CN112162324A - Intelligent security inspection method for effectively improving contraband identification rate - Google Patents

Intelligent security inspection method for effectively improving contraband identification rate Download PDF

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CN112162324A
CN112162324A CN202010907854.3A CN202010907854A CN112162324A CN 112162324 A CN112162324 A CN 112162324A CN 202010907854 A CN202010907854 A CN 202010907854A CN 112162324 A CN112162324 A CN 112162324A
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security inspection
contraband
energy information
low
ray
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戴剑彬
王晨曦
陈家欢
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Haishen Intelligent Technology Shanghai Co ltd
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    • G01V5/232—
    • 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
    • G01N23/04—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 forming images of the material
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0004—Industrial image inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2223/00—Investigating materials by wave or particle radiation
    • G01N2223/40—Imaging
    • G01N2223/401—Imaging image processing
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2223/00—Investigating materials by wave or particle radiation
    • G01N2223/40—Imaging
    • G01N2223/423—Imaging multispectral imaging-multiple energy imaging
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10116—X-ray image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20081—Training; Learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20084—Artificial neural networks [ANN]

Abstract

The invention relates to the technical field of security inspection equipment and security inspection methods, in particular to an intelligent security inspection method for effectively improving the identification rate of contraband, which is used for detecting and identifying the contraband on an X-ray image by a security inspection machine and comprises the following steps: step A: shooting an X-ray image of an article by using an X-ray machine, and outputting a high-energy spectrum gray-scale image and a low-energy spectrum gray-scale image; and B: analyzing the X-ray image by adopting an example segmentation algorithm to find out the non-overlapped part and the overlapped part of the X-ray image; and C: selecting high-energy information and low-energy information of non-overlapped parts of articles made of different materials for the non-overlapped parts, and finding out the color mapping relation of article overlapping through linear regression; step D: and calculating the corrected atomic number of the substance by using the example segmentation algorithm in the step B for the overlapped part of the X-ray images. The method overcomes the influence of object overlapping on the recognition rate, and greatly improves the accuracy and stability of recognition.

Description

Intelligent security inspection method for effectively improving contraband identification rate
Technical Field
The invention relates to the technical field of security inspection equipment and security inspection methods, in particular to an intelligent security inspection method for effectively improving the identification rate of contraband.
Background
In order to ensure the safety of public transportation, safety inspection has become a necessary means for ensuring the safety of public lives and properties. However, the demand of security inspection is increasing due to high mobility and large passenger flow of Chinese population. At present, the operation of security inspection equipment is implemented by security inspection personnel.
However, the X-ray security inspection machine luggage detection mainly depends on manual image viewing and judgment of security inspectors, the accuracy of the X-ray security inspection machine luggage detection depends on the self experience of the security inspectors, the detection speed is limited by the workload of the security inspectors, the efficiency is not easy to be further improved, the false alarm rate and the false alarm rate in the industry exceed 30%, and the situation that contraband articles are not mistakenly and mistakenly missed in the manual security inspection identification process is difficult to ensure, so that potential safety hazards are brought.
The image identification technology based on deep learning is introduced into the security inspection equipment industry, however, the existing image identification technology has certain limitations in contraband identification, and the difficult points include: multiple objects are overlapped and shielded, images are blurred in low resolution, contraband is various in types and shapes, and the like. In view of this, an intelligent security inspection method for effectively improving the identification rate of contraband is provided.
Disclosure of Invention
The invention aims to provide an intelligent security inspection method for effectively improving the identification rate of contraband so as to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme:
an intelligent security inspection method for effectively improving the identification rate of contraband is used for detecting and identifying the contraband of an X-ray image by a security inspection machine, and comprises the following steps:
step A: shooting an X-ray image of an article by using an X-ray machine, and outputting a high-energy spectrum gray-scale image and a low-energy spectrum gray-scale image;
and B: analyzing the X-ray image by adopting an example segmentation algorithm to find out the non-overlapped part and the overlapped part of the X-ray image;
and C: selecting high-energy information and low-energy information of non-overlapped parts of articles made of different materials for the non-overlapped parts, and finding out the color mapping relation of article overlapping through linear regression;
step D: for the overlapped part of the X-ray images, obtaining outlines and classifications of contraband articles in the articles through an example segmentation algorithm in the step B, wherein each classification corresponds to a material, finding out the positions, which are not in the atomic number range of the material, in the outlines, obtaining high-energy information and low-energy information of the positions from the high-energy spectrum gray-scale image and the low-energy spectrum gray-scale image, and calculating the atomic number of the materials after correction through the high-energy information and the low-energy information;
step E: and obtaining whether the substance is a contraband or not through the corrected atomic number.
Further, in the step a, the X-ray machine is a dual-source X-ray machine.
Further, in step B, the example segmentation algorithm is a Mask R-CNN framework.
Further, in step D, the method for calculating the corrected atomic number of the substance through the high energy information and the low energy information includes:
suppose Hx,y,Lx,yThe high energy spectrogram is a value corresponding to a pixel position (x, y) of the low energy spectrogram;
(Rx,y，Gx,y，Bx,y) RGB values corresponding to the (x, y) position;
for each material, using a series of (x, y) position-corresponding (R) in the non-overlapping set S of points for such materialx,y,Gx,y,Bx,y,Hx,y,Lx,y) Regression of a set of parameters (a)R,aG,aB,bR,bG,bB,cR,cG,cB)；
The specific relationship is shown in the following formula:
Rx,y＝aRHx,y+bRLx,y+cR (1)；
Gx,y＝aGHx,y+bGLx,y+cG (2)；
Bx,y＝aBHx,y+bBLx,y+cB (3)；
(x,y)∈S；
for each material, a set of parameter estimates can be obtained by linear regression:
Figure BDA0002662128390000031
c, calculating the high energy information and the low energy information H obtained in the step Cx,y,Lx,yThe adjusted RGB values of the overlapped portion may be calculated and then taken into the formula (4) to obtain the corrected atomic number, instead of the formula (1), the formula (2), and the formula (3).
Compared with the prior art, the invention has the beneficial effects that: according to the intelligent security inspection method for effectively improving the contraband identification rate, the Mask R-CNN framework is used for carrying out example segmentation on the X-ray image, so that the X-ray image can be layered and the overlapped part can be restored, the robustness of a loss function is improved, the shape and color information of an individual object can be obtained, the influence of object overlapping on the identification rate is overcome, and the identification accuracy rate and stability are greatly improved.
Drawings
FIG. 1 is a schematic structural view of example 1 of the present invention;
fig. 2 is a flowchart of embodiment 2 of the present invention.
In the figure: 100. a transmission belt; 200. a dual source X-ray machine; 300. a security inspection machine; 400. a display screen.
Detailed Description
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, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the 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.
Example 1
A security inspection device, as shown in FIG. 1, comprises a transmission belt 100, a dual-source X-ray machine 200, a security inspection machine 300 and a display screen 400; the conveyor belt 100 is used to transport objects; the dual-source X-ray machine 200 is arranged on the transmission belt 100 and used for extracting X-ray pictures of articles; the security check machine 300 is used for detecting and analyzing the X-ray picture extracted by the dual-source X-ray machine 200; the display screen 400 is used for displaying the detection result of the security inspection machine 300.
Example 2
As shown in fig. 2, an intelligent security inspection method for effectively improving the identification rate of contraband is used for detecting and identifying contraband on an X-ray image by a security inspection machine, and includes the following steps:
step A: shooting an X-ray image of an article by using an X-ray machine, and outputting a high-energy spectrum gray-scale image and a low-energy spectrum gray-scale image;
and B: analyzing the X-ray image by adopting an example segmentation algorithm to find out the non-overlapped part and the overlapped part of the X-ray image;
and C: selecting high-energy information and low-energy information of non-overlapped parts of articles made of different materials for the non-overlapped parts, and finding out the color mapping relation of article overlapping through linear regression;
step D: for the overlapped part of the X-ray images, obtaining outlines and classifications of contraband articles in the articles through an example segmentation algorithm in the step B, wherein each classification corresponds to a material, finding out the positions, which are not in the atomic number range of the material, in the outlines, obtaining high-energy information and low-energy information of the positions from the high-energy spectrum gray-scale image and the low-energy spectrum gray-scale image, and calculating the atomic number of the materials after correction through the high-energy information and the low-energy information;
step E: and obtaining whether the substance is a contraband or not through the corrected atomic number.
Specifically, in the step a, the X-ray machine is a dual-source X-ray machine.
It is worth noting that in step B, the example segmentation algorithm is a Mask R-CNN framework.
In the step D, the method for calculating the corrected atomic number of the substance through the high-energy information and the low-energy information comprises the following steps:
suppose Hx,y,Lx,yThe high energy spectrogram is a value corresponding to a pixel position (x, y) of the low energy spectrogram;
(Rx,y，Gx,y，Bx,y) RGB values corresponding to the (x, y) position;
for each material, using a series of (x, y) position-corresponding (R) in the non-overlapping set S of points for such materialx,y,Gx,y,Bx,y,Hx,y,Lx,y) Regression of a set of parameters (a)R,aG,aB,bR,bG,bB,cR,cG,cB)；
The specific relationship is shown in the following formula:
Rx,y＝aRHx,y+bRLx,y+cR (1)；
Gx,y＝aGHx,y+bGLx,y+cG (2)；
Bx,y＝aBHx,y+bBLx,y+cB (3)；
(x,y)∈S；
for each material, a set of parameter estimates can be obtained by linear regression:
Figure BDA0002662128390000051
c, calculating the high energy information and the low energy information H obtained in the step Cx,y,Lx,yThe adjusted RGB values of the overlapped portion may be calculated and then taken into the formula (4) to obtain the corrected atomic number, instead of the formula (1), the formula (2), and the formula (3).
When the intelligent security inspection method for effectively improving the identification rate of the contraband is used, the X-ray image is subjected to example segmentation through the MaskR-CNN frame, so that the X-ray image shot by the X-ray machine can be layered and the overlapped part can be restored, the atomic number of the shot object is corrected, the robustness of a loss function is improved, the shape and color information of an individual object can be obtained, the influence of object overlapping on the identification rate is overcome, and the identification accuracy and stability are greatly improved.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and the preferred embodiments of the present invention are described in the above embodiments and the description, and are not intended to limit the present invention. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (4)

1. An intelligent security inspection method for effectively improving the identification rate of contraband is used for detecting and identifying the contraband on an X-ray image by a security inspection machine, and is characterized by comprising the following steps:
step A: shooting an X-ray image of an article by using an X-ray machine, and outputting a high-energy spectrum gray-scale image and a low-energy spectrum gray-scale image;
and B: analyzing the X-ray image by adopting an example segmentation algorithm to find out the non-overlapped part and the overlapped part of the X-ray image;
and C: selecting high-energy information and low-energy information of non-overlapped parts of articles made of different materials for the non-overlapped parts, and finding out the color mapping relation of article overlapping through linear regression;
step D: for the overlapped part of the X-ray images, obtaining outlines and classifications of contraband articles in the articles through an example segmentation algorithm in the step B, wherein each classification corresponds to a material, finding out the positions, which are not in the atomic number range of the material, in the outlines, obtaining high-energy information and low-energy information of the positions from the high-energy spectrum gray-scale image and the low-energy spectrum gray-scale image, and calculating the atomic number of the materials after correction through the high-energy information and the low-energy information;
step E: and obtaining whether the substance is a contraband or not through the corrected atomic number.
2. The intelligent security inspection method for effectively improving the identification rate of contraband according to claim 1, wherein: in the step A, the X-ray machine is a double-source X-ray machine.
3. The intelligent security inspection method for effectively improving the identification rate of contraband according to claim 1, wherein: in the step B, the example segmentation algorithm is a Mask R-CNN framework.
4. The intelligent security inspection method for effectively improving the identification rate of contraband according to claim 1, wherein: in the step D, the method for calculating the corrected atomic number of the substance through the high-energy information and the low-energy information comprises the following steps:
suppose Hx,y,Lx,yThe high energy spectrogram is a value corresponding to a pixel position (x, y) of the low energy spectrogram;
(Rx,y，Gx,y，Bx,y) RGB values corresponding to the (x, y) position;
for each material, using a series of (x, y) position-corresponding (R) in the non-overlapping set S of points for such materialx,y,Gx,y,Bx,y,Hx,y,Lx,y) Regression of a set of parameters (a)R,aG,aB,bR,bG,bB,cR,cG,cB)；
The specific relationship is shown in the following formula:
Rx,y＝aRHx,y+bRLx,y+cR (1)；
Gx,y＝aGHx,y+bGLx,y+cG (2)；
Bx,y＝aBHx,y+bBLx,y+cB (3)；
(x,y)∈S；
for each material, a set of parameter estimates can be obtained by linear regression:
Figure FDA0002662128380000021
c, calculating the high energy information and the low energy information H obtained in the step Cx,y,Lx,yThe adjusted RGB values of the overlapped portion may be calculated and then taken into the formula (4) to obtain the corrected atomic number, instead of the formula (1), the formula (2), and the formula (3).
CN202010907854.3A 2020-09-02 2020-09-02 Intelligent security inspection method for effectively improving contraband identification rate Pending CN112162324A (en)

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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

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