CN113159110A - X-ray-based liquid intelligent detection method - Google Patents
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- 239000007788 liquid Substances 0.000 title claims abstract description 88
- 238000001514 detection method Methods 0.000 title claims abstract description 19
- 238000013527 convolutional neural network Methods 0.000 claims abstract description 21
- 230000001678 irradiating effect Effects 0.000 claims abstract description 3
- 238000004040 coloring Methods 0.000 claims description 12
- 238000007689 inspection Methods 0.000 claims description 12
- 239000003550 marker Substances 0.000 claims description 12
- 238000000034 method Methods 0.000 claims description 12
- 239000003086 colorant Substances 0.000 claims description 7
- 231100001261 hazardous Toxicity 0.000 claims description 4
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- 238000010586 diagram Methods 0.000 description 2
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- LFQSCWFLJHTTHZ-UHFFFAOYSA-N Ethanol Chemical compound CCO LFQSCWFLJHTTHZ-UHFFFAOYSA-N 0.000 description 1
- 238000010521 absorption reaction Methods 0.000 description 1
- 150000001298 alcohols Chemical class 0.000 description 1
- 239000002360 explosive Substances 0.000 description 1
- 239000003350 kerosene Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
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- 238000003062 neural network model Methods 0.000 description 1
- 230000001172 regenerating effect Effects 0.000 description 1
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- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N23/00—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00
- G01N23/02—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00 by transmitting the radiation through the material
- G01N23/06—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00 by transmitting the radiation through the material and measuring the absorption
- G01N23/083—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00 by transmitting the radiation through the material and measuring the absorption the radiation being X-rays
- G01N23/087—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00 by transmitting the radiation through the material and measuring the absorption the radiation being X-rays using polyenergetic X-rays
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Abstract
The invention relates to liquid detection classification, in particular to an X-ray-based liquid intelligent detection method, which comprises the steps of irradiating an object by using high-energy and low-energy X-rays, calculating an equivalent atomic number and an object density, determining a liquid type according to the equivalent atomic number and the object density, marking each pixel in an X-ray image according to the liquid type to obtain a marked image, identifying the X-ray image by using a convolutional neural network model to obtain an identified image, comparing the marked image with the identified image, and judging whether dangerous liquid exists or not according to a comparison result; the technical scheme provided by the invention can effectively overcome the defect that the liquid and the type of the liquid can not be identified through the X-ray image in the prior art.
Description
Technical Field
The invention relates to liquid detection classification, in particular to an intelligent liquid detection method based on X-ray.
Background
In daily life, illegal persons carry dangerous liquid, such as gasoline, alcohol, kerosene, various liquid bombs and other forbidden articles to take public transportation, and once the dangerous liquid cannot be effectively detected, serious casualties and property loss can be caused, and severe negative effects can be caused to the society.
To reduce the occurrence of such events, security efforts are imperative. A security inspection machine is one of the most effective security tools at present, and has been widely used in important places such as public transportation, judicial authorities, and logistics companies as one of the tools for security inspection. The security inspection is mainly an electronic device which can perform perspective scanning on luggage, articles and the like except human bodies, can find hidden dangerous articles, and sends the checked luggage into an X-ray inspection channel through a conveying belt to finish inspection.
However, the existing security inspection machine adopts high-energy and low-energy X-rays to pass through the detected object, and different substances have large difference in absorption rate to the high-energy X-rays and the low-energy X-rays, so that the equivalent atomic number of the detected object can be calculated according to the attenuation rate of the high-energy X-rays and the low-energy X-rays, and then the equivalent atomic number is colored according to a certain rule, and a final X-ray security inspection article image is displayed.
But because the equivalent atomic number difference of different liquid is less, traditional security check machine is colored according to the rule of colouring after, can't carry out liquid identification according to the picture after the colouring, and the colouring rule standard of different security check machine producers is different moreover, leads to the manual detection degree of difficulty to further increase, only can require the passenger to take out liquid, carries out manual detection through modes such as "sniffing", "see", "smell", "taste", has promoted the security check degree of difficulty greatly, has also reduced security check efficiency simultaneously.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects in the prior art, the invention provides an intelligent liquid detection method based on X-ray, which can effectively overcome the defect that the prior art cannot identify liquid and liquid types through X-ray images.
(II) technical scheme
In order to achieve the purpose, the invention is realized by the following technical scheme:
an intelligent liquid detection method based on X-ray comprises the following steps:
s1, irradiating the object by using high-energy and low-energy X rays, and calculating the equivalent atomic number and the object density;
s2, determining the liquid type according to the equivalent atomic number and the object density;
s3, marking each pixel in the X-ray image according to the type of the liquid to obtain a marked image;
s4, identifying the X-ray image by using the convolutional neural network model to obtain an identified image;
and S5, comparing the marked image with the identification image, and judging whether the dangerous liquid exists according to the comparison result.
Preferably, the equivalent atomic number and the object density are calculated in S1, including:
and calculating the equivalent atomic number and the object density by using the attenuation value of the X-ray.
Preferably, in S3, labeling each pixel in the X-ray image according to the type of the liquid to obtain a labeled image, including:
after the type of the liquid is determined, each pixel in the X-ray image comprises information of an abscissa, an ordinate and the type of the liquid, and each pixel is colored and marked by adopting colors corresponding to different types of liquid to obtain a marked image.
Preferably, the step of identifying the X-ray image by using the convolutional neural network model in S4 to obtain an identified image includes:
and identifying the X-ray image by using the trained convolutional neural network model, and coloring and marking the identification area by using colors corresponding to different types of liquid according to the identification result to obtain an identification image.
Preferably, the identifying the X-ray image by using the convolutional neural network model in S4, before obtaining the identified image, includes:
and constructing a convolutional neural network model, collecting the colored marked image as a training image set, and training the convolutional neural network model by using the training image set to obtain the trained convolutional neural network model.
Preferably, comparing the tag image with the recognition image in S5, and determining whether a hazardous liquid exists according to the comparison result, includes:
if the marked image and the identified image have areas with the same coloring, and the color is the same as the color corresponding to the dangerous liquid, judging that the areas have the dangerous liquid;
if the marked image and the identified image have areas with the same coloring, and the color is the same as the color corresponding to the non-dangerous liquid, judging that no dangerous liquid exists in the area;
if there is a region of different coloration in the marker image and the identification image, the process returns to S1 to regenerate the marker image and the identification image, and the regenerated marker image and the identification image are compared to determine whether or not there is a hazardous liquid.
Preferably, after comparing the marker image with the identification image and determining whether the marker image is a dangerous liquid in S5, the method includes: and if the dangerous liquid exists, updating the position information of the area with the dangerous liquid in real time, and prompting security check personnel to open a package for inspection.
(III) advantageous effects
Compared with the prior art, the intelligent detection method for the liquid based on the X-ray can determine the type of the liquid according to the equivalent atomic number and the object density, mark each pixel in the X-ray image according to the type of the liquid, and judge the dangerous liquid by combining the identification result of the convolutional neural network model, so that the liquid in the X-ray image can be accurately classified, whether the dangerous liquid exists or not can be accurately judged, the security inspection difficulty is greatly reduced, the security inspection efficiency is improved, and public safety driving protection is realized.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It is obvious that the drawings in the following description are only some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a schematic diagram of equivalent atomic number and object density versus object type in accordance with the present invention;
fig. 3 is a marker image obtained by marking each pixel in an X-ray image according to the type of liquid in the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
An intelligent liquid detection method based on X-ray, as shown in fig. 1 to fig. 3, uses high and low energy X-ray to irradiate the object, and calculates the equivalent atomic number and the object density.
Wherein, calculating equivalent atomic number and object density comprises:
and calculating the equivalent atomic number and the object density by using the attenuation value of the X-ray.
And determining the liquid type according to the equivalent atomic number and the object density.
As shown in FIG. 2, the equivalent atomic number is 6.8-7.2, and the density of the object is 0.68-0.72g/cm3Liquids within the range are identified as alcohols; the equivalent atomic number is 7.6-8.0, and the density of the object is 0.98-1.02g/cm3Liquids within the range are considered water; the equivalent atomic number is 7-9, and the density of the object is 1.25-1.7g/cm3The range is identified as an explosive region.
Marking each pixel in the X-ray image according to the type of the liquid to obtain a marked image, and the method specifically comprises the following steps:
after the type of the liquid is determined, each pixel in the X-ray image comprises information of an abscissa, an ordinate and the type of the liquid, and each pixel is colored and marked by adopting colors corresponding to different types of liquid to obtain a marked image.
As shown in fig. 3, where the dark colored area within the top left marker box is a hazardous liquid.
Utilize convolution neural network model to discern the X-ray image, obtain the discernment image, specifically include:
and identifying the X-ray image by using the trained convolutional neural network model, and coloring and marking the identification area by using colors corresponding to different types of liquid according to the identification result to obtain an identification image.
The method for identifying the X-ray image by using the convolutional neural network model comprises the following steps of before obtaining an identification image:
and constructing a convolutional neural network model, collecting the colored marked image as a training image set, and training the convolutional neural network model by using the training image set to obtain the trained convolutional neural network model.
Comparing the marked image with the identification image, and judging whether dangerous liquid exists according to a comparison result, wherein the method specifically comprises the following steps:
if the marked image and the identified image have areas with the same coloring, and the color is the same as the color corresponding to the dangerous liquid, judging that the areas have the dangerous liquid;
if the marked image and the identified image have areas with the same coloring, and the color is the same as the color corresponding to the non-dangerous liquid, judging that no dangerous liquid exists in the area;
and if areas with different colors exist in the marking image and the identification image, regenerating the marking image and the identification image, comparing the regenerated marking image with the identification image, and judging whether the dangerous liquid exists or not.
Comparing the marked image with the identification image, and judging whether the marked image is dangerous liquid or not, the method comprises the following steps: and if the dangerous liquid exists, updating the position information of the area with the dangerous liquid in real time, and prompting security personnel to open a package for inspection.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and the modifications or the substitutions do not cause the essence of the corresponding technical solutions to depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (7)
1. An intelligent liquid detection method based on X-ray is characterized in that: the method comprises the following steps:
s1, irradiating the object by using high-energy and low-energy X-rays, and calculating the equivalent atomic number and the object density;
s2, determining the liquid type according to the equivalent atomic number and the object density;
s3, marking each pixel in the X-ray image according to the type of the liquid to obtain a marked image;
s4, identifying the X-ray image by using the convolutional neural network model to obtain an identified image;
and S5, comparing the marked image with the identification image, and judging whether the dangerous liquid exists according to the comparison result.
2. The intelligent liquid detection method based on X-rays according to claim 1, characterized in that: calculating equivalent atomic number and object density in S1, including:
and calculating the equivalent atomic number and the object density by using the attenuation value of the X-ray.
3. The intelligent liquid detection method based on X-ray according to claim 2, characterized in that: in S3, labeling each pixel in the X-ray image according to the type of the liquid to obtain a label image, including:
after the type of the liquid is determined, each pixel in the X-ray image comprises information of an abscissa, an ordinate and the type of the liquid, and each pixel is colored and marked by adopting colors corresponding to different types of liquid to obtain a marked image.
4. The intelligent liquid detection method based on X-rays according to claim 3, characterized in that: in S4, recognizing the X-ray image by using the convolutional neural network model to obtain a recognized image, including:
and identifying the X-ray image by using the trained convolutional neural network model, and coloring and marking the identification area by using colors corresponding to different types of liquid according to the identification result to obtain an identification image.
5. The intelligent liquid detection method based on X-rays according to claim 4, wherein the method comprises the following steps: in S4, before the identifying the X-ray image by using the convolutional neural network model to obtain the identified image, the method includes:
and constructing a convolutional neural network model, collecting the colored marked image as a training image set, and training the convolutional neural network model by using the training image set to obtain the trained convolutional neural network model.
6. The intelligent liquid detection method based on X-rays according to claim 4, wherein the method comprises the following steps: s5, comparing the marked image with the identification image, and judging whether dangerous liquid exists according to the comparison result, including:
if the marked image and the identified image have areas with the same coloring, and the color is the same as the color corresponding to the dangerous liquid, judging that the areas have the dangerous liquid;
if the marked image and the identified image have areas with the same coloring, and the color is the same as the color corresponding to the non-dangerous liquid, judging that no dangerous liquid exists in the area;
if there is a region of different coloration in the marker image and the identification image, the process returns to S1 to regenerate the marker image and the identification image, and the regenerated marker image and the identification image are compared to determine whether or not there is a hazardous liquid.
7. The intelligent liquid detection method based on X-rays according to claim 6, wherein: after comparing the marker image with the identification image and determining whether the marker image is a dangerous liquid in S5, the method includes: and if the dangerous liquid exists, updating the position information of the area with the dangerous liquid in real time, and prompting security check personnel to open a package for inspection.
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| WO2023087653A1 (en) * | 2021-11-17 | 2023-05-25 | 湖南苏科智能科技有限公司 | Dual-view-angle associated security check method and system based on neural network and multi-source data |
| CN116401587A (en) * | 2023-06-08 | 2023-07-07 | 乐山师范学院 | Object category identification method based on X-rays |
| CN117347396A (en) * | 2023-08-18 | 2024-01-05 | 北京声迅电子股份有限公司 | XGBoost model-based substance type identification method |
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| CN113781426B (en) * | 2021-09-07 | 2024-02-13 | 海深智能科技(上海)有限公司 | Intelligent security inspection method for identifying liquid components |
| WO2023087653A1 (en) * | 2021-11-17 | 2023-05-25 | 湖南苏科智能科技有限公司 | Dual-view-angle associated security check method and system based on neural network and multi-source data |
| CN116401587A (en) * | 2023-06-08 | 2023-07-07 | 乐山师范学院 | Object category identification method based on X-rays |
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