CN110711718A - Express package intelligent sorting system and method based on X-ray image and storage medium - Google Patents
Express package intelligent sorting system and method based on X-ray image and storage medium Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
- B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
- B07C5/00—Sorting according to a characteristic or feature of the articles or material being sorted, e.g. by control effected by devices which detect or measure such characteristic or feature; Sorting by manually actuated devices, e.g. switches
- B07C5/34—Sorting according to other particular properties
- B07C5/3416—Sorting according to other particular properties according to radiation transmissivity, e.g. for light, x-rays, particle radiation
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
- B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
- B07C5/00—Sorting according to a characteristic or feature of the articles or material being sorted, e.g. by control effected by devices which detect or measure such characteristic or feature; Sorting by manually actuated devices, e.g. switches
- B07C5/02—Measures preceding sorting, e.g. arranging articles in a stream orientating
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
- B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
- B07C5/00—Sorting according to a characteristic or feature of the articles or material being sorted, e.g. by control effected by devices which detect or measure such characteristic or feature; Sorting by manually actuated devices, e.g. switches
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Abstract
The embodiment of the invention discloses an intelligent express parcel sorting system, method and storage medium based on X-ray images, wherein express parcels are conveyed by a conveying device, pass through a scanning and detecting device and then arrive at a sorting device; the scanning and detecting device is used for transmitting the position information, the serial number information and the X-ray image of the express package to the data processing terminal; the data processing terminal is used for detecting the express package according to a preset detection algorithm, writing the X-ray image into a contraband class label when the express package is judged to contain contraband, displaying a detection result and giving an alarm; and issuing a sorting instruction carrying the position information and the serial number information to the sorting device; and the sorting device is used for sorting the express packages according to the sorting instruction. The invention can improve the inspection efficiency and accuracy of express packages and accurately identify and sort contraband.
Description
Technical Field
The invention relates to the technical field of automatic control, in particular to an intelligent express parcel sorting system and method based on X-ray images and a storage medium.
Background
In recent years, owing to the rapid development of electronic commerce, the express industry has come to explosive growth, and particularly, the express industry has experienced rapid development of acceleration of more than 50% on average in 2011-2015. The rapid development of the industry also causes great pressure in the aspect of express security inspection. Express delivery parcel is before delivering, whether have contraband in the parcel in advance need to detect, because the authenticity of the waybill information on the express delivery parcel can not obtain reliable assurance.
For safety, conventionally, a professional security inspector is required to manually observe an X-ray image of each cargo beside an X-ray machine to determine whether the cargo is forbidden, which requires great manpower and material resources, and the working efficiency is reduced along with the increase of the fatigue of the security inspector, and the situations of missing inspection and error inspection are correspondingly increased.
At present, the main method for distinguishing contraband in express delivery mainly depends on manpower to carry out security inspection, usually several groups of workers work in turn, each group comprises at least one professional security inspection worker and two sorting workers, and great manpower and material resources are consumed.
Disclosure of Invention
The invention aims to solve the technical problem of providing an intelligent express parcel sorting system, method and storage medium based on X-ray images, which can improve the inspection efficiency and accuracy of express parcels and accurately identify and sort contraband.
In a first aspect, an embodiment of the present invention provides an intelligent sorting system for express packages based on X-ray images, which includes a data processing terminal, a conveying device, a scanning and detecting device, and a sorting device;
the express packages are conveyed by the conveying device, pass through the scanning and detecting device and then reach the sorting device;
the scanning and detecting device is used for transmitting the position information, the serial number information and the X-ray image of the express package to the data processing terminal;
the data processing terminal is used for detecting the express package according to a preset detection algorithm, writing the X-ray image into a contraband class label when the express package is judged to contain contraband, displaying a detection result and giving an alarm; and issuing a sorting instruction carrying the position information and the serial number information to the sorting device;
and the sorting device is used for sorting the express packages according to the sorting instruction.
Preferably, the preset detection algorithm includes:
1) establishing an X-ray image database of the express package, dividing two categories of contraband and non-contraband, and setting the contraband as a target detection category;
2) and dividing the X-ray image into a plurality of connected regions, wherein each connected region is a pixel block with the size of 3X 3.
3) Extracting the characteristics of each connected region, specifically,
A. calculating characteristic variables of the connected region in a local rotation invariant and uniform mode by adopting the following formula:
B. setting the transformation frequency threshold of the characteristic variable parameters obtained in the step A as N times, and additionally adding N-dimensional characteristics on the basis of the characteristic variable dimensions, wherein the formula is as follows:
wherein P is the number of pixels in the neighborhood, R is the radius of the neighborhood, gpIs the gray value of the neighborhood pixel, gc(x) for center point gray pixel values, s (x) is a sign function, u (x) calculates 0/1 transformation times in local binary mode;
C. selecting a gradient operator, and calculating a directional gradient density characteristic histogram of the connected region:
wherein, M (x, y) is the gradient amplitude of the pixel, theta (x, y) is the gradient direction of the pixel, and theta (x, y) is divided into a plurality of bins so as to obtain a directional gradient density characteristic histogram;
D. combining the characteristic variables obtained in the steps to construct a characteristic descriptor:
feature＝concat(feature1,feature2,feature3,...,featureL)
4) constructing a learning classifier, training the characteristic represented by the characteristic descriptor, and learning a characteristic classification decision plane y which is wx + b;
A. the dimension of the input vector is the dimension of the feature characterized by the feature descriptor;
B. carrying out scale normalization processing on the characteristic variables:
C. an enhanced kernel function improved based on Manhattan distance and Euclidean distance is designed and adopted, and the formula is as follows:
taking the X-ray image of the express package as training data, training by the aid of the designed learning classifier, and learning a characteristic classification decision surface;
5) accessing real-time image data from an imaging system, and cutting the image based on the express package outline;
6) and constructing a feature descriptor of the express package graph after cutting processing, putting the feature descriptor into a trained learning model, and obtaining contraband label information of the express package.
Preferably, the conveying device comprises a driving motor and a conveying belt driven by the driving motor; the sorting device comprises a sorting controller, a mechanical arm and a sorting line.
Preferably, the scanning and detecting device comprises an X-ray security check device, and a first detecting device and a second detecting device which are respectively arranged at two ends of the X-ray security check device along the direction of the conveyor belt; the X-ray safety inspection device comprises a first detector device, a second detector device, a first data acquisition device, a first scanning controller, a second data acquisition device and a second scanning controller, wherein the first detector device comprises a first position sensor, a first data acquisition device and a first scanning controller; the sorting device comprises a sorting controller, a mechanical arm and a sorting line.
In a second aspect, an embodiment of the present invention further provides an intelligent sorting method for express packages based on X-ray images, which is applicable to a data processing terminal, and includes:
receiving the position information, the serial number information and the X-ray image of the express package transmitted by the scanning and detecting device;
detecting the express package according to a preset detection algorithm, writing the X-ray image into a contraband class label when the express package is judged to contain contraband, displaying a detection result and giving an alarm;
and issuing a sorting instruction carrying the position information and the serial number information to the sorting device so as to sort the express packages according to the sorting instruction.
Preferably, the preset detection algorithm includes:
1) establishing an X-ray image database of the express package, dividing two categories of contraband and non-contraband, and setting the contraband as a target detection category;
2) and dividing the X-ray image into a plurality of connected regions, wherein each connected region is a pixel block with the size of 3X 3.
3) Extracting the characteristics of each connected region, specifically,
A. calculating characteristic variables of the connected region in a local rotation invariant and uniform mode by adopting the following formula:
B. setting the transformation frequency threshold of the characteristic variable parameters obtained in the step A as N times, and additionally adding N-dimensional characteristics on the basis of the characteristic variable dimensions, wherein the formula is as follows:
C. selecting a gradient operator, and calculating a directional gradient density characteristic histogram of the connected region:
wherein, M (x, y) is the gradient amplitude of the pixel, theta (x, y) is the gradient direction of the pixel, and theta (x, y) is divided into a plurality of bins so as to obtain a directional gradient density characteristic histogram;
D. combining the characteristic variables obtained in the steps to construct a characteristic descriptor:
feature＝concat(feature1,feature2,feature3,...,featureL)
4) constructing a learning classifier, training the characteristic represented by the characteristic descriptor, and learning a characteristic classification decision plane y which is wx + b;
A. the dimension of the input vector is the dimension of the feature characterized by the feature descriptor;
B. carrying out scale normalization processing on the characteristic variables:
C. an enhanced kernel function improved based on Manhattan distance and Euclidean distance is designed and adopted, and the formula is as follows:
taking the X-ray image of the express package as training data, training by the aid of the designed learning classifier, and learning a characteristic classification decision surface;
5) accessing real-time image data from an imaging system, and cutting the image based on the express package outline;
6) and constructing a feature descriptor of the express package graph after cutting processing, putting the feature descriptor into a trained learning model, and obtaining contraband label information of the express package.
Preferably, the scanning and detecting device comprises an X-ray security check device, and a first detecting device and a second detecting device which are respectively arranged at two ends of the X-ray security check device along the direction of the conveyor belt; the X-ray safety inspection device comprises a first detector device, a second detector device, a first data acquisition device, a first scanning controller, a second data acquisition device and a second scanning controller, wherein the first detector device comprises a first position sensor, a first data acquisition device and a first scanning controller; the sorting device comprises a sorting controller, a mechanical arm and a sorting line.
In a third aspect, an embodiment of the present invention further provides a computer-readable storage medium, where the storage medium includes a stored computer program, where the apparatus on which the storage medium is located is controlled to execute the intelligent X-ray image-based sorting method for express packages when the computer program runs.
The embodiment of the invention has the following beneficial effects:
according to the intelligent express parcel sorting system and method based on the X-ray images, express parcels are conveyed by the conveying device, pass through the scanning and detecting device and then reach the sorting device; the scanning and detecting device is used for transmitting the position information, the serial number information and the X-ray image of the express package to the data processing terminal; the data processing terminal is used for detecting the express package according to a preset detection algorithm, writing the X-ray image into a contraband class label when the express package is judged to contain contraband, displaying a detection result and giving an alarm; and issuing a sorting instruction carrying the position information and the serial number information to the sorting device; and the sorting device is used for sorting the express packages according to the sorting instruction. The invention can improve the inspection efficiency and accuracy of express packages and accurately identify and sort contraband.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic structural diagram of an intelligent X-ray image-based parcel sorting system for express delivery according to an embodiment of the present invention;
fig. 2 is another schematic structural diagram of an intelligent X-ray image-based parcel sorting system for express delivery according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an intelligent X-ray image-based parcel sorting system for express delivery according to an embodiment of the present invention;
fig. 4 is a schematic flow chart of an intelligent express parcel sorting method based on X-ray images according to an embodiment of 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. 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.
It should be understood that the step numbers used herein are for convenience of description only and are not intended as limitations on the order in which the steps are performed.
It is to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
The terms "comprises" and "comprising" indicate the presence of the described features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
The term "and/or" refers to and includes any and all possible combinations of one or more of the associated listed items.
The first embodiment of the present invention:
referring to fig. 1-3, an intelligent express parcel sorting system based on X-ray images comprises a data processing terminal, a conveying device, a scanning and detecting device and a sorting device;
the express packages are conveyed by the conveying device, pass through the scanning and detecting device and then reach the sorting device;
the scanning and detecting device is used for transmitting the position information, the serial number information and the X-ray image of the express package to the data processing terminal;
the data processing terminal is used for detecting the express package according to a preset detection algorithm, writing the X-ray image into a contraband class label when the express package is judged to contain contraband, displaying a detection result and giving an alarm; and issuing a sorting instruction carrying the position information and the serial number information to the sorting device;
and the sorting device is used for sorting the express packages according to the sorting instruction.
The conveying device comprises a driving motor and a conveying belt driven by the driving motor; the sorting device comprises a sorting controller, a mechanical arm and a sorting line.
The scanning and detecting device comprises X-ray security check equipment, a first detecting device and a second detecting device, wherein the first detecting device and the second detecting device are respectively arranged at two ends of the X-ray security check equipment along the direction of the conveying belt; the X-ray safety inspection device comprises a first detector device, a second detector device, a first data acquisition device, a first scanning controller, a second data acquisition device and a second scanning controller, wherein the first detector device comprises a first position sensor, a first data acquisition device and a first scanning controller; the sorting device comprises a sorting controller, a mechanical arm and a sorting line.
In the specific embodiment, when the driving motor receives the started pulse signal, the driving motor starts to operate, so that the conveying belt lifts the packaged to-be-detected express packages and transmits the to-be-detected express packages to each device for contraband identification.
The X-ray security check equipment of the scanning and detecting device is used for scanning express parcels passing through the equipment to obtain X-ray images. The detection device adopts the position sensor to judge express package position information, starts the scanning controller, records express package sequence numbers, and sends express package scanning position signals and sequence signals to the data acquisition unit to confirm contraband express package position and sequence information. The system comprises a conveying belt, an X-ray safety inspection device, a position signal and a sequence signal, wherein the position signal refers to position information of express packages on the conveying belt when the express packages pass through the X-ray safety inspection device, the express packages are sequenced when passing through the X-ray safety inspection device, and the sequence signal refers to sequencing information of the express packages passing through the X-ray safety inspection device.
And after receiving the express package position signal and the sequence signal sent by the detection device, the data acquisition device sends the express package position signal and the sequence signal to the data processing terminal, and compares the express package position signal and the sequence signal with the express package identification label of the contraband. And when the transmission controller receives the pulse signal of the data processing terminal, the transmission controller controls the transmission device to start, and meanwhile, the scanning controller and the sorting controller respectively control the scanning and detecting device and the sorting device to operate.
In this embodiment, the data processing terminal is a computer terminal, and includes a display screen, a computer server, and loaded image feature recognition software. The display screen is used for displaying an X-ray image and an express package detection effect picture. And the computer server is used for storing the scanning result and carrying out detection operation. And the image feature recognition software extracts the features of the X-ray image to realize the target detection function.
The extracted features include: 1) improved local binary pattern features of the image; 2) directional gradient density characteristics of the pictures.
The extraction method comprises the following steps: after the extraction algorithm is programmed on a computer, an input interface of the algorithm is set as an acquired X-ray image source, and the algorithm processes the image to obtain the desired characteristics.
Treatment after extraction: after the features are extracted, the extracted features are converted into a vector form and input into a trained classifier, and the classifier is mapped to the category to which the features belong, so that whether the features belong to contraband or not can be judged. For example: the current X-ray image is an X-ray image of a battery, the image is processed by a feature extraction algorithm, improved local binary pattern features and direction gradient density features of the image are converted into a vector mode, the vector is input into a classifier, and the classifier judges whether a battery package represented by the vector is contraband or not through mapping of a nonlinear function.
When the manipulator receives a starting signal from the sorting controller, the express packages on the current conveyor belt are pushed into a contraband sorting line.
In the middle of specific embodiment, when the express delivery parcel does not pass through X-ray security check equipment, the position information and the sequence information of the express delivery parcel are obtained through a first detection device, then the express delivery parcel is conveyed to the X-ray security check equipment through a conveyor belt, security check scanning detection is carried out, and finally, contraband express delivery parcel sorting is carried out through a second detection device in combination with a sorting device. The first detection device and the second detection device are respectively installed at two ends of the electric conveyor belt and are fixed by the scanning frame.
In a preferred embodiment, the manipulator and the sorting line are arranged on two sides of the conveying belt, and the manipulator pushes the express packages on the current conveying belt into the sorting line after receiving signals of the sorting controller.
Embodiments of the invention are described in further detail below:
step one, in the initial stage of security inspection, a transmission controller sends a pulse signal to a driving motor, and an electric driving belt starts to operate.
And step two, putting the packaged express packages on an electric conveyor belt, when the express packages pass through a detection device, detecting the express package passing through by a sensor, starting a scanning controller, recording the position information and the sequence information of the express packages, and sending the position information and the sequence information to a data acquisition unit. And then the express package enters an X-ray machine.
And step three, the X-ray machine emits X-rays to scan the express package, the scanning result is sent to the computer server, and the X-ray scanning and checking picture of the express package is displayed on the display screen through the imaging system.
Inputting the X-ray image of the express package into a computer terminal, detecting the package through image feature recognition software, judging whether the express package contains contraband, writing the X-ray image into a contraband type label, and displaying a detection result on a display screen.
It should be noted that the contraband class labels are mainly classified into two categories of contraband and non-contraband, the characteristics of the two categories of sample data are extracted in advance to train the classifier, and the output of the classifier is the contraband and the non-contraband, that is, the transition information. Note: here, the contraband category label is expressed in a wrong manner, and the label is preferably written as a category label.
The X-ray image of the express parcel is converted into a vector form through a feature extraction algorithm, then the X-ray image passes through a classifier, the classifier judges which type the feature composition form is closer to, namely the image is divided into which type, namely, the label of which type is printed (for example, the current parcel is a battery, the battery forms the X-ray image through a security inspection machine, the feature extraction algorithm introduces the image and converts the image into the vector form to be used as the input of the classifier, the classifier judges that the express parcel belongs to the contraband type through function mapping, the label of contraband is printed on the express parcel, and the detection result is displayed.
In this section, the main steps of the detection algorithm are as follows:
1) establishing an X-ray image database of the express package, dividing two categories of contraband and non-contraband, and setting the contraband as a target detection category;
2) and dividing the X-ray image into a plurality of connected regions, wherein each connected region is a pixel block with the size of 3X 3.
3) Extracting the characteristics of each connected region, specifically,
A. calculating characteristic variables of the connected region in a local rotation invariant and uniform mode by adopting the following formula:
B. setting the transformation frequency threshold of the characteristic variable parameters obtained in the step A as N times, and additionally adding N-dimensional characteristics on the basis of the characteristic variable dimensions, wherein the formula is as follows:
C. selecting a gradient operator, and calculating a directional gradient density characteristic histogram of the connected region:
wherein, M (x, y) is the gradient amplitude of the pixel, theta (x, y) is the gradient direction of the pixel, and theta (x, y) is divided into a plurality of bins so as to obtain a directional gradient density characteristic histogram;
D. combining the characteristic variables obtained in the steps to construct a characteristic descriptor:
feature＝concat(feature1,feature2,feature3,...,featureL)
4) constructing a learning classifier, training the characteristic represented by the characteristic descriptor, and learning a characteristic classification decision plane y which is wx + b;
A. the dimension of the input vector is the dimension of the feature characterized by the feature descriptor;
B. carrying out scale normalization processing on the characteristic variables:
C. an enhanced kernel function improved based on Manhattan distance and Euclidean distance is designed and adopted, and the formula is as follows:
taking the X-ray image of the express package as training data, training by the aid of the designed learning classifier, and learning a characteristic classification decision surface;
5) accessing real-time image data from an imaging system, and cutting the image based on the express package outline;
6) and constructing a feature descriptor of the express package graph after cutting processing, putting the feature descriptor into a trained learning model, and obtaining contraband label information of the express package.
And step five, the data acquisition unit sends the position information and the sequence information of the express packages to a computer server, and the position information and the sequence information of the express packages containing the contraband are judged by comparing the position information and the sequence information with the contraband label of the X-ray scanning picture.
And step six, the express packages enter a sorting unit after passing through the X-ray security check equipment, when the detection device detects that the express packages pass through, a scanning controller is started, the position information and the sequence information of the express packages are recorded, and the position information and the sequence information are sent to a computer server through a data acquisition unit.
And seventhly, the computer server compares the position information of the express packages of the contraband with the sequence information and judges whether the express packages entering the sorting unit contain the contraband. If the current express package contains contraband, a pulse signal is sent to the sorting controller, and the mechanical arm device is started.
And step eight, pushing the express packages containing the contraband into a contraband sorting line after the manipulator device receives the starting signal.
Based on the teaching of the embodiment, in the process of security inspection of express packages in large batches, a large amount of manpower and material resources are consumed in the prior art, and the cost is high.
Second embodiment of the invention:
referring to fig. 4, an intelligent express parcel sorting method based on X-ray images is applicable to a data processing terminal, and includes:
s10, receiving the position information, the serial number information and the X-ray image of the express package transmitted by the scanning and detecting device;
s11, detecting the express package according to a preset detection algorithm, writing the X-ray image into a contraband class label when the express package is judged to contain contraband, displaying a detection result and giving an alarm;
and S12, issuing a sorting instruction carrying the position information and the serial number information to the sorting device, so that the express package is sorted out according to the sorting instruction.
The conveying device comprises a driving motor and a conveying belt driven by the driving motor; the sorting device comprises a sorting controller, a mechanical arm and a sorting line.
The scanning and detecting device comprises X-ray security check equipment, a first detecting device and a second detecting device, wherein the first detecting device and the second detecting device are respectively arranged at two ends of the X-ray security check equipment along the direction of the conveying belt; the X-ray safety inspection device comprises a first detector device, a second detector device, a first data acquisition device, a first scanning controller, a second data acquisition device and a second scanning controller, wherein the first detector device comprises a first position sensor, a first data acquisition device and a first scanning controller; the sorting device comprises a sorting controller, a mechanical arm and a sorting line.
In the specific embodiment, when the driving motor receives the started pulse signal, the driving motor starts to operate, so that the conveying belt lifts the packaged to-be-detected express packages and transmits the to-be-detected express packages to each device for contraband identification.
The X-ray security check equipment of the scanning and detecting device is used for scanning express parcels passing through the equipment to obtain X-ray images. The detection device adopts the position sensor to judge express package position information, starts the scanning controller, records express package sequence numbers, and sends express package scanning position signals and sequence signals to the data acquisition unit to confirm contraband express package position and sequence information. The system comprises a conveying belt, an X-ray safety inspection device, a position signal and a sequence signal, wherein the position signal refers to position information of express packages on the conveying belt when the express packages pass through the X-ray safety inspection device, the express packages are sequenced when passing through the X-ray safety inspection device, and the sequence signal refers to sequencing information of the express packages passing through the X-ray safety inspection device.
And after receiving the express package position signal and the sequence signal sent by the detection device, the data acquisition device sends the express package position signal and the sequence signal to the data processing terminal, and compares the express package position signal and the sequence signal with the express package identification label of the contraband. And when the transmission controller receives the pulse signal of the data processing terminal, the transmission controller controls the transmission device to start, and meanwhile, the scanning controller and the sorting controller respectively control the scanning and detecting device and the sorting device to operate.
In this embodiment, the data processing terminal is a computer terminal, and includes a display screen, a computer server, and loaded image feature recognition software. The display screen is used for displaying an X-ray image and an express package detection effect picture. And the computer server is used for storing the scanning result and carrying out detection operation. And the image feature recognition software extracts the features of the X-ray image to realize the target detection function.
The extracted features include: 1) improved local binary pattern features of the image; 2) directional gradient density characteristics of the pictures.
The extraction method comprises the following steps: after the extraction algorithm is programmed on a computer, an input interface of the algorithm is set as an acquired X-ray image source, and the algorithm processes the image to obtain the desired characteristics.
Treatment after extraction: after the features are extracted, the extracted features are converted into a vector form and input into a trained classifier, and the classifier is mapped to the category to which the features belong, so that whether the features belong to contraband or not can be judged. For example: the current X-ray image is an X-ray image of a battery, the image is processed by a feature extraction algorithm, improved local binary pattern features and direction gradient density features of the image are converted into a vector mode, the vector is input into a classifier, and the classifier judges whether a battery package represented by the vector is contraband or not through mapping of a nonlinear function.
When the manipulator receives a starting signal from the sorting controller, the express packages on the current conveyor belt are pushed into a contraband sorting line.
In the middle of specific embodiment, when the express delivery parcel does not pass through X-ray security check equipment, the position information and the sequence information of the express delivery parcel are obtained through a first detection device, then the express delivery parcel is conveyed to the X-ray security check equipment through a conveyor belt, security check scanning detection is carried out, and finally, contraband express delivery parcel sorting is carried out through a second detection device in combination with a sorting device. The first detection device and the second detection device are respectively installed at two ends of the electric conveyor belt and are fixed by the scanning frame.
In a preferred embodiment, the manipulator and the sorting line are arranged on two sides of the conveying belt, and the manipulator pushes the express packages on the current conveying belt into the sorting line after receiving signals of the sorting controller.
Embodiments of the invention are described in further detail below:
step one, in the initial stage of security inspection, a transmission controller sends a pulse signal to a driving motor, and an electric driving belt starts to operate.
And step two, putting the packaged express packages on an electric conveyor belt, when the express packages pass through a detection device, detecting the express package passing through by a sensor, starting a scanning controller, recording the position information and the sequence information of the express packages, and sending the position information and the sequence information to a data acquisition unit. And then the express package enters an X-ray machine.
And step three, the X-ray machine emits X-rays to scan the express package, the scanning result is sent to the computer server, and the X-ray scanning and checking picture of the express package is displayed on the display screen through the imaging system.
Inputting the X-ray image of the express package into a computer terminal, detecting the package through image feature recognition software, judging whether the express package contains contraband, writing the X-ray image into a contraband type label, and displaying a detection result on a display screen.
It should be noted that the contraband class labels are mainly classified into two categories of contraband and non-contraband, the characteristics of the two categories of sample data are extracted in advance to train the classifier, and the output of the classifier is the contraband and the non-contraband, that is, the transition information. Note: here, the contraband category label is expressed in a wrong manner, and the label is preferably written as a category label.
The X-ray image of the express parcel is converted into a vector form through a feature extraction algorithm, then the X-ray image passes through a classifier, the classifier judges which type the feature composition form is closer to, namely the image is divided into which type, namely, the label of which type is printed (for example, the current parcel is a battery, the battery forms the X-ray image through a security inspection machine, the feature extraction algorithm introduces the image and converts the image into the vector form to be used as the input of the classifier, the classifier judges that the express parcel belongs to the contraband type through function mapping, the label of contraband is printed on the express parcel, and the detection result is displayed.
In this section, the main steps of the detection algorithm are as follows:
1) establishing an X-ray image database of the express package, dividing two categories of contraband and non-contraband, and setting the contraband as a target detection category;
2) and dividing the X-ray image into a plurality of connected regions, wherein each connected region is a pixel block with the size of 3X 3.
3) Extracting the characteristics of each connected region, specifically,
A. calculating characteristic variables of the connected region in a local rotation invariant and uniform mode by adopting the following formula:
B. setting the transformation frequency threshold of the characteristic variable parameters obtained in the step A as N times, and additionally adding N-dimensional characteristics on the basis of the characteristic variable dimensions, wherein the formula is as follows:
C. selecting a gradient operator, and calculating a directional gradient density characteristic histogram of the connected region:
wherein, M (x, y) is the gradient amplitude of the pixel, theta (x, y) is the gradient direction of the pixel, and theta (x, y) is divided into a plurality of bins so as to obtain a directional gradient density characteristic histogram;
D. combining the characteristic variables obtained in the steps to construct a characteristic descriptor:
feature＝concat(feature1,feature2,feature3,...,featureL)
4) constructing a learning classifier, training the characteristic represented by the characteristic descriptor, and learning a characteristic classification decision plane y which is wx + b;
A. the dimension of the input vector is the dimension of the feature characterized by the feature descriptor;
B. carrying out scale normalization processing on the characteristic variables:
C. an enhanced kernel function improved based on Manhattan distance and Euclidean distance is designed and adopted, and the formula is as follows:
taking the X-ray image of the express package as training data, training by the aid of the designed learning classifier, and learning a characteristic classification decision surface;
5) accessing real-time image data from an imaging system, and cutting the image based on the express package outline;
6) and constructing a feature descriptor of the express package graph after cutting processing, putting the feature descriptor into a trained learning model, and obtaining contraband label information of the express package.
And step five, the data acquisition unit sends the position information and the sequence information of the express packages to a computer server, and the position information and the sequence information of the express packages containing the contraband are judged by comparing the position information and the sequence information with the contraband label of the X-ray scanning picture.
And step six, the express packages enter a sorting unit after passing through the X-ray security check equipment, when the detection device detects that the express packages pass through, a scanning controller is started, the position information and the sequence information of the express packages are recorded, and the position information and the sequence information are sent to a computer server through a data acquisition unit.
And seventhly, the computer server compares the position information of the express packages of the contraband with the sequence information and judges whether the express packages entering the sorting unit contain the contraband. If the current express package contains contraband, a pulse signal is sent to the sorting controller, and the mechanical arm device is started.
And step eight, pushing the express packages containing the contraband into a contraband sorting line after the manipulator device receives the starting signal.
Based on the teaching of the embodiment, the express package inspection system can improve the inspection efficiency and accuracy of express packages and accurately identify and sort contraband.
In a third aspect, an embodiment of the present invention further provides a computer-readable storage medium, where the storage medium includes a stored computer program, where the apparatus on which the storage medium is located is controlled to execute the intelligent X-ray image-based sorting method for express packages when the computer program runs.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and can include the processes of the embodiments of the methods described above when the computer program is executed. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.
Claims (8)
1. An express package intelligent sorting system based on X-ray images is characterized by comprising a data processing terminal, a conveying device, a scanning and detecting device and a sorting device;
the express packages are conveyed by the conveying device, pass through the scanning and detecting device and then reach the sorting device;
the scanning and detecting device is used for transmitting the position information, the serial number information and the X-ray image of the express package to the data processing terminal;
the data processing terminal is used for detecting the express package according to a preset detection algorithm, writing the X-ray image into a contraband class label when the express package is judged to contain contraband, displaying a detection result and giving an alarm; and issuing a sorting instruction carrying the position information and the serial number information to the sorting device;
and the sorting device is used for sorting the express packages according to the sorting instruction.
2. The intelligent X-ray image-based parcel sorting system for express delivery according to claim 1, wherein the preset detection algorithm comprises:
1) establishing an X-ray image database of the express package, dividing two categories of contraband and non-contraband, and setting the contraband as a target detection category;
2) and dividing the X-ray image into a plurality of connected regions, wherein each connected region is a pixel block with the size of 3X 3.
3) Extracting the characteristics of each connected region, specifically,
A. calculating characteristic variables of the connected region in a local rotation invariant and uniform mode by adopting the following formula:
B. setting the transformation frequency threshold of the characteristic variable parameters obtained in the step A as N times, and additionally adding N-dimensional characteristics on the basis of the characteristic variable dimensions, wherein the formula is as follows:
wherein P is the number of pixels in the neighborhood, R is the radius of the neighborhood, gpIs the gray value of the neighborhood pixel, gc(x) for center point gray pixel values, s (x) is a sign function, u (x) calculates 0/1 transformation times in local binary mode;
C. selecting a gradient operator, and calculating a directional gradient density characteristic histogram of the connected region:
wherein, M (x, y) is the gradient amplitude of the pixel, theta (x, y) is the gradient direction of the pixel, and theta (x, y) is divided into a plurality of bins so as to obtain a directional gradient density characteristic histogram;
D. combining the characteristic variables obtained in the steps to construct a characteristic descriptor:
feature＝concat(feature1,feature2,feature3,...,featureL)；
4) constructing a learning classifier, training the characteristic represented by the characteristic descriptor, and learning a characteristic classification decision plane y which is wx + b;
A. the dimension of the input vector is the dimension of the feature characterized by the feature descriptor;
B. carrying out scale normalization processing on the characteristic variables:
C. an enhanced kernel function improved based on Manhattan distance and Euclidean distance is designed and adopted, and the formula is as follows:
taking the X-ray image of the express package as training data, training by the aid of the designed learning classifier, and learning a characteristic classification decision surface;
5) accessing real-time image data from an imaging system, and cutting the image based on the express package outline;
6) and constructing a feature descriptor of the express package graph after cutting processing, putting the feature descriptor into a trained learning model, and obtaining contraband label information of the express package.
3. The X-ray image-based intelligent parcel sorting system according to claim 1, wherein said conveyor comprises a drive motor and a conveyor belt driven by said drive motor; the sorting device comprises a sorting controller, a mechanical arm and a sorting line.
4. The X-ray image-based intelligent sorting system for express parcels according to claim 1, wherein the scanning and detecting device comprises an X-ray security check device, and a first detecting device and a second detecting device respectively installed at two ends of the X-ray security check device along the direction of the conveyor belt; the X-ray safety inspection device comprises a first detector device, a second detector device, a first data acquisition device, a first scanning controller, a second data acquisition device and a second scanning controller, wherein the first detector device comprises a first position sensor, a first data acquisition device and a first scanning controller; the sorting device comprises a sorting controller, a mechanical arm and a sorting line.
5. An intelligent express parcel sorting method based on X-ray images is suitable for a data processing terminal and is characterized by comprising the following steps:
receiving the position information, the serial number information and the X-ray image of the express package transmitted by the scanning and detecting device;
detecting the express package according to a preset detection algorithm, writing the X-ray image into a contraband class label when the express package is judged to contain contraband, displaying a detection result and giving an alarm;
and issuing a sorting instruction carrying the position information and the serial number information to the sorting device so as to sort the express packages according to the sorting instruction.
6. The intelligent express parcel sorting method based on X-ray images as claimed in claim 5, wherein the preset detection algorithm comprises:
1) establishing an X-ray image database of the express package, dividing two categories of contraband and non-contraband, and setting the contraband as a target detection category;
2) and dividing the X-ray image into a plurality of connected regions, wherein each connected region is a pixel block with the size of 3X 3.
3) Extracting the characteristics of each connected region, specifically,
A. calculating characteristic variables of the connected region in a local rotation invariant and uniform mode by adopting the following formula:
B. setting the transformation frequency threshold of the characteristic variable parameters obtained in the step A as N times, and additionally adding N-dimensional characteristics on the basis of the characteristic variable dimensions, wherein the formula is as follows:
wherein P is the number of pixels in the neighborhood, R is the radius of the neighborhood, gpIs the gray value of the neighborhood pixel, gc(x) for center point gray pixel values, s (x) is a sign function, u (x) calculates 0/1 transformation times in local binary mode;
C. selecting a gradient operator, and calculating a directional gradient density characteristic histogram of the connected region:
wherein, M (x, y) is the gradient amplitude of the pixel, theta (x, y) is the gradient direction of the pixel, and theta (x, y) is divided into a plurality of bins so as to obtain a directional gradient density characteristic histogram;
D. combining the characteristic variables obtained in the steps to construct a characteristic descriptor:
feature＝concat(feature1,feature2,feature3,...,featureL)；
4) constructing a learning classifier, training the characteristic represented by the characteristic descriptor, and learning a characteristic classification decision plane y which is wx + b;
A. the dimension of the input vector is the dimension of the feature characterized by the feature descriptor;
B. carrying out scale normalization processing on the characteristic variables:
C. an enhanced kernel function improved based on Manhattan distance and Euclidean distance is designed and adopted, and the formula is as follows:
taking the X-ray image of the express package as training data, training by the aid of the designed learning classifier, and learning a characteristic classification decision surface;
5) accessing real-time image data from an imaging system, and cutting the image based on the express package outline;
6) and constructing a feature descriptor of the express package graph after cutting processing, putting the feature descriptor into a trained learning model, and obtaining contraband label information of the express package.
7. The intelligent X-ray image-based express parcel sorting method according to claim 5, wherein the scanning and detecting device comprises an X-ray security inspection device, and a first detecting device and a second detecting device which are respectively installed at two ends of the X-ray security inspection device along the direction of the conveyor belt; the X-ray safety inspection device comprises a first detector device, a second detector device, a first data acquisition device, a first scanning controller, a second data acquisition device and a second scanning controller, wherein the first detector device comprises a first position sensor, a first data acquisition device and a first scanning controller; the sorting device comprises a sorting controller, a mechanical arm and a sorting line.
8. A computer-readable storage medium, wherein the storage medium includes a stored computer program, and wherein when the computer program is run, the apparatus on which the storage medium is located is controlled to execute the intelligent X-ray image-based sorting method for parcels in express delivery according to any one of claims 5 to 7.
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