CN113076880A

CN113076880A - Hazardous article pattern recognition algorithm

Info

Publication number: CN113076880A
Application number: CN202110369262.5A
Authority: CN
Inventors: 李元伟
Original assignee: Guangdong Industry Technical College
Current assignee: Guangdong Industry Technical College
Priority date: 2021-04-06
Filing date: 2021-04-06
Publication date: 2021-07-06
Anticipated expiration: 2041-04-06
Also published as: CN113076880B

Abstract

The invention discloses a graphic identification algorithm for dangerous goods, belonging to the field of electronics and information technology. The specific steps of the identification algorithm are as follows: (1) X-ray detection; (2) image wavelet decomposition; (3) image wavelet reconstruction; (4) image wavelet reconstruction; ) morphological filling; (5) image matching; (6) characteristic curve detection; the present invention uses X-ray images for shape detection and matching for objects with an average effective atomic number greater than 10, and for objects with an average effective atomic number less than 10, The characteristic curve fitted by its high and low gray value is used to match the characteristic curve of standard dangerous goods; the combination of the two methods to detect the dangerous goods can comprehensively detect the dangerous goods carried in the luggage, which is conducive to improving the The efficiency of dangerous goods detection is conducive to reducing the labor intensity of security inspectors.

Description

Hazardous article pattern recognition algorithm

Technical Field

The invention relates to the technical field of electronics and information, in particular to a dangerous goods pattern recognition algorithm.

Background

Through retrieval, the Chinese patent number CN105427712B discloses a dangerous goods automatic identification device and method based on three-dimensional X-ray imaging, the detection method of the X-ray detection system is single, and the detection accuracy is low; at present, safety inspection equipment such as magnetic needles, metal weapon detection doors, X-ray detectors and the like are mostly arranged at places needing safety inspection such as airports, stations, government buildings, prisons and the like, and can find dangerous goods such as weapons, common explosives and the like, so that the safety inspection equipment plays an important role in safety inspection work; however, the devices are limited by the original technical conditions, are not satisfactory in the actual use process, and have high false alarm rate and missed detection rate; with the development of scientific technology, criminals and terrorists have gradually begun to use high and new technologies to manufacture new weapons, explosives, and the like; like high-precision bombs, plastic explosives, many drugs and the like manufactured by utilizing an integrated circuit technology, the traditional detection means cannot be used; therefore, it becomes important to invent a dangerous goods pattern recognition algorithm;

the existing dangerous goods identification method mostly adopts the traditional safety inspection equipment to cooperate with safety inspection personnel to identify dangerous goods, although dangerous goods such as weapons, common explosives and the like can be found, the labor intensity of the safety inspection personnel is easily increased, and the traditional safety inspection equipment is limited by the original technical conditions, so that high-precision bombs, plastic explosives, a plurality of drugs and the like manufactured by the integrated circuit technology cannot be quickly identified, and the identification effect is not ideal for low-density goods such as kerosene, ethanol and the like; therefore, a dangerous goods pattern recognition algorithm is proposed.

Disclosure of Invention

The invention aims to solve the defects in the prior art and provides a dangerous article image recognition algorithm.

In order to achieve the purpose, the invention adopts the following technical scheme:

a dangerous goods pattern recognition algorithm comprises the following specific steps:

(1) x-ray detection: obtaining an average effective atomic sequence value of an object image to be detected by an X-ray detector, jumping to the step (2) to perform image wavelet decomposition on the object image to be detected if the average effective atomic sequence value is greater than or equal to 10, and jumping to the step (6) to perform characteristic curve detection on the object image to be detected if the average effective atomic sequence value is less than or equal to 10;

(2) image wavelet decomposition: performing one-layer wavelet decomposition on an object image to be detected by adopting a wavelet decomposition method, wherein each layer of image is decomposed into a low-frequency wavelet coefficient matrix and a high-frequency wavelet coefficient matrix, the low-frequency coefficient represents an approximate part of the image, and the high-frequency wavelet coefficient matrix comprises coefficients in the horizontal direction, the vertical direction and the diagonal direction;

(3) and (3) image wavelet reconstruction: carrying out edge detection on an image approximate part, namely a low-frequency part by using a Canny algorithm to obtain a sub-image of the low-frequency part; simultaneously, utilizing Roberts and Sobel algorithms to carry out edge detection on coefficients in the horizontal direction, the vertical direction and the diagonal direction of the image, namely the high-frequency part, so as to obtain a sub-image of the high-frequency part; performing wavelet reconstruction on the sub-image of the low-frequency part and the sub-image of the high-frequency part to obtain an edge image;

(4) morphological filling: performing morphological filling on the edge image by using a hole filling mode to obtain an enhanced edge image to be detected;

(5) image matching: constructing a dangerous article standard image, and matching the enhanced edge image to be detected with the dangerous article standard image by using a curvature contour angular point method to obtain a dangerous article judgment result of a curvature angular point;

(6) and (3) characteristic curve detection: and constructing a characteristic curve of the common dangerous goods, and simultaneously carrying out dangerous goods detection matching on the image of the object to be detected by adopting an inorganic matter characteristic curve method to obtain a curved dangerous goods judgment result.

Preferably, the wavelet decomposition of the image in the step (2) needs a scale function in a two-dimensional case

And two-dimensional wavelet psi_H(x,y)、ψ_V(x, y) and ψ_D(x, y), each of which can be expressed as a one-dimensional scale function

And ψ, as follows:

its two-dimensional separable direction-sensitive wavelet can be represented as:

ψ_D(x,y)＝ψ(x)ψ(y) (4)

in the formula: psi_H、ψ_VAnd psi_DRespectively representing the image intensity variation in different directions.

Preferably, given the separable two-dimensional scale and wavelet functions, the scale and wavelet basis functions are defined as follows:

in the formula: i ═ { H, V, D } represents wavelets in different directions;

then an f (x, y) discrete wavelet transform of size M × N:

in the formula: j is a function of₀In order to be the initial scale, the method comprises the following steps,

define a dimension of j₀F (x, y) approximation image; w_ψ(j, m, n) contains detail images in horizontal, vertical, and diagonal directions; let j₀0, and N is selected to be M2^jJ-0, 1,2, …, j-1 and m, n-0, 1,2, … 2^j-1。

Preferably, the wavelet reconstruction formula in step (3) is as follows:

preferably, the hole filling formula in step (4) is as follows:

in the formula: k 1,2,3.

Preferably, the curvature in step (5) is mathematically defined as follows: taking an arc section from a point M on the smooth arc, wherein the length of the arc section is delta s, and the corresponding tangent rotation angle is delta alpha, and defining the arc section;

wherein the average curvature over Δ S is:

the curvature at point M is:

the formula for calculating the discrete curvature by using a curve parameter equation is as follows:

preferably, the curvature in step (5) includes global and local curvatures of the image, and the specific calculation process is as follows:

s1: calculating the average curvature of each to-be-detected enhanced edge image profile;

s2: then, judging the current contour point by taking the average curvature of the contour as a threshold, and if the current curvature is smaller than the threshold, determining the current contour point as a real contour angular point;

s3: after the angular points of all to-be-detected enhanced edge images are extracted, performing angular point matching on the to-be-detected enhanced edge images and the standard images of the dangerous goods;

when the corner points are matchedBuilding a corner feature descriptor, wherein the corner feature descriptor is defined as: suppose P₁、P₂And P₃Three adjacent corner points on the contour curve S with curvatures of K₁、K₂And K₃，P₁To P₂Is a distance L₁，P₂To P₃Is L₂) Then P is₂The descriptor B is:

B＝(L₁+L₂)(K₃-K₁) (14)。

preferably, the image matching in step (5) specifically comprises the following steps:

SS 1: extracting real corner points of a complete edge image with distinct outline features and a standard image of the dangerous goods;

SS 2: respectively solving descriptors B and B' of each enhanced edge image corner point to be detected and each dangerous article standard image corner point by using a formula (14);

SS 3: performing sequential difference operation on each corner point descriptor in the enhanced edge image to be detected and all corner point descriptors in the standard image of the hazardous article, wherein if the ratio of the first term to the second term of the vector of the difference value is smaller than a threshold value, and the threshold value is 0.5, the diagonal points of the two images are matched, otherwise, the diagonal points are not matched;

SS 4: and counting the matching similarity percentage of the corner points of the same target contour, and if the similarity is greater than a preset value, determining that the article is a dangerous article.

Preferably, the specific process of detecting the characteristic curve in the step (6) is as follows:

SSS 1: acquiring high and low energy gray values of an object image to be detected through an X-ray security inspection machine, and drawing a characteristic curve of the object image to be detected according to the high and low energy gray values;

SSS 2: constructing a characteristic curve of a common dangerous article, and taking the characteristic curve as a standard for detecting the dangerous article;

SSS 3: comparing the characteristic curve of the image of the object to be detected with the characteristic curve of the common dangerous goods; and if the characteristic curve of the image of the object to be detected is close to the characteristic curve of the dangerous goods, judging that the object is the dangerous goods, otherwise, judging that the object is normal.

Compared with the prior art, the invention has the beneficial effects that:

1. according to the method, the average effective atomic number value of an object is obtained by an X-ray detector, the detected object is divided into two types, the shape of the object with the average effective atomic number larger than 10 is detected and matched by adopting an X-ray image, and for the target with the average effective atomic number smaller than 10, a characteristic curve fitted by using the high-low gray value of the target is matched with a characteristic curve of a standard dangerous article; the dangerous goods are detected by combining two modes, so that the dangerous goods carried in the luggage can be comprehensively detected, the efficiency of dangerous goods detection is favorably improved, and the labor intensity of security personnel is favorably reduced.

Drawings

The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention.

Fig. 1 is an overall flowchart of a hazardous material pattern recognition algorithm according to the present invention.

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.

In the description of the present invention, it is to be understood that the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention.

Example 1

In the step (2), the image wavelet decomposition needs a scale function under the condition of two dimensions

And ψ, as follows:

ψ_D(x,y)＝ψ(x)ψ(y) (4)

After the two-dimensional scale and wavelet function are given separately, the scale and wavelet basis functions are defined as follows:

in the formula: i ═ { H, V, D } represents wavelets in different directions;

then an f (x, y) discrete wavelet transform of size M × N:

The wavelet reconstruction formula in the step (3) is as follows:

the hole filling formula in the step (4) is as follows:

in the formula: k 1,2,3.

The mathematical definition of the curvature of step (5) is as follows: taking an arc section from a point M on the smooth arc, wherein the length of the arc section is delta s, and the corresponding tangent rotation angle is delta alpha, and defining the arc section;

wherein the average curvature over Δ S is:

the curvature at point M is:

the curvature in the step (5) comprises global curvature and local curvature of the image, and the specific calculation process is as follows:

when the corner points are matched, a corner point feature descriptor is constructed, wherein the corner point feature descriptor is defined as: suppose P₁、P₂And P₃Three adjacent corner points on the contour curve S with curvatures of K₁、K₂And K₃，P₁To P₂Is a distance L₁，P₂To P₃Is L₂) Then P is₂The descriptor B is:

B＝(L₁+L₂)(K₃-K₁) (14)。

the image matching in the step (5) comprises the following specific steps:

The specific process of detecting the characteristic curve in the step (6) is as follows:

In order to verify the image matching effect, an X-ray dangerous goods image with the size of 256 × 256 is selected as a sample (the sample comprises a pistol, handcuffs and a grenade), simulation is carried out by adopting matlab7.1, and the specific test data is as follows:

example 2

(6) and (3) characteristic curve detection: constructing a common dangerous article characteristic curve, and simultaneously carrying out dangerous article detection matching on an object image to be detected by adopting an inorganic matter characteristic curve method to obtain a curved dangerous article judgment result;

the rest is the same as example 1.

In order to utilize the verification characteristic curve to detect the effect of inorganic dangerous goods, firecrackers with different thicknesses are selected in the test, the firecrackers are detected through the established firecracker characteristic curve, and the test detection data are as follows:

example 3

the rest is the same as example 1.

In order to test the effectiveness of the invention, several articles including a cutter, a toy gun, bottled alcohol, gasoline and the like are selected as test samples, the test samples are respectively and independently mixed with other articles, a dangerous article shape image database (28 types) and common dangerous article characteristic curves (15 types) are established as dangerous article judgment standards, a plurality of tests are carried out in different directions and positions, and the detection accuracy rates of different articles are specifically as follows:

article with a cover	Number of samples	Rate of accuracy	False detection rate	Rate of missed examination
					Cutting tool	80	91.25％	7.50％	8.13％
Toy gun	80	87.50％	5.20％	7.50％
					Gasoline (gasoline)	80	92.00％	6.25％	5.00％
Bottled alcohol	80	90.00％	5.00％	7.13％

As can be seen from the data tables of the embodiment 1, the embodiment 2 and the embodiment 3, the dangerous goods object detection accuracy of the invention is high, and the false detection rate and the omission factor are both below 10%, so that the invention can comprehensively detect the dangerous goods carried in the luggage, improve the efficiency of dangerous goods detection, and is beneficial to reducing the labor intensity of security personnel.

The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.

Claims

1. a dangerous goods graphic identification algorithm, is characterized in that, the concrete steps of this identification algorithm are as follows:

(1) X-ray detection: The average effective atomic number value of the image of the object to be measured is obtained by the X-ray detector. If the value is greater than or equal to 10, skip to step (2) to perform image wavelet decomposition on the image of the object to be measured. When less than or equal to 10, skip to step (6) to perform characteristic curve detection on the image of the object to be measured;

(2) Image wavelet decomposition: The image of the object to be measured is decomposed by one layer of wavelet decomposition, and each layer of image is decomposed into low-frequency and high-frequency wavelet coefficient matrices. The frequency contains horizontal, vertical and diagonal direction coefficients;

(3) Image wavelet reconstruction: use the Canny algorithm to detect the edge of the approximate part of the image, that is, the low-frequency part, and obtain the sub-image of the low-frequency part; That is, edge detection is performed on the high-frequency part to obtain the sub-image of the high-frequency part; the sub-image of the low-frequency part and the sub-image of the high-frequency part are subjected to wavelet reconstruction to obtain the edge image;

(4) Morphological filling: the edge image is morphologically filled by using the hole filling method to obtain the enhanced edge image to be tested;

(5) Image matching: Construct a standard image of dangerous goods, and use the contour corner point method of curvature to match the enhanced edge image to be tested and the standard image of dangerous goods, and obtain the judgment result of dangerous goods at the corners of curvature;

(6) Characteristic curve detection: Construct the characteristic curve of common dangerous goods, and at the same time use the inorganic characteristic curve method to detect and match the image of the object to be measured, and obtain the dangerous goods judgment result of the curve.

2. A kind of dangerous goods graphic recognition algorithm according to claim 1, is characterized in that, in the case of two-dimensional image wavelet decomposition of step (2), scale function is required

(x, y) and two-dimensional wavelets ψ _H (x, y), ψ _V (x, y) and ψ _D (x, y), all of which can be expressed as one-dimensional scaling functions

and the product of the corresponding wavelet function of ψ, as follows:

Its two-dimensional separable direction-sensitive wavelet can be expressed as:

ψ _D (x, y) = ψ(x)ψ(y) (4)

In the formula: ψ _H , ψ _V and ψ _D represent the image intensity changes along different directions, respectively.

3. a kind of dangerous goods graphic identification algorithm according to claim 2, is characterized in that, after described separable two-dimensional scale and wavelet function are given, define scale and wavelet basis function, as follows:

In the formula: i={H, V, D} represents wavelets in different directions;

Then the discrete wavelet transform of f(x, y) with size M×N is:

where: j ₀ is the initial scale,

Define an approximate image of scale j ₀ , f(x, y); W _ψ (j, m, n) contains detail images in horizontal, vertical and diagonal directions; let j ₀ =0, and choose N=M=2 ^j , j=0, 1, 2, ..., j-1 and m, n=0, 1, 2, ... 2 ^j -1.

4. a kind of dangerous goods graphic identification algorithm according to claim 1, is characterized in that, the described wavelet reconstruction formula of step (3) is as follows:

5. a kind of dangerous goods graphic identification algorithm according to claim 1, is characterized in that, the described hole filling formula of step (4) is as follows:

In the formula: k=1, 2, 3....

6. a kind of dangerous goods figure recognition algorithm according to claim 1 is characterized in that, the mathematical definition of the curvature described in step (5) is as follows: on smooth arc, take arc segment from point M, and its length is Δs, The corresponding tangent angle is △α, which defines the arc segment;

Among them, the average curvature on △S is:

The curvature at point M is:

Its discrete curvature is calculated by the curve parameter equation as follows:

7. A kind of dangerous goods graphic recognition algorithm according to claim 1, is characterized in that, the described curvature of step (5) comprises image global and local curvature, and its concrete calculation process is as follows:

S1: Calculate the average curvature of the contour of each enhanced edge image to be tested;

S2: Then use the average curvature of the contour as the threshold to judge the current contour point, if the current curvature is less than the threshold, it is the real contour corner;

S3: After extracting the corners of all the enhanced edge images to be tested, perform corner matching between the enhanced edge images to be tested and the standard image of dangerous goods;

When the corner points are matched, a corner point feature descriptor is constructed, and the corner point feature descriptor is defined as: Suppose P ₁ , P ₂ and P ₃ are three adjacent corner points on the contour curve S, and their curvatures are K ₁ , P 2 and P 3 respectively. K ₂ and K ₃ , the distance from P ₁ to P ₂ is L ₁ , and P ₂ to P ₃ is L ₂ ), then the descriptor B of P ₂ is:

B=(L ₁ +L ₂ )(K ₃ −K ₁ ) (14).

8. a kind of dangerous goods graphic identification algorithm according to claim 1, is characterized in that, the concrete steps of image matching described in step (5) are as follows:

SS1: Extract the complete edge map with distinct contour features and the real corners of the standard image of dangerous goods;

SS2: Use formula (14) to find the descriptors B and B′ of each corner of the enhanced edge image to be tested and the corner of the standard image of dangerous goods;

SS3: Perform sequential difference operation between each corner descriptor in the enhanced edge image to be tested and all corner descriptors in the standard image of dangerous goods. If the ratio between the first item and the second item of the difference vector vector is less than the threshold, the threshold is 0.5, the diagonal points of the two images match, otherwise, they do not match;

SS4: Count the matching similarity percentage of the corner points of the same target contour. If the similarity is greater than the preset value, the item is a dangerous item.

9. A kind of dangerous goods graphic identification algorithm according to claim 1, is characterized in that, the described characteristic curve detection specific process of step (6) is as follows:

SSS1: Obtain the high and low energy gray value of the image of the object to be tested through the X-ray security inspection machine, and draw the characteristic curve of the image of the object to be tested according to the high and low energy gray value;

SSS2: Construct the characteristic curve of common dangerous goods and use it as the standard for detecting dangerous goods;

SSS3: Compare the characteristic curve of the image of the object to be measured with the characteristic curve of common dangerous goods; if the characteristic curve of the image of the object to be measured is close to the characteristic curve of the dangerous goods, it is judged that the item is a dangerous goods; otherwise, it is judged to be normal.