CN111474186A - X-ray imaging and CNN express package contraband detection method - Google Patents

X-ray imaging and CNN express package contraband detection method Download PDF

Info

Publication number
CN111474186A
CN111474186A CN202010244249.2A CN202010244249A CN111474186A CN 111474186 A CN111474186 A CN 111474186A CN 202010244249 A CN202010244249 A CN 202010244249A CN 111474186 A CN111474186 A CN 111474186A
Authority
CN
China
Prior art keywords
package
contraband
cnn
ray imaging
layer
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202010244249.2A
Other languages
Chinese (zh)
Inventor
胡天羽
徐双
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Anhui University of Science and Technology
Original Assignee
Anhui University of Science and Technology
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Anhui University of Science and Technology filed Critical Anhui University of Science and Technology
Priority to CN202010244249.2A priority Critical patent/CN111474186A/en
Publication of CN111474186A publication Critical patent/CN111474186A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N23/00—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00
    • G01N23/02—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00 by transmitting the radiation through the material
    • G01N23/04—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00 by transmitting the radiation through the material and forming images of the material
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V5/00—Prospecting or detecting by the use of ionising radiation, e.g. of natural or induced radioactivity
    • G01V5/20—Detecting prohibited goods, e.g. weapons, explosives, hazardous substances, contraband or smuggled objects
    • G01V5/22—Active interrogation, i.e. by irradiating objects or goods using external radiation sources, e.g. using gamma rays or cosmic rays
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10116—X-ray image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20081—Training; Learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20084—Artificial neural networks [ANN]

Landscapes

  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Immunology (AREA)
  • Quality & Reliability (AREA)
  • General Health & Medical Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Chemical & Material Sciences (AREA)
  • Pathology (AREA)
  • Health & Medical Sciences (AREA)
  • Biochemistry (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Theoretical Computer Science (AREA)
  • High Energy & Nuclear Physics (AREA)
  • General Life Sciences & Earth Sciences (AREA)
  • Geophysics (AREA)
  • Image Analysis (AREA)

Abstract

The invention discloses an X-ray imaging and CNN express package contraband detection method, which relates to the field of optical engineering and artificial intelligence and comprises the following steps: (1) acquiring package X-ray image information; (2) dividing a parcel X-ray image sample; (3) extracting the characteristics of the package X-ray image; (4) and constructing a random forest contraband parcel identification model. The invention adopts CNN-RF to construct an express package contraband detection model, provides a new convolutional neural network model which can extract more and more effective characteristic information, can avoid overfitting, greatly improves the training speed by using two GPUs for training, and is very suitable for accurate and rapid detection of express package contraband.

Description

X-ray imaging and CNN express package contraband detection method
Technical Field
The invention relates to the field of optical engineering and artificial intelligence, in particular to a method for detecting whether an express package contains contraband or not.
Background
Along with economic development in recent years, the express delivery market is increasingly prosperous, the express delivery industry is labor-intensive, the working time is long, and the flow is complex. When the express business network takes the articles, the condition of receiving contraband articles may occur due to the lack of professional knowledge of personnel, and unnecessary influence is caused on the development of the economy and the society and the public safety.
Artificial Intelligence (AI) is a new technical science that studies and develops theories, methods, techniques and applications for simulating, extending and expanding human intelligence. The method mainly comprises a robot, image processing, natural language processing, an expert system and the like. Convolutional Neural Networks (CNNs) are feed-forward Neural Networks (Feedforward Neural Networks) including Convolutional calculations and having deep structures, are one of the typical algorithms for deep learning, and are widely applied to computer vision, natural language processing, and the like.
Optical engineering (english) refers to a type of engineering that applies optical theory to practical applications. Optical engineering optical instruments, such as lenses, microscopes and telescopes, also include other devices that utilize optical properties. In addition, optical engineering has also investigated optical sensors and associated measurement systems, lasers, fiber optic communications, and optical discs (e.g., CDs, DVDs), among others. X-rays, also known as roentgen rays, are a type of radiation that is invisible to the naked eye, but can cause some compounds to fluoresce or to sensitize photographic negatives. Meanwhile, the X-ray can penetrate substances, and the X-ray detector is widely applied to the fields of security inspection, industrial flaw detection, medical treatment and the like.
Disclosure of Invention
Aiming at the problems, the invention provides a simple and quick contraband identification method without unpacking.
In order to achieve the purpose, the invention adopts the technical scheme that: a method for detecting whether an express package of X-ray imaging and CNN contains contraband or not comprises the following steps:
(1) and acquiring package X-ray image information.
(2) And (4) dividing the parcel X-ray image sample.
(3) And (5) extracting the characteristics of the package X-ray image.
(4) And constructing a random forest contraband parcel identification model.
Preferably, in the step (1), the image information of the parcel is acquired by using an X-ray imaging technology, so as to obtain an image data set of the parcel.
Preferably, in the step (2), the acquired parcel image data is divided into independent and non-repeating training sets and test sets in a certain proportion by adopting a random sampling mode.
Preferably, in step (3), the Convolutional Neural Networks (CNN) for extracting information features of the X-ray image is a network including 8 training parameters, and the network structure includes a Convolutional layer, a local response normalization layer, a pooling layer, a full connection layer, and the like.
Preferably, in the step (4), a Random Forest (RF) contraband identification model is constructed on a training set by using the X-ray image features extracted by the CNN, parameters of the identification model are determined, and then the identification effect is detected by using the test set to verify the performance of the model.
Through the technical scheme, the invention has the beneficial effects that: the new convolutional neural network model can acquire more and more effective image information, and is favorable for quickly and accurately detecting the contraband in the package.
Drawings
Fig. 1 is a flowchart of a method for identifying contraband of express packages according to an embodiment of the present invention.
FIG. 2 is a schematic diagram of a convolutional neural network structure for extracting image features according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings in combination with specific embodiments.
The invention works in Windows 10 environment, adopts Keras to analyze and takes TensorFlow as the back end.
The invention discloses an X-ray imaging and CNN express contraband detection method, which comprises the following steps:
(1) and acquiring package X-ray image information.
(2) And (4) dividing the parcel X-ray image sample.
(3) And (5) extracting the characteristics of the package X-ray image.
(4) And constructing a random forest contraband parcel identification model.
In order to make the objects, technical solutions and advantages of the present invention clearer, the following will further describe in detail an X-ray imaging and CNN express contraband detection method according to the present invention with reference to the accompanying drawings, in combination with specific examples, where the identification steps are shown in fig. 1:
101, acquiring the X-ray image of the package, and acquiring the X-ray image and the information of the package through an X-ray imaging device.
102, dividing X-ray spectrum image samples of packages, and dividing the spectrum image data containing contraband and not containing the contraband in the packages into an independent test set and an independent training set according to the proportion of 70 percent of the training set and 30 percent of the test set by adopting a random sampling method.
103, the convolutional neural network wraps the X-ray image feature extraction, the convolutional neural network for extracting the X-ray image feature is a network comprising 8 training parameters, the network structure comprises a convolutional layer, a local response normalization layer, a pooling layer, a full-link layer and the like, and the specific description of judging the type is as follows:
the network contains 8 weighted layers; the first 5 layers are convolutional layers and the remaining 3 layers are fully-connected layers. And connecting the output characteristics of the last full connection layer, wherein the dimensionality is 1000 (or self-defined), and then taking the output characteristics as the input of the random forest.
And (4) rolling up a layer C1, wherein the processing flow of the layer is convolution- > Re L U- > pooling- > normalization.
The convolutional layer, input was 227 × 227 × 3, using 96 convolution kernels of 11 × 11 × 3.
Re L U, FeatureMap output by the convolutional layer is input into the Re L U function.
Pooling layer, using 3 × 3 with pooling cells of step size 2.
The local response normalization layer was locally normalized using k 2, n 5, α -10, β 0.75 with an output of 27 × 27 × 96, and the output was divided into two groups, each group having a size of 27 × 27 × 48.
The processing flow of the convolutional layer C2 is convolution, Re L U, pooling and normalization
The input is 2 sets of 27 × 27 × 48. 2 sets of 128 convolution kernels of size 5 × 5 × 48 are used and edge filling padding is done at 2, the step size of the convolution is 1.
Re L U, FeatureMap output by the convolutional layer is input into the Re L U function.
Pooling layer, size of operation 3 × 3, step size 2.
The local response normalization layer is locally normalized by using k 2, n 5, α 10-4, β 0.75, and the output is 13 × 13 × 256, and the output is divided into 2 groups, and the size of each group is 13 × 13 × 128.
And (4) rolling up a layer C3, wherein the processing flow of the layer is convolution- - > Re L U.
The convolutional layer, input is 13 × 13 × 256, 2 sets of convolutional kernels of size 3 × 3 × 256 are used, edge padding is 1, and the step size of convolution is 1.
Re L U, FeatureMap output by the convolutional layer is input into the Re L U function.
And (3) rolling up a layer C4, wherein the processing flow of the layer is convolution- - > Re L U, the layer is similar to C3.
The convolution layer, input is 13 × 13 × 384, is divided into two groups, each group is 13 × 13 × 192, 2 groups are used, each group is 192 convolution kernels with the size of 3 × 3 × 192, edge filling padding is made to be 1, the step size of convolution is 1, output FeatureMap is 13 × 13times384, and the convolution layer is divided into two groups, each group is 13 × 13 × 192.
Re L U, FeatureMap output by the convolutional layer is input into the Re L U function.
And (4) rolling up a layer C5, wherein the processing flow of the layer is convolution- - > Re L U- - > pooling.
The convolutional layer, input 13 × 13 × 384, was divided into two groups, each group was 13 × 13 × 192, 2 groups were used, each group was a 128 size convolution kernel of 3 × 3 × 192, edge padding was done at 1, and the step size of the convolution was 1.
Re L U, FeatureMap output by the convolutional layer is input into the Re L U function.
The pooling layer, the size of pooling operation is 3 × 3, and the output of pooling with step size of 2 is 6 × 6 × 256.
And the flow of the full connection layer FC6 is convolution full connection- - > Re L U- - > Dropout
(convolution) full concatenation: input is 6 × 6 × 256, this layer has 4096 convolution kernels.
Re L U, the 4096 operation results in 4096 values generated by Re L U activation functions.
Dropout, inhibition over-fitting, random disconnection of certain neurons or non-activation of certain neurons.
The full-connection layer FC7 has the process of full connection of Re L U Dropout
Fully connected, the input is a 4096 vector.
Re L U, the 4096 operation results in 4096 values generated by Re L U activation functions.
Dropout, inhibition over-fitting, random disconnection of certain neurons or non-activation of certain neurons.
And in the output layer, 4096 data output by the seventh layer are fully connected with 1000 neurons of the eighth layer, and 1000 float-type values are output after training, which is the extracted characteristic.
The AlexNet runs on two GPUs, so that pixel data are divided into two groups and stored in the two GPUs respectively, the convolutional layers C2, C4 and C5 are all connected by the previous layer of pixel data in the GPU where the group of pixel data is located, the C3 layer of the convolutional layer is fully connected with the previous two layers, and the full connection is 2 GPUs.
104, constructing a random forest contraband parcel identification model, constructing the random forest contraband identification model on a training set by using the characteristics of X-ray images extracted by CNN, determining the parameters of the identification model, detecting the identification effect by using a test set, and verifying the performance of the model.
Through the technical scheme, the invention has the beneficial effects that: the new convolutional neural network model can acquire more and more effective image information, and is favorable for quickly and accurately detecting the contraband in the package.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present description refers to embodiments, not every embodiment may include only a single embodiment, and such description is for clarity only, and those skilled in the art will be able to make the description as a whole, and the embodiments may be combined as appropriate to form other embodiments understood by those skilled in the art.

Claims (5)

1. A method for detecting whether an express package of X-ray imaging and CNN contains contraband or not comprises the following steps:
(1) and acquiring package X-ray image information.
(2) And (4) dividing the parcel X-ray image sample.
(3) And (5) extracting the characteristics of the package X-ray image.
(4) And constructing a random forest contraband parcel identification model.
2. The method of claim 1 for detecting whether an express package of X-ray imaging and CNN contains contraband, comprising: in the step (1), the image information of the package is acquired by using an X-ray imaging technology, so as to obtain an image data set of the package.
3. The method of claim 1 for detecting whether an express package of X-ray imaging and CNN contains contraband, comprising: in the step (2), the acquired parcel image data is divided into independent and non-repetitive training sets and test sets according to a certain proportion by adopting a random sampling mode.
4. The method of claim 1 for detecting whether an express package of X-ray imaging and CNN contains contraband, comprising: in the step (3), a Convolutional Neural Network (CNN) for extracting information features of the X-ray image is a network including 8 training parameters, and a network structure includes a convolutional layer, a local response normalization layer, a pooling layer, a full connection layer, and the like.
5. The method of claim 1 for detecting whether an express package of X-ray imaging and CNN contains contraband, comprising: in the step (4), a random forest (Randomforest) contraband identification model is constructed on a training set by using the characteristic of the X-ray image extracted by the CNN, the parameter of the identification model is determined, then the identification effect is detected by using a test set, and the performance of the model is verified.
CN202010244249.2A 2020-03-31 2020-03-31 X-ray imaging and CNN express package contraband detection method Pending CN111474186A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010244249.2A CN111474186A (en) 2020-03-31 2020-03-31 X-ray imaging and CNN express package contraband detection method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010244249.2A CN111474186A (en) 2020-03-31 2020-03-31 X-ray imaging and CNN express package contraband detection method

Publications (1)

Publication Number Publication Date
CN111474186A true CN111474186A (en) 2020-07-31

Family

ID=71750376

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010244249.2A Pending CN111474186A (en) 2020-03-31 2020-03-31 X-ray imaging and CNN express package contraband detection method

Country Status (1)

Country Link
CN (1) CN111474186A (en)

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6118850A (en) * 1997-02-28 2000-09-12 Rutgers, The State University Analysis methods for energy dispersive X-ray diffraction patterns
WO2009000157A1 (en) * 2007-06-21 2008-12-31 Tsinghua University Method and system for contraband detection using a photoneutron x-ray
CN106991374A (en) * 2017-03-07 2017-07-28 中国矿业大学 Handwritten Digit Recognition method based on convolutional neural networks and random forest
CN109784203A (en) * 2018-12-24 2019-05-21 中国科学院大学 Weakly supervised x-ray image contraband inspection method based on delamination propagation and activation
CN109948692A (en) * 2019-03-16 2019-06-28 四川大学 Picture detection method is generated based on the computer of multiple color spaces convolutional neural networks and random forest
CN109948565A (en) * 2019-03-26 2019-06-28 浙江啄云智能科技有限公司 A kind of not unpacking detection method of the contraband for postal industry
CN110533051A (en) * 2019-08-02 2019-12-03 中国民航大学 Contraband automatic testing method in X-ray safety check image based on convolutional neural networks
CN110544054A (en) * 2019-09-30 2019-12-06 北京物资学院 Anti-violence sorting active express sorting operation assisting and evaluating system and method

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6118850A (en) * 1997-02-28 2000-09-12 Rutgers, The State University Analysis methods for energy dispersive X-ray diffraction patterns
WO2009000157A1 (en) * 2007-06-21 2008-12-31 Tsinghua University Method and system for contraband detection using a photoneutron x-ray
CN106991374A (en) * 2017-03-07 2017-07-28 中国矿业大学 Handwritten Digit Recognition method based on convolutional neural networks and random forest
CN109784203A (en) * 2018-12-24 2019-05-21 中国科学院大学 Weakly supervised x-ray image contraband inspection method based on delamination propagation and activation
CN109948692A (en) * 2019-03-16 2019-06-28 四川大学 Picture detection method is generated based on the computer of multiple color spaces convolutional neural networks and random forest
CN109948565A (en) * 2019-03-26 2019-06-28 浙江啄云智能科技有限公司 A kind of not unpacking detection method of the contraband for postal industry
CN110533051A (en) * 2019-08-02 2019-12-03 中国民航大学 Contraband automatic testing method in X-ray safety check image based on convolutional neural networks
CN110544054A (en) * 2019-09-30 2019-12-06 北京物资学院 Anti-violence sorting active express sorting operation assisting and evaluating system and method

Similar Documents

Publication Publication Date Title
US10013615B2 (en) Inspection methods and devices
Weaver et al. LeafMachine: Using machine learning to automate leaf trait extraction from digitized herbarium specimens
CN111985310A (en) Training method of deep convolutional neural network for face recognition
CN111507370A (en) Method and device for obtaining sample image of inspection label in automatic labeling image
CN112581450B (en) Pollen detection method based on expansion convolution pyramid and multi-scale pyramid
Lin PCA/SVM‐based method for pattern detection in a multisensor system
CN109522838A (en) A kind of safety cap image recognition algorithm based on width study
Shoohi et al. DCGAN for Handling Imbalanced Malaria Dataset based on Over-Sampling Technique and using CNN.
CN114692156A (en) Memory segment malicious code intrusion detection method, system, storage medium and equipment
CN113344862A (en) Defect detection method, defect detection device, electronic equipment and storage medium
Kumar et al. MOSQUITO‐NET: a deep learning based CADx system for malaria diagnosis along with model interpretation using GradCam and class activation maps
KR102635610B1 (en) Seismic data generation method and device using deep learning
Powell et al. Generating transient noise artefacts in gravitational-wave detector data with generative adversarial networks
He et al. Wildfire detection for transmission line based on improved lightweight YOLO
Chen et al. Causality and independence enhancement for biased node classification
CN111474186A (en) X-ray imaging and CNN express package contraband detection method
CN109977400B (en) Verification processing method and device, computer storage medium and terminal
CN117173759A (en) Face detection method based on multi-scale feature fusion
CN116385957A (en) X-ray image contraband detection method, device, equipment and medium
CN113763315B (en) Slide image information acquisition method, device, equipment and medium
Waghmare et al. A review on malware detection methods
CN112580724A (en) Lubricant performance detection method based on uniform rotation identification of torque wheel
Ayyar et al. Identifying Transient Candidates in the Dark Energy Survey Using Convolutional Neural Networks
CN116032567B (en) Method, device, equipment and storage medium for describing risk of unknown network threat
Chourasiya et al. Optimization Accuracy on an Intelligent Approach to Detect Fake News on Twitter Using LSTM Neural Network

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
RJ01 Rejection of invention patent application after publication
RJ01 Rejection of invention patent application after publication

Application publication date: 20200731