample image, and performing type analysis training on the marked capsule samples by using a convolutional neural network to generate a main recognition model;
the second training unit is used for carrying out region segmentation and type analysis training on the medicine regions and the air regions in the capsule samples to generate a sub-detection model;
the extraction unit is used for obtaining a pseudo-color image of the capsule to be detected under X-ray;
and the detection unit is used for identifying and detecting the capsule to be detected and outputting a detection result.
3. The capsule recognition and detection system based on X-ray imaging and deep learning of claim 2, wherein the sampling unit comprises: the normalization processing module is used for adjusting the penetration of the capsule sample through the X-ray imaging equipment, enabling the image reaction area of the X-ray imaging equipment to be adjusted to the imaging area of liquid, medicine surface and particles in the capsule sample, carrying out normalization processing, mapping the normalized penetration to a pseudo-color space, and generating a capsule sample image.
4. A capsule recognition and detection system based on X-ray imaging and deep learning according to claim 3, wherein the second training unit comprises: the liquid capsule detection module is used for classifying and marking bubbles in the liquid capsule and performing type analysis training by using an FRCNN model; and the medicine powder capsule and particle capsule detection module is used for marking the medicine areas and the air areas in the medicine powder capsule and the particle capsule, and performing area segmentation and category analysis training by using a mask-rcnn.
5. The capsule recognition and detection system based on X-ray imaging and deep learning of claim 4, wherein the detection unit comprises: the main identification module is used for loading the main identification model and judging the type of the capsule to be detected; and the sub-detection module is used for loading the sub-detection model, judging whether the air bubble ratio in the liquid capsule exceeds a threshold value or judging whether the air area ratio in the medicine powder capsule and the particle capsule exceeds a preset ratio.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8712163B1 (en) * | 2012-12-14 | 2014-04-29 | EyeNode, LLC | Pill identification and counterfeit detection method |
| CN105139384A (en) * | 2015-08-11 | 2015-12-09 | 北京天诚盛业科技有限公司 | Defective capsule detection method and apparatus |
| CN108229561A (en) * | 2018-01-03 | 2018-06-29 | 北京先见科技有限公司 | Particle product defect detection method based on deep learning |
| CN109685781A (en) * | 2018-12-17 | 2019-04-26 | 江苏蜂奥生物科技有限公司 | A kind of multiple target method for quickly identifying based on certain rule applied to bee glue soft capsule |
| CN109949285A (en) * | 2019-03-12 | 2019-06-28 | 天津瑟威兰斯科技有限公司 | Method and system for sorting ores under X-ray image based on convolutional neural network |
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| ATE503179T1 (en) * | 2007-07-10 | 2011-04-15 | Boehringer Ingelheim Int | OPTICAL FILLING CONTROL OF PHARMACEUTICAL CAPSULES ON CAPSULE FILLING MACHINES |
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Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8712163B1 (en) * | 2012-12-14 | 2014-04-29 | EyeNode, LLC | Pill identification and counterfeit detection method |
| CN105139384A (en) * | 2015-08-11 | 2015-12-09 | 北京天诚盛业科技有限公司 | Defective capsule detection method and apparatus |
| CN108229561A (en) * | 2018-01-03 | 2018-06-29 | 北京先见科技有限公司 | Particle product defect detection method based on deep learning |
| CN109685781A (en) * | 2018-12-17 | 2019-04-26 | 江苏蜂奥生物科技有限公司 | A kind of multiple target method for quickly identifying based on certain rule applied to bee glue soft capsule |
| CN109949285A (en) * | 2019-03-12 | 2019-06-28 | 天津瑟威兰斯科技有限公司 | Method and system for sorting ores under X-ray image based on convolutional neural network |
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