CN111209950A - 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 an X-ray imaging device; 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 species analysis training on the medicine region and the air region in each type of 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 output testing result, on current capsule outward appearance detects the basis, through X-ray imaging device to the inside formation of image of capsule, can judge whether the traditional chinese medicine volume of opaque capsule meets the demands, realize the inside and outside while detection of capsule, avoid the long-time repetitive labor of workman, degree of automation is high, can detect the capsule in batches, can effectively improve the bad quality detection efficiency of capsule.
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 wraps the effective medicinal components by using the glue, the edible glue slowly melts after the capsule enters a human body, the effective medicament is slowly released, the full absorption of the human body is facilitated, meanwhile, the problem of poor taste of the medicament taken by people is solved by the appearance of the capsule, and the capsule has a good absorption effect. The capsule can be divided into liquid capsule, medicinal powder capsule and granule capsule according to the medicine liquid, medicinal powder and granule encapsulated in the capsule, and the medicine is encapsulated, so that the medicine property is protected from being damaged, and the digestive organs and respiratory tract are protected. In the capsule production process, need detect it, whether detect the project content for judging the medicine is equipped with in the capsule to and the medicine volume satisfies the requirement in the capsule, current detection mode needs carry out artifical the detection, degree of automation is low, can't carry out the capsule in batches and detect, and some capsules are opaque capsules, can't satisfy current detection demand through outward appearance detection, make traditional detection mode not only measurement accuracy low, and measuring state is unstable, can't satisfy the production and detect the needs moreover.
Disclosure of Invention
The invention aims to solve the technical problem of overcoming the defects in the prior art and provides 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:
a capsule identification and detection method based on X-ray imaging and deep learning is characterized by comprising the following steps: a. collecting various capsule sample images through an X-ray imaging device; b. classifying and marking various capsule samples in the capsule sample image, and performing species analysis training on the marked capsule samples by using a convolutional neural network to generate a main recognition model; c. carrying out region segmentation and type analysis training on the medicine region and the air region in each type of capsule sample to generate a sub-detection model; d. obtaining a pseudo-color image of the capsule to be detected under X-ray; e. carrying out identification detection on the capsule to be detected; f. and outputting a 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, step a includes: passing the capsule sample through an X-ray imaging device; adjusting the penetration degree, and adjusting the image reaction area of the X-ray imaging equipment to the imaging area of liquid, medicine surface and particles in the capsule sample for normalization treatment; and mapping the normalized penetration degree to a pseudo color space to generate a capsule sample image.
According to the above technical solution, preferably, step c includes: classifying and marking the air bubbles in the liquid capsule, and performing species 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 type analysis training by using 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; and loading the sub-detection model, and judging whether the ratio of the air bubbles in the liquid capsule exceeds a threshold value or whether the ratio of the air areas in the medicine surface 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 acquiring various capsule sample images through X-ray imaging equipment; the first training unit is used for carrying out classification marking on various capsule samples in the capsule sample images, carrying out category analysis training on the marked capsule samples by using a convolutional neural network, and generating 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 various 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: and the normalization processing module is used for enabling the capsule sample to pass through the X-ray imaging device, adjusting the penetration degree, enabling the image reaction area of the X-ray imaging device to be adjusted to the imaging area of liquid, medicine surface and particles in the capsule sample, carrying out normalization processing, and mapping the normalized penetration degree to a pseudo-color space to generate a capsule sample image.
According to the above technical solution, preferably, the second training unit includes: the liquid capsule detection module is used for carrying out classification marking on air bubbles in the liquid capsules and carrying out species analysis training by using an FRCNN model; and the medicine powder capsule and particle capsule detection module is used for marking the medicine area and the air area in the medicine powder capsule and the particle capsule, and performing area segmentation and type analysis training by using mask-rcnn.
According to the above technical solution, 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 ratio of the air bubbles in the liquid capsules exceeds a threshold value or judging whether the ratio of the air areas in the medicine surface capsules and the particle capsules exceeds a preset ratio.
The invention has the beneficial effects that:
along with the development of image recognition technology, on the basis of the existing capsule appearance detection, the inside of the capsule is imaged through X-ray imaging equipment, whether the traditional Chinese medicine amount of the opaque capsule meets the requirements can be judged, the simultaneous detection inside and outside the capsule is realized, the long-time repetitive labor of workers is avoided, the automation degree is high, the capsule can be detected in batches, the detection efficiency of defective capsules can be effectively improved, and the purpose of rapid detection is achieved.
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FIG. 1 is a schematic workflow diagram of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood by those skilled in the art, the present invention will be further described in detail with reference to the accompanying drawings and preferred embodiments.
As shown in the figure, the invention comprises the following steps: a. collecting various capsule sample images through an X-ray imaging device; b. classifying and marking various capsule samples in the capsule sample image, and performing species analysis training on the marked capsule samples by using a convolutional neural network to generate a main recognition model; c. carrying out region segmentation and type analysis training on the medicine region and the air region in each type of capsule sample to generate a sub-detection model; d. obtaining a pseudo-color image of the capsule to be detected under X-ray; e. carrying out identification detection on the capsule to be detected; f. and outputting a detection result. In capsule extraction and classification, networks such as frcnn, yolo, etc. may be used for training to achieve contour extraction while classifying. Along with the development of image recognition technology, on the basis of the existing capsule appearance detection, the inside of the capsule is imaged through X-ray imaging equipment, whether the traditional Chinese medicine amount of the opaque capsule meets the requirements can be judged, the simultaneous detection inside and outside the capsule is realized, the long-time repetitive labor of workers is avoided, the automation degree is high, the capsule can be detected in batches, the detection efficiency of defective capsules can be effectively improved, and the purpose of rapid detection is achieved.
According to the above embodiment, preferably, the capsule sample includes a liquid capsule, a powder capsule and a granule capsule, and the capsule can be divided into the liquid capsule, the powder capsule and the granule capsule according to the liquid medicine, the powder and the granule encapsulated in the capsule.
According to the above embodiment, preferably, step a includes: passing the capsule sample through an X-ray imaging device; adjusting the penetration degree, and adjusting the image reaction area of the X-ray imaging equipment to the imaging area of liquid, medicine surface and particles in the capsule sample for normalization treatment; and mapping the normalized penetration degree to a pseudo color space to generate a capsule sample image. In actual operation, a plurality of 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 X-ray to medicine penetration are adjusted, and the detection requirements of different capsules are met.
According to the above embodiment, preferably, step c includes: classifying and marking the air bubbles in the liquid capsule, and performing species 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 type analysis training by using mask-rcnn. When dividing the samples of the medicine area and the air area in the medicine surface capsule and the particle capsule, the whole outline of the air and the medicine area needs to be drawn along the outline, and the medicine area and the air area in the capsule to be detected can be automatically divided by the computer after training.
According to the above embodiment, preferably, step e includes: loading the main identification model, and judging whether the type of the capsule to be detected belongs to a liquid capsule, a medicinal powder capsule, a particle capsule or an empty capsule; and loading the sub-detection model, and judging whether the ratio of the air bubbles in the liquid capsule exceeds a threshold value or whether the ratio of the air areas in the medicine surface 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 bubble proportion exceeds a preset threshold value, the judgment position is unqualified when a detection result is output; and when the air area ratio exceeds the preset ratio, judging that the position is unqualified 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 acquiring various capsule sample images through X-ray imaging equipment; the first training unit is used for carrying out classification marking on various capsule samples in the capsule sample images, carrying out category analysis training on the marked capsule samples by using a convolutional neural network, and generating 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 various 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: and the normalization processing module is used for enabling the capsule sample to pass through the X-ray imaging device, adjusting the penetration degree, enabling the image reaction area of the X-ray imaging device to be adjusted to the imaging area of liquid, medicine surface and particles in the capsule sample, carrying out normalization processing, and mapping the normalized penetration degree to a pseudo-color space to generate a capsule sample image.
According to the above embodiment, preferably, the second training unit includes: the liquid capsule detection module is used for carrying out classification marking on air bubbles in the liquid capsules and carrying out species analysis training by using an FRCNN model; and the medicine powder capsule and particle capsule detection module is used for marking the medicine area and the air area in the medicine powder capsule and the particle capsule, and performing area segmentation and type analysis training by using 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 ratio of the air bubbles in the liquid capsules exceeds a threshold value or judging whether the ratio of the air areas in the medicine surface capsules and the particle capsules exceeds a preset ratio.
Along with the development of image recognition technology, on the basis of the existing capsule appearance detection, the inside of the capsule is imaged through X-ray imaging equipment, whether the traditional Chinese medicine amount of the opaque capsule meets the requirements can be judged, the simultaneous detection inside and outside the capsule is realized, the long-time repetitive labor of workers is avoided, the automation degree is high, the capsule can be detected in batches, the detection efficiency of defective capsules can be effectively improved, and the purpose of rapid detection is achieved.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (9)
1. A capsule identification and detection method based on X-ray imaging and deep learning is characterized by comprising the following steps: a. collecting various capsule sample images through an X-ray imaging device; b. classifying and marking various capsule samples in the capsule sample image, and performing species analysis training on the marked capsule samples by using a convolutional neural network to generate a main recognition model; c. carrying out region segmentation and type analysis training on the medicine region and the air region in each type of capsule sample to generate a sub-detection model; d. obtaining a pseudo-color image of the capsule to be detected under X-ray; e. carrying out identification detection on the capsule to be detected; f. and outputting a detection result.
2. The method as claimed in claim 1, wherein the capsule samples include liquid capsule, powder capsule and particle capsule.
3. The capsule identification and detection method based on X-ray imaging and deep learning according to claim 2, wherein the step a comprises: passing the capsule sample through an X-ray imaging device; adjusting the penetration degree, and adjusting the image reaction area of the X-ray imaging equipment to the imaging area of liquid, medicine surface and particles in the capsule sample for normalization treatment; and mapping the normalized penetration degree to a pseudo color space to generate a capsule sample image.
4. The method for identifying and detecting capsules based on X-ray imaging and deep learning according to claim 2 or 3, wherein step c comprises: classifying and marking the air bubbles in the liquid capsule, and performing species 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 type analysis training by using mask-rcnn.
5. The method for identifying and detecting the capsule based on the X-ray imaging and the deep learning as claimed in claim 4, wherein the step e comprises: loading the main identification model and judging the type of the capsule to be detected; and loading the sub-detection model, and judging whether the ratio of the air bubbles in the liquid capsule exceeds a threshold value or whether the ratio of the air areas in the medicine surface capsule and the particle capsule exceeds a preset ratio.
6. A capsule identification and detection system based on X-ray imaging and deep learning, which uses the capsule identification and detection method based on X-ray imaging and deep learning of claim 5, and is characterized by comprising the following steps:
the sampling unit is used for acquiring various capsule sample images through X-ray imaging equipment;
the first training unit is used for carrying out classification marking on various capsule samples in the capsule sample images, carrying out category analysis training on the marked capsule samples by using a convolutional neural network, and generating 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 various 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.
7. The system of claim 6, wherein the sampling unit comprises: and the normalization processing module is used for enabling the capsule sample to pass through the X-ray imaging device, adjusting the penetration degree, enabling the image reaction area of the X-ray imaging device to be adjusted to the imaging area of liquid, medicine surface and particles in the capsule sample, carrying out normalization processing, and mapping the normalized penetration degree to a pseudo-color space to generate a capsule sample image.
8. The system of claim 7, wherein the second training unit comprises: the liquid capsule detection module is used for carrying out classification marking on air bubbles in the liquid capsules and carrying out species analysis training by using an FRCNN model; and the medicine powder capsule and particle capsule detection module is used for marking the medicine area and the air area in the medicine powder capsule and the particle capsule, and performing area segmentation and type analysis training by using mask-rcnn.
9. The system of claim 8, 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 ratio of the air bubbles in the liquid capsules exceeds a threshold value or judging whether the ratio of the air areas in the medicine surface capsules and the particle capsules exceeds a preset ratio.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112700168A (en) * | 2021-01-14 | 2021-04-23 | 北京赛而生物药业有限公司 | Method and device for quality inspection of capsule medicines |
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