CN111860578B - Image pre-classification method for airport security inspection contraband automatic identification system - Google Patents
Image pre-classification method for airport security inspection contraband automatic identification system Download PDFInfo
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- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
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- G06V10/40—Extraction of image or video features
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- G06V10/40—Extraction of image or video features
- G06V10/56—Extraction of image or video features relating to colour
Abstract
The invention provides an image pre-classification method of an airport security inspection contraband automatic identification system, which comprises the following steps of S1: obtaining the front and side photographs of an X-ray machine image, completing RGB and HSI conversion of an input image through the side photographs and the front photographs, adopting color segmentation, judging whether a black area exists in the image through a black threshold value Vth, wherein the area of the area is larger than 3 multiplied by 3pixel, and alarming when the front photographs and the side photographs both have unknown areas so as to remind security personnel to open a package for inspection; s2, filtering and denoising the front illumination and the side illumination processed in the S1, and extracting HOG characteristics and LBP characteristics; s3: constructing a comprehensive characteristic vector X (HOG _ V, LBP _ V, HOG _ S and LBP _ S) in a serial cascade mode; s4: and (3) a Support Vector Machine (SVM) classifier is adopted to complete the pre-classification of comprehensive characteristic vectors X (HOG _ V, LBP _ V, HOG _ S and LBP _ S) of the positive shot and the side shot of contraband, and when the contraband exists, true is sent to an alarm to trigger the alarm. The method disclosed by the invention integrates the positive-shot X-ray image and the side-shot X-ray image, constructs the combined feature vector, realizes the rough classification of contraband, and improves the overall robustness and real-time performance of a classification algorithm.
Description
Technical Field
The invention relates to the field of airport security inspection, in particular to an image pre-classification method of an airport security inspection baggage contraband auxiliary identification system.
Background
With the economic development of China, the civil aviation industry enters a development express way, and the construction of an airport serving as a supporting facility of the civil aviation industry also enters a rapid development stage. In particular, in recent years, airport passenger throughput has been increasing year by year, and the security pressure of airport security systems has been increasing year by year. Whether the flight safety of the airplane can be effectively protected and the safety precaution of the airport can be comprehensively improved becomes a problem to be solved urgently. The airport is used as a transportation hub, the personnel flow is large, and the requirement of the safety protection level is relatively high. However, the conventional monitoring stage of the X-ray machine of the airport still remains, and in the conventional mode, the X-ray machine needs to look out of the eyes of the X-ray machine, so that the X-ray machine is highly stressed to monitor the video picture of the X-ray machine, and the work intensity is high. Although the diagnostician generally has a 'shift of half an hour', the diagnostician is far beyond the limit of human beings with the continuous appearance of novel dangerous goods, a severe challenge is provided for a manual diagnostician mode depending on memory and experience, and misjudgment and even missed judgment are frequent. Therefore, an airport security inspection baggage contraband identification system for assisting manual image judgment is needed to improve airport security.
Disclosure of Invention
In order to solve the problems, an image pre-classification method of an airport security inspection contraband automatic identification system is provided.
The object of the invention is achieved in the following way:
an image pre-classification method of an airport security inspection contraband automatic identification system comprises
S1: obtaining the front and side photographs of an X-ray machine image, completing RGB and HSI conversion of an input image through the side photographs and the front photographs, adopting color segmentation, judging whether a black area exists in the image through a black threshold value Vth, wherein the area of the area is larger than 3 multiplied by 3pixel, and alarming when the front photographs and the side photographs both have unknown areas so as to remind security personnel to open a package for inspection;
s2, filtering and denoising the front illumination and the side illumination of the X-ray machine image, and extracting HOG characteristics and LBP characteristics;
s3: constructing a comprehensive characteristic vector X (HOG _ V, LBP _ V, HOG _ S and LBP _ S) in a serial cascade mode;
s4: and (3) finishing pre-classifying comprehensive characteristic vectors X (HOG _ V, LBP _ V, HOG _ S and LBP _ S) of the positive and side photographs of the contraband by adopting a Support Vector Machine (SVM) classifier, and sending true to an alarm to trigger alarm when the contraband exists.
Extracting HOG characteristics in S2, and firstly, completing the construction of an image standardized Gamma space and a color space; secondly, calculating gradients in the x direction and the y direction in the gray level image, capturing outline information, setting the size n multiplied by n of cells, forming each m cells into a block (block), and grading the gradient range; and counting the features contained in all the cells in the block again, and connecting all the block features in series to construct a feature vector.
The front and side lighting of the X-ray machine image comprises the output of two channels, namely VGA or Ethernet, and data acquisition of the two channels is completed through FPGA.
The FPGA adopts a XILINX XC7K325T3FFG900I chip to finish preprocessing work such as image data filtering noise reduction, integral graph synthesis, dynamic gain adjustment and the like, wherein median filtering is adopted to carry out filtering noise reduction, and a gain coefficient g is dynamically adjusted through image average gray scale in an integral graph.
A readable storage medium having stored thereon an executable program which, when executed by a processor, performs the steps of the above method.
An electronic device, comprising:
a memory having an executable program stored thereon;
a processor for executing the executable program in the memory to implement the steps of the above method.
The invention has the beneficial effects that: compared with the prior art, the method disclosed by the invention integrates the positive-shot and side-shot X-ray images, constructs the combined feature vector, realizes the rough classification of contraband, and improves the overall robustness and real-time performance of the classification algorithm.
Drawings
FIG. 1 is a diagram of a working scenario of an apparatus according to the present invention;
FIG. 2 is a block diagram of the components of the apparatus according to the present invention;
fig. 3 is a LBP + HOG based feature vector according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present application are clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments that can be derived from the embodiments given herein by a person of ordinary skill in the art are intended to be within the scope of the present disclosure.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same technical meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
In the present invention, terms such as "fixedly connected", "connected", and the like are to be understood in a broad sense, and mean either a fixed connection or an integrally connected or detachable connection; may be directly connected or indirectly connected through an intermediate. The specific meanings of the above terms in the present invention can be determined according to specific situations by persons skilled in the art, and should not be construed as limiting the present invention.
An image pre-classification method of an airport security inspection contraband automatic identification system comprises
S1: obtaining the front and side illumination of an X-ray machine image, completing RGB and HSI conversion of an input image through the side illumination and the front illumination, wherein Q (R, G and B respectively represent red, green and blue components of a color image, H (Hue) represents Hue, S (Saturation) represents Saturation, I (Intensity) represents brightness, and theta represents a Hue angle;
s2, filtering and denoising the front illumination and the side illumination of the X-ray machine image, and extracting HOG characteristics and LBP characteristics;
s3: constructing a comprehensive characteristic vector X (HOG _ V, LBP _ V, HOG _ S and LBP _ S) in a serial cascade mode;
s4: and (3) finishing pre-classifying comprehensive characteristic vectors X (HOG _ V, LBP _ V, HOG _ S and LBP _ S) of the positive and side photographs of the contraband by adopting a Support Vector Machine (SVM) classifier, and sending true to an alarm to trigger alarm when the contraband exists.
Extracting HOG characteristics in S2, and firstly, completing the construction of an image standardized Gamma space and a color space; secondly, calculating gradients in the x direction and the y direction in the gray level image, capturing outline information, setting the size n multiplied by n of cells, forming each m cells into a block (block), and grading the gradient range; and counting the features contained in all the cells in the block again, and connecting all the block features in series to construct a feature vector.
The front and side lighting of the X-ray machine image comprises the output of two channels, namely VGA or Ethernet, and data acquisition of the two channels is completed through FPGA.
The FPGA adopts a XILINX XC7K325T3FFG900I chip to finish preprocessing work such as image data filtering noise reduction, integral graph synthesis, dynamic gain adjustment and the like, wherein median filtering is adopted to carry out filtering noise reduction, and a gain coefficient g is dynamically adjusted through image average gray scale in an integral graph.
A readable storage medium having stored thereon an executable program which, when executed by a processor, performs the steps of the above method.
An electronic device, comprising:
a memory having an executable program stored thereon;
a processor for executing the executable program in the memory to implement the steps of the above method.
As shown in fig. 1, combining the openness of different manufacturers, firstly, image data reception of an X-ray host computer can be realized through 2 channels (VGA or ethernet), preprocessing technologies such as median filtering, histogram, dynamic gain adjustment and image restoration are adopted, and the front-view and side-view feature information is fused by combining LBP and HOG, so that if an object with unknown black or a contraband with obvious features is found, an alarm can be triggered to prompt security personnel to unpack and check. The hardware of the device adopts the FPGA + GPU technology, as shown in figure 2, the FPGA adopts a XILINX XC7K325T3FFG900I chip to cache data by reading two channels, and sends the data to the GPU after finishing related preprocessing work. The GPU adopts NVIDEA TX2 which is provided with 256 independent general processing units and can have 256 threads which run independently, namely Cuda _ Thread, in a CUDA environment, the feature vector construction is achieved through a GPU parallel processing technology, and an operating system can run on an ARM core. The target pre-classification method is shown in fig. 3, and the specific implementation steps are as follows:
STEP1, firstly, finishing the conversion of RGB and HSI of an input image through side illumination and front illumination, adopting color segmentation, judging whether a black area exists in the image through a black threshold value Vth, wherein the area of the area is more than 3 multiplied by 3pixel, if the condition is satisfied, returning true, otherwise false. If and only if the front and side photographs have unknown areas, an alarm can be given to remind security personnel to open a package for inspection;
STEP2, extracting HOG characteristics after filtering and denoising, and firstly completing the construction of an image standardized Gamma space and a color space; secondly, calculating gradients in the x direction and the y direction in the gray level image, capturing outline information, setting the size n multiplied by n of cells, forming each m cells into a block (block), and grading the gradient range; counting the characteristics contained in all the cells in the block again, and constructing a characteristic vector by using all the block characteristics;
STEP3: considering that the random placement of objects in the passenger luggage is large, the problem of rotation caused by different placement angles is solved by combining a local binary pattern LBPs method, and the texture descriptor is defined as follows:
wherein the content of the first and second substances,
STEP4: the construction of comprehensive characteristic vectors X (HOG _ V, LBP _ V, HOG _ S and LBP _ S) is completed in a serial cascade mode;
STEP5, a Support Vector Machine (SVM) classifier is adopted to complete the pre-classification of comprehensive characteristic vectors X (HOG _ V, LBP _ V, HOG _ S and LBP _ S) of the positive pictures and the side pictures of the contraband, and when the contraband exists, true is sent to an alarm to trigger the alarm.
The invention can assist security inspectors to quickly and efficiently identify contraband by adopting a parallel processing means when carrying out security inspection on passengers or luggage in airports, reduces the labor intensity and the working efficiency of security inspectors in airports, and effectively improves the identification rate of dangerous goods. X-ray sensors are arranged at the positions where X-ray machine passengers and security personnel appear for real-time detection to guarantee personnel security, and a cloud platform management technology is adopted, so that big data processing, analysis and management are facilitated, and the system is more intelligent.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.
Claims (6)
1. An image pre-classification method of an airport security inspection contraband automatic identification system is characterized in that: comprises that
S1: obtaining the front photograph and the side photograph of an X-ray machine image, completing RGB and HSI conversion of an input image through the side photograph and the front photograph, adopting color segmentation, judging whether a black area exists in the image through a black threshold value Vth, wherein the area of the area is larger than 3 multiplied by 3pixel, and alarming to remind security check personnel to open a bag for checking when the front photograph and the side photograph are unknown areas;
s2, filtering and denoising the front illumination and the side illumination of the X-ray machine image, and extracting HOG characteristics and LBP characteristics;
s3: constructing a comprehensive characteristic vector X (HOG _ V, LBP _ V, HOG _ S and LBP _ S) in a serial cascade mode;
s4: and (3) finishing pre-classifying comprehensive characteristic vectors X (HOG _ V, LBP _ V, HOG _ S and LBP _ S) of the positive and side photographs of the contraband by adopting a Support Vector Machine (SVM) classifier, and sending true to an alarm to trigger alarm when the contraband exists.
2. The image pre-classification method for the automatic identification system of the airport security inspection contraband as claimed in claim 1, characterized in that: extracting HOG characteristics in S2, and firstly, completing the construction of an image standardized Gamma space and a color space; secondly, calculating gradients in the x direction and the y direction in the gray level image, capturing outline information, setting the size of each cell to be n multiplied by n, forming each m cells into a block, and grading the gradient range; and counting the characteristics contained in all the cells in the block again, and connecting all the block characteristics in series to construct a characteristic vector.
3. The image pre-classification method for the automatic identification system of the airport security inspection contraband as claimed in claim 1, characterized in that: the front and side lighting of the X-ray machine image comprises the output of two channels, namely VGA or Ethernet, and data acquisition of the two channels is completed through the FPGA.
4. The image pre-classification method for the automatic identification system of the airport security inspection contraband as claimed in claim 3, characterized in that: the FPGA adopts a XILINX XC7K325T3FFG900I chip, the FPGA completes the work of filtering and noise reduction of image data, integral graph synthesis and dynamic gain adjustment pretreatment, wherein the filtering and noise reduction treatment is carried out by adopting median filtering, and the gain coefficient g is dynamically adjusted through the average gray level of an image in the integral graph.
5. A readable storage medium having stored thereon an executable program, wherein the executable program when executed by a processor implements the steps of the method of any one of claims 1 to 4.
6. An electronic device, comprising:
a memory having an executable program stored thereon;
a processor for executing the executable program in the memory to implement the steps of the method of any of claims 1-4.
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| US5600303A (en) * | 1993-01-15 | 1997-02-04 | Technology International Incorporated | Detection of concealed explosives and contraband |
| CN106250936A (en) * | 2016-08-16 | 2016-12-21 | 广州麦仑信息科技有限公司 | Multiple features multithreading safety check contraband automatic identifying method based on machine learning |
| CN106872498A (en) * | 2017-04-11 | 2017-06-20 | 西安培华学院 | A kind of contraband safety check automatic identification equipment |
| CN107607562A (en) * | 2017-09-11 | 2018-01-19 | 北京匠数科技有限公司 | A kind of prohibited items identification equipment and method, X-ray luggage security check system |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5600303A (en) * | 1993-01-15 | 1997-02-04 | Technology International Incorporated | Detection of concealed explosives and contraband |
| CN106250936A (en) * | 2016-08-16 | 2016-12-21 | 广州麦仑信息科技有限公司 | Multiple features multithreading safety check contraband automatic identifying method based on machine learning |
| CN106872498A (en) * | 2017-04-11 | 2017-06-20 | 西安培华学院 | A kind of contraband safety check automatic identification equipment |
| CN107607562A (en) * | 2017-09-11 | 2018-01-19 | 北京匠数科技有限公司 | A kind of prohibited items identification equipment and method, X-ray luggage security check system |
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