CN107871122A - Safety check detection method, device, system and electronic equipment - Google Patents
Safety check detection method, device, system and electronic equipment Download PDFInfo
- Publication number
- CN107871122A CN107871122A CN201711126618.2A CN201711126618A CN107871122A CN 107871122 A CN107871122 A CN 107871122A CN 201711126618 A CN201711126618 A CN 201711126618A CN 107871122 A CN107871122 A CN 107871122A
- Authority
- CN
- China
- Prior art keywords
- safety check
- ray image
- deep learning
- learning model
- ray
- 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
Links
- 238000001514 detection method Methods 0.000 title claims abstract description 37
- 238000013136 deep learning model Methods 0.000 claims abstract description 52
- 238000000034 method Methods 0.000 claims abstract description 34
- 238000012216 screening Methods 0.000 claims abstract description 25
- 238000013527 convolutional neural network Methods 0.000 claims abstract description 22
- 238000012549 training Methods 0.000 claims abstract description 22
- 238000000605 extraction Methods 0.000 claims abstract description 17
- 238000009499 grossing Methods 0.000 claims description 9
- 238000004590 computer program Methods 0.000 claims description 7
- 230000008569 process Effects 0.000 claims description 7
- 238000010801 machine learning Methods 0.000 claims description 4
- 238000012935 Averaging Methods 0.000 claims description 3
- 235000013399 edible fruits Nutrition 0.000 claims description 2
- 238000004891 communication Methods 0.000 description 8
- 238000003860 storage Methods 0.000 description 8
- 230000007613 environmental effect Effects 0.000 description 7
- 238000012545 processing Methods 0.000 description 7
- 238000013135 deep learning Methods 0.000 description 6
- 239000000126 substance Substances 0.000 description 6
- 238000010586 diagram Methods 0.000 description 5
- 230000006870 function Effects 0.000 description 5
- 230000008901 benefit Effects 0.000 description 4
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 238000007689 inspection Methods 0.000 description 3
- 230000011218 segmentation Effects 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 230000008878 coupling Effects 0.000 description 2
- 230000002708 enhancing effect Effects 0.000 description 2
- 238000007781 pre-processing Methods 0.000 description 2
- 230000009471 action Effects 0.000 description 1
- 208000003464 asthenopia Diseases 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000001537 neural effect Effects 0.000 description 1
- 230000007935 neutral effect Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000035515 penetration Effects 0.000 description 1
- 238000004064 recycling Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012706 support-vector machine Methods 0.000 description 1
- 230000001131 transforming effect Effects 0.000 description 1
Classifications
-
- 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
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/20—Scenes; Scene-specific elements in augmented reality scenes
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Life Sciences & Earth Sciences (AREA)
- Multimedia (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Evolutionary Computation (AREA)
- Evolutionary Biology (AREA)
- General Engineering & Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Computational Biology (AREA)
- Artificial Intelligence (AREA)
- High Energy & Nuclear Physics (AREA)
- General Life Sciences & Earth Sciences (AREA)
- Geophysics (AREA)
- Image Analysis (AREA)
Abstract
The invention provides a kind of safety check detection method, device, system and electronic equipment, wherein, this method includes obtaining the x-ray image of the X-ray machine collection in the screening machine that safety check terminal receives, and the x-ray image is pre-processed, obtains pretreated x-ray image；According to the article characteristics of corresponding thing to be detected in the default pretreated x-ray image of deep learning model extraction, the default deep learning model includes the deep learning model based on convolutional neural networks；Article characteristics are identified using the grader based on default deep learning model training, the recognition result of the corresponding thing to be detected of generation；The recognition result of thing to be detected is sent to safety check terminal, so that safety check terminal shows the recognition result.Technical scheme provided in an embodiment of the present invention, the automatic identification detection to contraband is realized, and while recognition efficiency is improved, the accuracy to contraband identification has been effectively ensured, has prevented the generation of potential safety hazard.
Description
Technical field
The present invention relates to technical field of security inspection equipment, more particularly, to a kind of safety check detection method, device, system and electronics
Equipment.
Background technology
With the enhancing that public security is realized, various rays safety detection apparatus are widely used in airport, port, harbour, subway, method
Institute, the important public place such as venue of important sports events.And most common of which is exactly (X ray) screening machine, screening machine exists at present
Passenger flow and logistics field have obtained more and more extensive application.General screening machine all includes motor, by motor-driven conveyer belt
System, the fuselage being crossed in the middle part of conveyer belt system, certainly also supporting X-ray machine.The fuselage and conveyer belt of usual screening machine
Between form security check passage, at least side of security check passage is provided with X-ray machine.One end of conveyer belt system is region to be checked, to be checked
After survey thing is placed on region to be checked, the conveyer belt system that can be motor driven is transferred to the other end, necessarily passes safety check halfway and leads to
Road, scanning generation X-ray imaging is irradiated by X-ray machine, so as to identify whether thing to be detected is contraband.
But existing detection method is due to easily by the outside environmental elements such as detection penetrability, detection angles or outer
Boundary's interference effect, greatly reduce identification accuracy；And, it is necessary to which security staff is to the figure of display after terminal X light image
Piece is investigated.So security staff's long-time monitor screen easily causes visual fatigue, causes the feelings such as flase drop, false retrieval, missing inspection
Condition occurs.
Therefore, existing safety check detection method, it is difficult to ensure the accuracy of identification, and recognition efficiency is low, easily causes peace
Full hidden danger.
The content of the invention
In view of this, it is an object of the invention to provide a kind of safety check detection method, device, system and electronic equipment, with
While recognition efficiency is improved, the accuracy to contraband identification is effectively ensured, prevents potential safety hazard.
In a first aspect, the embodiments of the invention provide a kind of safety check detection method, including：
The x-ray image of the X-ray machine collection in the screening machine that safety check terminal receives is obtained, the x-ray image is carried out pre-
Processing, obtains pretreated x-ray image；
According to the article of corresponding thing to be detected in pretreated x-ray image described in default deep learning model extraction
Feature, the default deep learning model include the deep learning model based on convolutional neural networks；
The article characteristics are identified using the grader based on the default deep learning model training, generated
The recognition result of the corresponding thing to be detected；
The recognition result of the thing to be detected is sent to the safety check terminal, so that the safety check terminal shows the knowledge
Other result.
With reference in a first aspect, the embodiments of the invention provide the possible embodiment of the first of first aspect, wherein, institute
State and the x-ray image is pre-processed, including：
Smoothing denoising is carried out to the x-ray image of collection using neighborhood averaging, obtains the X-ray figure after smoothing denoising
Picture；
The marginal information of the image after the smoothing denoising is strengthened using histogram equalization method, after obtaining pretreatment
X-ray image.
With reference in a first aspect, the embodiments of the invention provide the possible embodiment of second of first aspect, wherein, institute
It is that the article sample data for exceeding certain threshold value by quantity trains to obtain to state the deep learning model based on convolutional neural networks
, the article sample data includes picture corresponding to the contraband of different shape.
With reference in a first aspect, the embodiments of the invention provide the possible embodiment of the third of first aspect, wherein, institute
Stating the training process of grader includes：
Utilize the depth characteristic of the deep learning model extraction article sample data based on convolutional neural networks；
Based on machine learning algorithm, grader is trained to the depth characteristic；
Wherein described article sample data include the X-ray picture for the different recognition results specified.
Second aspect, the embodiment of the present invention also provide a kind of safety check detection means, including：
Pretreatment module, the x-ray image of the X-ray machine collection in the screening machine received for obtaining safety check terminal, to institute
State x-ray image to be pre-processed, obtain pretreated x-ray image；
Characteristic extracting module, for right in the pretreated x-ray image according to default deep learning model extraction
The article characteristics for the thing to be detected answered, the default deep learning model include the deep learning mould based on convolutional neural networks
Type；
As a result identification module, for utilizing the grader based on the default deep learning model training to the article
Feature is identified, the recognition result of the corresponding thing to be detected of generation；
Result display module, for the recognition result of the thing to be detected to be sent to the safety check terminal, so that described
Safety check terminal shows the recognition result.
The third aspect, the embodiment of the present invention also provide a kind of safety check detecting system, including screening machine, safety check terminal and safety check
Identification equipment, X-ray machine is provided with the safety check case of the screening machine, and the safety check identification equipment is included as described in second aspect
Safety check detection means；The X-ray machine, the safety check identification equipment are connected with the safety check terminal respectively；
The X-ray machine, the x-ray image of the thing to be detected for gathering the security check passage by the screening machine, by the X
Light image is sent to the safety check terminal；
The safety check terminal, for when listening to the x-ray image of reception, the x-ray image to be sent to safety check
Identification equipment；It is additionally operable to receive the recognition result for the thing to be detected that the safety check identification equipment is sent, the identification is tied
Fruit is shown by display screen.
With reference to the third aspect, the embodiments of the invention provide the possible embodiment of the first of the third aspect, wherein, institute
Stating recognition result includes contraband and non-contraband two types；The system also includes warning device, the warning device with
The safety check terminal connection；
The safety check terminal, it is additionally operable to when the recognition result received is contraband, sends alarm signal to institute
Warning device is stated, so that the warning device carries out alarm.
With reference to the third aspect, the embodiments of the invention provide the possible embodiment of second of the third aspect, wherein, institute
The bottom for stating the security check passage of screening machine is provided with pressure sensor, and the pressure sensor is connected with the safety check terminal；
The pressure sensor, for gathering the pressure information born on the security check passage, the pressure information is sent out
Deliver to the safety check terminal；
The safety check terminal, it is additionally operable to be turned on or off the X-ray machine according to the pressure information.
With reference to the third aspect and its any possible embodiment, the embodiments of the invention provide the 3rd of the third aspect
The possible embodiment of kind, wherein, the safety check identification equipment includes Nvidia Jetson TX2 chips.
Fourth aspect, the embodiment of the present invention also provide a kind of electronic equipment, including memory, processor, the memory
On be stored with the computer program that can be run on the processor, described in the computing device during computer program realize on
State first aspect and its method described in any possible embodiment.
The embodiment of the present invention brings following beneficial effect：
The embodiments of the invention provide a kind of safety check detection method, device, system and electronic equipment, wherein this method includes
The x-ray image of the X-ray machine collection in the screening machine that safety check terminal receives is obtained, the x-ray image is pre-processed, obtained pre-
X-ray image after processing；According to corresponding to be detected in pretreated x-ray image described in default deep learning model extraction
The article characteristics of thing, the default deep learning model include the deep learning model based on convolutional neural networks；Using based on
Article characteristics are identified the grader of default deep learning model training, the recognition result of the corresponding thing to be detected of generation；
The recognition result of thing to be detected is sent to safety check terminal, so that safety check terminal shows the recognition result.In the embodiment of the present invention
In the technical scheme of offer, reduce the influence of outside environmental elements first with pretreatment, then by based on convolutional Neural net
The deep learning model extraction article characteristics of network, and treat detectable substance using the grader of the deep learning model training and known
Not, the automatic identification detection to contraband has been achieved in that, and while recognition efficiency is improved, has been effectively ensured to contraband
The accuracy of identification, prevent the generation of potential safety hazard.
Other features and advantages of the present invention will illustrate in the following description, also, partly become from specification
Obtain it is clear that or being understood by implementing the present invention.The purpose of the present invention and other advantages are in specification, claims
And specifically noted structure is realized and obtained in accompanying drawing.
To enable the above objects, features and advantages of the present invention to become apparent, preferred embodiment cited below particularly, and coordinate
Appended accompanying drawing, is described in detail below.
Brief description of the drawings
, below will be to specific in order to illustrate more clearly of the specific embodiment of the invention or technical scheme of the prior art
The required accompanying drawing used is briefly described in embodiment or description of the prior art, it should be apparent that, in describing below
Accompanying drawing is some embodiments of the present invention, for those of ordinary skill in the art, before creative work is not paid
Put, other accompanying drawings can also be obtained according to these accompanying drawings.
Fig. 1 is the schematic flow sheet of safety check detection method provided in an embodiment of the present invention；
Fig. 2 is the structural representation of safety check detection means provided in an embodiment of the present invention；
Fig. 3 is the communication link map interlinking of safety check detecting system provided in an embodiment of the present invention；
Fig. 4 is the structural representation of electronic equipment provided in an embodiment of the present invention.
Embodiment
To make the purpose, technical scheme and advantage of the embodiment of the present invention clearer, below in conjunction with accompanying drawing to the present invention
Technical scheme be clearly and completely described, it is clear that described embodiment is part of the embodiment of the present invention, rather than
Whole embodiments.Based on the embodiment in the present invention, those of ordinary skill in the art are not making creative work premise
Lower obtained every other embodiment, belongs to the scope of protection of the invention.
Current existing safety check detection method, it is difficult to ensure the accuracy of identification, and recognition efficiency is low, easily causes safety
Hidden danger, based on this, a kind of safety check detection method, device, system and electronic equipment provided in an embodiment of the present invention, it can utilize pre-
Processing reduces the influence of outside environmental elements, then special by the deep learning model extraction article based on convolutional neural networks
Sign, and treat detectable substance using the grader of the deep learning model training and be identified, be achieved in that to contraband from
Dynamic recognition detection, and while recognition efficiency is improved, the accuracy to contraband identification has been effectively ensured, prevent safety hidden
The generation of trouble.
For ease of understanding the present embodiment, a kind of safety check detection method disclosed in the embodiment of the present invention is entered first
Row is discussed in detail.
Embodiment one：
Safety check detection method provided in an embodiment of the present invention can be, but not limited to be applied to airport, port, harbour, subway,
Law court, important sports events the safety check scene of important public place such as venue in.
Fig. 1 shows the schematic flow sheet of safety check detection method provided in an embodiment of the present invention.As shown in figure 1, the safety check
Detection method includes：
Step S101, the x-ray image of the X-ray machine collection in the screening machine that safety check terminal receives is obtained, to the x-ray image
Pre-processed, obtain pretreated x-ray image.
The safety check terminal can be, but not limited to as computer, console.Specifically, when product to be detected pass through the safety check of screening machine
During passage, the X-ray machine being arranged in the safety check case of screening machine can be scanned to product to be detected and generate x-ray image, by the X-ray
Image is sent to above-mentioned safety check terminal.
The x-ray image of safety check terminal transmission is being got, during detecting x-ray image, is requiring that the x-ray image has first
There is good performance.But because external interference and X-ray machine oneself factor can cause the reduction of x-ray image quality, based on this
The pretreatment operation that inventive embodiments use mainly includes image enhaucament and denoising, above-mentioned to carry out pretreatment bag to the x-ray image
Include：
Smoothing denoising is carried out to the x-ray image of collection using neighborhood averaging, obtains the x-ray image after smoothing denoising；
The marginal information of the image after smoothing denoising is strengthened using histogram equalization method, obtains pretreated X
Light image.
By above-mentioned preprocess method, the useful information such as marginal information in image can be strengthened, to a certain extent
Weaken interference (medium scatters, high-speed motion, noise jamming), improve the performance of image so that the feature of image is fully shown
Come, be more beneficial for the feature extraction and expression in later stage.
Step S102, according to corresponding to be checked in the above-mentioned pretreated x-ray image of default deep learning model extraction
The article characteristics of thing are surveyed, the default deep learning model includes the deep learning model based on convolutional neural networks.
The above-mentioned deep learning model based on convolutional neural networks is the article sample number for exceeding certain threshold value by quantity
Obtained according to training, the article sample data includes X-ray picture corresponding to the contraband of different shape.Wherein, contraband is not
Include the state that such as gun split into the state of each parts, cutter folds with form.In a preferred embodiment,
The above-mentioned deep learning model based on convolutional neural networks can be realized by Caffe deep learnings framework.
Specifically, the quantity of above-mentioned X-ray picture is The more the better, and data are more, train generation based on convolutional neural networks
Deep learning model versatility it is better, such as above-mentioned X-ray picture include multiple angles, the various contrabands of a variety of penetration levels
Picture, be so advantageous to subsequently treat accurately identifying for detectable substance, overcome the influence of outside environmental elements, improving should be based on volume
The recognition capability of the deep learning model of product neutral net.
Step S102 is specifically included：Using pretreated x-ray image as input picture in default deep learning mould
Carry out features training successively in the multiple basic units included in type, after the completion of training, extract it is multiple it is integrated in full articulamentum or
Person other specify the characteristic vector of basic units' output as corresponding to article characteristics to be detected in pretreated x-ray image.
Further, in order to simple and handle characteristics of image rapidly, in step s 102 according to default deep learning mould
Type is extracted in pretreated x-ray image before the article characteristics of corresponding thing to be detected, in addition to：
Pretreated x-ray image is divided into according to the gamma characteristic of pretreated x-ray image by background and the class of template two,
The variance made between two classes is obtained into maximum parameter as optimal threshold；
Binary image is obtained using the Optimal-threshold segmentation, using the binary image as pretreated x-ray image.
In various threshold optimization dividing methods, OTSU algorithms propose that maximizing split plot design based on inter-class variance is acknowledged as
It is Optimal-threshold segmentation algorithm, it divides the image into background and the class of target two according to the gamma characteristic of image, then calculates and allows two
Variance between class obtains maximum parameter as optimal threshold, the binary picture for recycling Optimal-threshold segmentation to be worked well
Picture.
Thus, by above-mentioned binary conversion treatment, the gray value of the pixel on x-ray image is arranged to 0 or 255, X-ray
Data volume is greatly reduced in image, and image processing speed can substantially reduce.
Step S103, above-mentioned article characteristics are carried out using the grader based on above-mentioned default deep learning model training
Identification, the recognition result of the corresponding thing to be detected of generation.
Input i.e. using the feature extracted in step 102 as the grader based on default deep learning model training,
After being identified by the grader, final recognition result is obtained.Specifically, recognition result is contraband or non-contraband, can
With but be not limited by correct or error identification, and apply different picture identifications, be specifically identified method and be not construed as limiting here.
In an optional embodiment, the training process for the grader applied in step 103 includes：
Utilize the depth characteristic of the deep learning model extraction article sample data based on convolutional neural networks；
Based on machine learning algorithm, grader is trained to above-mentioned depth characteristic；
Wherein above-mentioned article sample data include the X-ray picture for the different recognition results specified.Above-mentioned machine learning is calculated
Method can be nearest neighbor algorithm, EM algorithm and algorithm of support vector machine etc., and specific algorithm can select as the case may be,
Here it is not construed as limiting.
In an optional embodiment, above-mentioned article sample data include triple data；The wherein triple data
Including：Source data and source data belong to same category of forward data and adhere to different classes of reverse number separately with the source data
According to.
Wherein, source data is the recognition result identical sample data got at random from article sample data.
Forward data is the sample data consistent with the recognition result of source data obtained at random from article sample data；
The matching degree of the source data is higher than the matching degree of forward data.
Reverse data is the sample number inconsistent with the recognition result of source data obtained at random from article sample data
According to.
In a specific embodiment, triple data are respectively：X-ray image is of good performance in article sample data
First picture (source data), the second picture (forward data) of the x-ray image poor-performing shot in article sample data, and
The 3rd picture as reverse data different from the first picture and second picture recognition result.First picture and second picture
Recognition result is contraband, and the recognition result of second picture is non-contraband.Second picture is because image property is poor, such as clear
There is gap in clear degree, resolution ratio etc., its matching degree is less than the first picture with the first picture.3rd picture is then in training
The reverse data of reverse contrast is carried out, once by positive and negative contrast, further enhancing the recognition capability of grader.
Step S104, the recognition result of above-mentioned thing to be detected is sent to safety check terminal, so that safety check terminal shows the knowledge
Other result.
Specifically, the safety check terminal is carried out after the recognition result of thing to be detected is received in the display interface of display screen
Render, to show the recognition result.
In technical scheme provided in an embodiment of the present invention, reduce the influence of outside environmental elements first with pretreatment,
Then by the deep learning model extraction article characteristics based on convolutional neural networks, and the deep learning model training is utilized
Grader is treated detectable substance and is identified, and has been achieved in that the automatic identification detection to contraband, and improving recognition efficiency
Meanwhile the accuracy to contraband identification has been effectively ensured, prevent the generation of potential safety hazard.
Embodiment two：
Fig. 2 shows the structural representation of safety check detection means provided in an embodiment of the present invention.As shown in Fig. 2 the safety check
Detection means includes：
Pretreatment module 11, the x-ray image of the X-ray machine collection in the screening machine received for obtaining safety check terminal are right
The x-ray image is pre-processed, and obtains pretreated x-ray image；
Characteristic extracting module 12, for according in the above-mentioned pretreated x-ray image of default deep learning model extraction
The article characteristics of corresponding thing to be detected, the default deep learning model include the deep learning mould based on convolutional neural networks
Type；
As a result identification module 13, for utilizing the grader based on above-mentioned default deep learning model training to above-mentioned thing
Product feature is identified, the recognition result of the corresponding thing to be detected of generation；
Result display module 14, for the recognition result of above-mentioned thing to be detected to be sent to safety check terminal, so that safety check is whole
End shows the recognition result.
The above-mentioned deep learning model based on convolutional neural networks is the article sample number for exceeding certain threshold value by quantity
Obtained according to training, the article sample data includes X-ray picture corresponding to the contraband of different shape.Wherein, contraband is not
Include the state that such as gun split into the state of each parts, cutter folds with form.In a preferred embodiment,
The above-mentioned deep learning model based on convolutional neural networks can be realized by Caffe deep learnings framework.
In technical scheme provided in an embodiment of the present invention, reduce the influence of outside environmental elements first with pretreatment,
Then by the deep learning model extraction article characteristics based on convolutional neural networks, and the deep learning model training is utilized
Grader is treated detectable substance and is identified, and has been achieved in that the automatic identification detection to contraband, and improving recognition efficiency
Meanwhile the accuracy to contraband identification has been effectively ensured, prevent the generation of potential safety hazard.
Embodiment three：
Fig. 3 shows the communication link map interlinking of safety check detecting system provided in an embodiment of the present invention.As shown in figure 3, the safety check
Detecting system includes：Including screening machine 400, safety check terminal 500 and safety check identification equipment 600, set in the safety check case of the screening machine
X-ray machine 700 is equipped with, safety check identification equipment is included such as the safety check detection means in embodiment two；X-ray machine, safety check identification equipment point
It is not connected with safety check terminal.
X-ray machine, the x-ray image of the thing to be detected for gathering the security check passage by screening machine, the x-ray image is sent
To the safety check terminal.
Safety check terminal, for when listening to the above-mentioned x-ray image of reception, x-ray image to be sent to safety check identification equipment；
It is additionally operable to receive the recognition result for the thing to be detected that safety check identification equipment is sent, the recognition result is shown by display screen.
In technical scheme provided in an embodiment of the present invention, reduce the influence of outside environmental elements first with pretreatment,
Then by the deep learning model extraction article characteristics based on convolutional neural networks, and the deep learning model training is utilized
Grader is treated detectable substance and is identified, and has been achieved in that the automatic identification detection to contraband, and improving recognition efficiency
Meanwhile the accuracy to contraband identification has been effectively ensured, prevent the generation of potential safety hazard.
In an optional embodiment, above-mentioned recognition result includes contraband and non-contraband two types；Above-mentioned peace
Inspection detecting system also includes warning device 800, and the warning device is connected with safety check terminal.
Specifically, the safety check terminal is additionally operable to when the above-mentioned recognition result received is contraband, sends alarm signal
To warning device, so that warning device carries out alarm.Wherein the type of alarm of warning device includes light warning, voice reporting
The alarm of alert or picture and text showing.
In another optional embodiment, the bottom of the security check passage of screening machine is provided with pressure sensor 900, the pressure
Force snesor is connected with safety check terminal.
Pressure sensor is used to gather the pressure information born on security check passage, and pressure information is sent to safety check terminal.
Safety check terminal is additionally operable to be turned on or off X-ray machine according to the pressure information.
Specifically, the pressure value of pressure sensor is read when security check passage zero load, using the pressure value as pressure
Threshold value, when the pressure information that pressure sensor that safety check terminal receives is sent exceedes the pressure threshold, illustrate to have to be detected
Thing will then open X-ray machine by security check passage, so that the X-ray machine gathers the x-ray image of thing to be detected.When safety check terminal connects
When the pressure information that the pressure sensor received is sent returns to the pressure threshold, illustrate thing to be detected from security check passage
It is removed, closes the X-ray machine.So, automatically opening up and closing for X-ray machine is realized, serves and saves the energy and extension machine
The effect of service life.
In one embodiment, above-mentioned safety check identification equipment includes Nvidia Jetson TX2 chips, and the chip is in low work(
Powerful operational capability is maintained while consumption, it is possible to achieve the prohibited items in Millisecond identifies X-ray picture.Whole core
Only credit card-sized, the Real time identification of contraband can be both realized on the basis of not transforming existing X-ray machine, again can be with
X-ray machine combines offer identification service.
Example IV：
Referring to Fig. 4, the embodiment of the present invention also provides a kind of electronic equipment 100, including：Processor 40, memory 41, bus
42 and communication interface 43, the processor 40, communication interface 43 and memory 41 connected by bus 42；Processor 40 is used to hold
The executable module stored in line storage 41, such as computer program.
Wherein, memory 41 may include high-speed random access memory (RAM, Random Access Memory),
Non-labile memory (non-volatile memory), for example, at least a magnetic disk storage may also be included.By extremely
A few communication interface 43 (can be wired or wireless) is realized logical between the system network element and at least one other network element
Letter connection, can use internet, wide area network, LAN, Metropolitan Area Network (MAN) etc..
Bus 42 can be isa bus, pci bus or eisa bus etc..The bus can be divided into address bus, data
Bus, controlling bus etc..Only represented for ease of representing, in Fig. 4 with a four-headed arrow, it is not intended that an only bus or
A type of bus.
Wherein, memory 41 is used for storage program, and the processor 40 performs the journey after execute instruction is received
Sequence, the method performed by device that the stream process that foregoing any embodiment of the embodiment of the present invention discloses defines can apply to handle
In device 40, or realized by processor 40.
Processor 40 is probably a kind of IC chip, has the disposal ability of signal.In implementation process, above-mentioned side
Each step of method can be completed by the integrated logic circuit of the hardware in processor 40 or the instruction of software form.Above-mentioned
Processor 40 can be general processor, including central processing unit (Central Processing Unit, abbreviation CPU), network
Processor (Network Processor, abbreviation NP) etc.；It can also be digital signal processor (Digital Signal
Processing, abbreviation DSP), application specific integrated circuit (Application Specific Integrated Circuit, referred to as
ASIC), ready-made programmable gate array (Field-Programmable Gate Array, abbreviation FPGA) or other are programmable
Logical device, discrete gate or transistor logic, discrete hardware components.It can realize or perform in the embodiment of the present invention
Disclosed each method, step and logic diagram.General processor can be microprocessor or the processor can also be appointed
What conventional processor etc..The step of method with reference to disclosed in the embodiment of the present invention, can be embodied directly in hardware decoding processing
Device performs completion, or performs completion with the hardware in decoding processor and software module combination.Software module can be located at
Machine memory, flash memory, read-only storage, programmable read only memory or electrically erasable programmable memory, register etc. are originally
In the ripe storage medium in field.The storage medium is located at memory 41, and processor 40 reads the information in memory 41, with reference to
Its hardware completes the step of above method.
Safety check detection means, system and electronic equipment provided in an embodiment of the present invention, the safety check provided with above-described embodiment
Detection method has identical technical characteristic, so can also solve identical technical problem, reaches identical technique effect.
The computer program product for the progress safety check detection method that the embodiment of the present invention is provided, including store processor
The computer-readable recording medium of executable non-volatile program code, the instruction that described program code includes can be used for performing
Method described in previous methods embodiment, specific implementation can be found in embodiment of the method, will not be repeated here.
It is apparent to those skilled in the art that for convenience and simplicity of description, the device of foregoing description,
The specific work process of system and electronic equipment, the corresponding process in preceding method embodiment is may be referred to, will not be repeated here.
Flow chart and block diagram in accompanying drawing show multiple embodiment method and computer program products according to the present invention
Architectural framework in the cards, function and operation.At this point, each square frame in flow chart or block diagram can represent one
A part for module, program segment or code, a part for the module, program segment or code include one or more and are used to realize
The executable instruction of defined logic function.It should also be noted that at some as the work(in the realization replaced, marked in square frame
Energy can also be with different from the order marked in accompanying drawing generation.For example, two continuous square frames can essentially be substantially parallel
Ground is performed, and they can also be performed in the opposite order sometimes, and this is depending on involved function.It is also noted that block diagram
And/or the combination of each square frame and block diagram in flow chart and/or the square frame in flow chart, work(as defined in performing can be used
Can or the special hardware based system of action realize, or the combination of specialized hardware and computer instruction can be used come reality
It is existing.In addition, term " first ", " second ", " the 3rd " are only used for describing purpose, and it is not intended that instruction or implying relatively important
Property.
In several embodiments provided herein, it should be understood that disclosed systems, devices and methods, can be with
Realize by another way.Device embodiment described above is only schematical, for example, the division of the unit,
Only a kind of division of logic function, can there is other dividing mode when actually realizing, in another example, multiple units or component can
To combine or be desirably integrated into another system, or some features can be ignored, or not perform.It is another, it is shown or beg for
The mutual coupling of opinion or direct-coupling or communication connection can be by some communication interfaces, device or unit it is indirect
Coupling or communication connection, can be electrical, mechanical or other forms.
The unit illustrated as separating component can be or may not be physically separate, show as unit
The part shown can be or may not be physical location, you can with positioned at a place, or can also be distributed to multiple
On NE.Some or all of unit therein can be selected to realize the mesh of this embodiment scheme according to the actual needs
's.
In addition, each functional unit in each embodiment of the present invention can be integrated in a processing unit, can also
That unit is individually physically present, can also two or more units it is integrated in a unit.
If the function is realized in the form of SFU software functional unit and is used as independent production marketing or in use, can be with
It is stored in the executable non-volatile computer read/write memory medium of a processor.Based on such understanding, the present invention
The part that is substantially contributed in other words to prior art of technical scheme or the part of the technical scheme can be with software
The form of product is embodied, and the computer software product is stored in a storage medium, including some instructions are causing
One computer equipment (can be personal computer, server, or network equipment etc.) performs each embodiment institute of the present invention
State all or part of step of method.And foregoing storage medium includes：USB flash disk, mobile hard disk, read-only storage (ROM, Read-
Only Memory), random access memory (RAM, Random Access Memory), magnetic disc or CD etc. are various can be with
The medium of store program codes.
Finally it should be noted that：Embodiment described above, it is only the embodiment of the present invention, to illustrate the present invention
Technical scheme, rather than its limitations, protection scope of the present invention is not limited thereto, although with reference to the foregoing embodiments to this hair
It is bright to be described in detail, it will be understood by those within the art that：Any one skilled in the art
The invention discloses technical scope in, it can still modify to the technical scheme described in previous embodiment or can be light
Change is readily conceivable that, or equivalent substitution is carried out to which part technical characteristic；And these modifications, change or replacement, do not make
The essence of appropriate technical solution departs from the spirit and scope of technical scheme of the embodiment of the present invention, should all cover the protection in the present invention
Within the scope of.Therefore, protection scope of the present invention described should be defined by scope of the claims.
Claims (10)
- A kind of 1. safety check detection method, it is characterised in that including：The x-ray image of the X-ray machine collection in the screening machine that safety check terminal receives is obtained, the x-ray image is pre-processed, Obtain pretreated x-ray image；It is special according to the article of corresponding thing to be detected in pretreated x-ray image described in default deep learning model extraction Sign, the default deep learning model include the deep learning model based on convolutional neural networks；The article characteristics are identified using the grader based on the default deep learning model training, generation is corresponding The recognition result of the thing to be detected；The recognition result of the thing to be detected is sent to the safety check terminal, so that the safety check terminal shows the identification knot Fruit.
- 2. according to the method for claim 1, it is characterised in that it is described that the x-ray image is pre-processed, including：Smoothing denoising is carried out to the x-ray image of collection using neighborhood averaging, obtains the x-ray image after smoothing denoising；The marginal information of the image after the smoothing denoising is strengthened using histogram equalization method, obtains pretreated X Light image.
- 3. according to the method for claim 1, it is characterised in that the deep learning model based on convolutional neural networks is The article sample data for exceeding certain threshold value by quantity trains what is obtained, and the article sample data includes disobeying for different shape Picture corresponding to contraband goods.
- 4. according to the method for claim 1, it is characterised in that the training process of the grader includes：Utilize the depth characteristic of the deep learning model extraction article sample data based on convolutional neural networks；Based on machine learning algorithm, grader is trained to the depth characteristic；Wherein described article sample data include the X-ray picture for the different recognition results specified.
- A kind of 5. safety check detection means, it is characterised in that including：Pretreatment module, the x-ray image of the X-ray machine collection in the screening machine received for obtaining safety check terminal, to the X-ray Image is pre-processed, and obtains pretreated x-ray image；Characteristic extracting module, corresponding in the pretreated x-ray image according to default deep learning model extraction The article characteristics of thing to be detected, the default deep learning model include the deep learning model based on convolutional neural networks；As a result identification module, for utilizing the grader based on the default deep learning model training to the article characteristics It is identified, the recognition result of the corresponding thing to be detected of generation；Result display module, for the recognition result of the thing to be detected to be sent to the safety check terminal, so that the safety check Terminal shows the recognition result.
- 6. a kind of safety check detecting system, it is characterised in that including screening machine, safety check terminal and safety check identification equipment, the safety check X-ray machine is provided with the safety check case of machine, the safety check identification equipment includes safety check detection means as claimed in claim 5；Institute State X-ray machine, the safety check identification equipment is connected with the safety check terminal respectively；The X-ray machine, the x-ray image of the thing to be detected for gathering the security check passage by the screening machine, by the X-ray figure As sending to the safety check terminal；The safety check terminal, for when listening to the x-ray image of reception, the x-ray image being sent to safety check and identified Equipment；It is additionally operable to receive the recognition result for the thing to be detected that the safety check identification equipment is sent, the recognition result is led to Display screen is crossed to show.
- 7. system according to claim 6, it is characterised in that the recognition result includes two kinds of contraband and non-contraband Type；The system also includes warning device, and the warning device is connected with the safety check terminal；The safety check terminal, it is additionally operable to when the recognition result received is contraband, sends alarm signal to the report Alarm device, so that the warning device carries out alarm.
- 8. system according to claim 6, it is characterised in that the bottom of the security check passage of the screening machine is provided with pressure Sensor, the pressure sensor are connected with the safety check terminal；The pressure sensor, for gathering the pressure information born on the security check passage, by the pressure information send to The safety check terminal；The safety check terminal, it is additionally operable to be turned on or off the X-ray machine according to the pressure information.
- 9. according to the system described in claim any one of 6-8, it is characterised in that the safety check identification equipment includes Nvidia Jetson TX2 chips.
- 10. a kind of electronic equipment, including memory, processor, it is stored with and can runs on the processor on the memory Computer program, it is characterised in that the claims 1 to 4 are realized described in the computing device during computer program Method described in one.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711126618.2A CN107871122A (en) | 2017-11-14 | 2017-11-14 | Safety check detection method, device, system and electronic equipment |
| PCT/CN2018/115498 WO2019096181A1 (en) | 2017-11-14 | 2018-11-14 | Detection method, apparatus and system for security inspection, and electronic device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711126618.2A CN107871122A (en) | 2017-11-14 | 2017-11-14 | Safety check detection method, device, system and electronic equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN107871122A true CN107871122A (en) | 2018-04-03 |
Family
ID=61753983
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711126618.2A Pending CN107871122A (en) | 2017-11-14 | 2017-11-14 | Safety check detection method, device, system and electronic equipment |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN107871122A (en) |
| WO (1) | WO2019096181A1 (en) |
Cited By (37)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108519625A (en) * | 2018-04-08 | 2018-09-11 | 安徽理工大学 | Recognizable prohibited items safe examination system based on ray technology and deep learning |
| CN108830276A (en) * | 2018-07-02 | 2018-11-16 | 合肥格泉智能科技有限公司 | A kind of intelligent identifying system based on X-ray machine image |
| CN109001833A (en) * | 2018-06-22 | 2018-12-14 | 天和防务技术（北京）有限公司 | A kind of Terahertz hazardous material detection method based on deep learning |
| CN109086740A (en) * | 2018-08-25 | 2018-12-25 | 李叶 | Stagewise ferry safety check mechanism |
| CN109101937A (en) * | 2018-08-22 | 2018-12-28 | 海尔优家智能科技（北京）有限公司 | Item identification method, device, cloud server, intelligent appliance equipment and system |
| CN109584339A (en) * | 2018-12-04 | 2019-04-05 | 中国民航大学 | A kind of safety check contraband x-ray image generation method based on production confrontation network |
| WO2019096181A1 (en) * | 2017-11-14 | 2019-05-23 | 深圳码隆科技有限公司 | Detection method, apparatus and system for security inspection, and electronic device |
| CN109884721A (en) * | 2018-12-10 | 2019-06-14 | 深圳极视角科技有限公司 | Safety check prohibited items detection method, device and electronic equipment based on artificial intelligence |
| 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 |
| CN109946746A (en) * | 2019-03-21 | 2019-06-28 | 长安大学 | A kind of safe examination system and method based on deep neural network |
| CN109978827A (en) * | 2019-02-25 | 2019-07-05 | 平安科技（深圳）有限公司 | Violated object recognition methods, device, equipment and storage medium based on artificial intelligence |
| CN109975332A (en) * | 2019-02-25 | 2019-07-05 | 北京声迅电子股份有限公司 | A kind of intelligent quality testing system applied to X-ray machine |
| CN110059618A (en) * | 2019-04-17 | 2019-07-26 | 北京易达图灵科技有限公司 | A kind of recognition methods of prohibited items and device |
| CN110163191A (en) * | 2019-06-17 | 2019-08-23 | 北京航星机器制造有限公司 | A kind of dangerous material intelligent identification Method, system and dangerous material safe examination system |
| CN110261923A (en) * | 2018-08-02 | 2019-09-20 | 浙江大华技术股份有限公司 | A kind of contraband detecting method and device |
| CN110286415A (en) * | 2019-07-12 | 2019-09-27 | 广东工业大学 | Safety check contraband detecting method, apparatus, equipment and computer readable storage medium |
| CN110378912A (en) * | 2019-07-12 | 2019-10-25 | 深圳码隆科技有限公司 | Wrap up detection method, device, computer readable storage medium and computer equipment |
| CN110543857A (en) * | 2019-09-05 | 2019-12-06 | 安徽启新明智科技有限公司 | Contraband identification method, device and system based on image analysis and storage medium |
| CN110580710A (en) * | 2019-08-21 | 2019-12-17 | 深圳码隆科技有限公司 | object tracking method, device, computer readable storage medium and computer equipment |
| CN110738096A (en) * | 2019-08-23 | 2020-01-31 | 银河水滴科技（北京）有限公司 | intelligent security inspection method, equipment and computer readable storage medium |
| CN110781812A (en) * | 2019-10-24 | 2020-02-11 | 谷琛 | Method for automatically identifying target object by security check instrument based on machine learning |
| CN110794466A (en) * | 2019-07-16 | 2020-02-14 | 中云智慧（北京）科技有限公司 | X-ray machine picture acquisition auxiliary device and processing method |
| CN110850493A (en) * | 2019-10-24 | 2020-02-28 | 武汉艾崴科技有限公司 | Linear array high-speed security inspection machine for visual image judgment of scanning imaging machine |
| CN111103629A (en) * | 2018-10-25 | 2020-05-05 | 杭州海康威视数字技术股份有限公司 | Target detection method and device, NVR (network video recorder) equipment and security check system |
| CN111126447A (en) * | 2019-11-29 | 2020-05-08 | 中国船舶重工集团公司第七一三研究所 | Intelligent passenger security check luggage image automatic identification method |
| CN111126238A (en) * | 2019-12-19 | 2020-05-08 | 华南理工大学 | X-ray security inspection system and method based on convolutional neural network |
| CN111167733A (en) * | 2020-01-14 | 2020-05-19 | 东莞理工学院 | Automatic visual defect detection system |
| CN111310635A (en) * | 2020-02-10 | 2020-06-19 | 上海应用技术大学 | Security inspection contraband identification system and method based on TensorFlow |
| CN111435119A (en) * | 2019-01-11 | 2020-07-21 | 合肥美亚光电技术股份有限公司 | X-ray foreign matter detection system and method based on block chain and computer equipment |
| CN111665572A (en) * | 2020-05-30 | 2020-09-15 | 中国船舶重工集团公司第七一三研究所 | Airport passenger security inspection intelligent auxiliary system and method based on X-ray machine image |
| CN111901577A (en) * | 2020-01-15 | 2020-11-06 | 吴庆芹 | Instant projection system based on signal analysis and corresponding terminal |
| CN113436151A (en) * | 2021-06-07 | 2021-09-24 | 中国铁道科学研究院集团有限公司电子计算技术研究所 | Security check method and device, electronic equipment and storage medium |
| CN113640321A (en) * | 2020-05-11 | 2021-11-12 | 同方威视技术股份有限公司 | Security check delay optimization method and device |
| CN113960687A (en) * | 2020-07-03 | 2022-01-21 | 株式会社日立解决方案 | X-ray image processing device and X-ray image processing method |
| CN117409199A (en) * | 2023-10-19 | 2024-01-16 | 中南大学 | Growing intelligent security inspection system and method based on cloud big data technology |
| CN117422881A (en) * | 2023-11-15 | 2024-01-19 | 山东衡昊信息技术有限公司 | Intelligent centralized security check graph judging system |
| CN117422881B (en) * | 2023-11-15 | 2024-09-24 | 山东衡昊信息技术有限公司 | Intelligent centralized security check graph judging system |
Families Citing this family (26)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110197480B (en) * | 2019-05-29 | 2023-01-03 | 江汉大学 | Hexavalent chromium detection method and device and readable storage medium |
| CN110222675A (en) * | 2019-07-02 | 2019-09-10 | 同略科技有限公司 | A kind of police strength dispatching method, system, terminal and storage medium |
| CN111311470B (en) * | 2020-02-20 | 2023-04-07 | 苏州颢裕智能科技有限公司 | Mail package safety early warning monitoring method |
| CN111323835B (en) * | 2020-03-20 | 2022-09-16 | 安徽启新明智科技有限公司 | Security inspection method and device based on personal bag association |
| CN111382725B (en) * | 2020-04-01 | 2023-04-07 | 上海东普信息科技有限公司 | Method, device, equipment and storage medium for processing illegal express packages |
| CN111985497B (en) * | 2020-07-21 | 2023-09-26 | 国网山东省电力公司电力科学研究院 | Crane operation identification method and system under overhead transmission line |
| CN111861330A (en) * | 2020-07-22 | 2020-10-30 | 南京奥拓电子科技有限公司 | Contraband identification and security inspection method and express delivery cabinet device |
| CN112069907A (en) * | 2020-08-11 | 2020-12-11 | 盛视科技股份有限公司 | X-ray machine image recognition method, device and system based on example segmentation |
| CN112362656B (en) * | 2020-09-29 | 2024-01-30 | 江西鹭鹭行科技有限公司 | Pre-security inspection method, system, readable storage medium and pre-security inspection equipment |
| CN112954003B (en) * | 2021-01-25 | 2023-10-13 | 鼎电智能科技(江苏)有限公司 | Intelligent security inspection forbidden article identification system |
| CN113139950B (en) * | 2021-05-08 | 2024-04-16 | 佳都科技集团股份有限公司 | Target object identification method and device |
| CN113570543B (en) * | 2021-05-27 | 2024-09-20 | 浙江大华技术股份有限公司 | Security check package identification method, system, storage medium and equipment |
| CN113281814A (en) * | 2021-06-23 | 2021-08-20 | 重庆世纪科怡科技股份有限公司 | Security inspection device based on image recognition and security inspection method thereof |
| CN113554598A (en) * | 2021-06-24 | 2021-10-26 | 浙江大华技术股份有限公司 | Article detection method, article detection device, electronic equipment and computer-readable storage medium |
| CN113657493B (en) * | 2021-08-17 | 2024-06-25 | 北京理工大学 | X-ray security inspection image contraband detection method based on attention of style characteristic channel |
| CN113706497B (en) * | 2021-08-24 | 2024-04-09 | 南京金盾公共安全技术研究院有限公司 | Intelligent contraband identification device and system |
| CN113822341B (en) * | 2021-08-31 | 2023-06-20 | 成都智元汇信息技术股份有限公司 | Graph judging client of subway security check centralized graph judging system and working method thereof |
| CN113971443A (en) * | 2021-10-29 | 2022-01-25 | 成都智元汇信息技术股份有限公司 | Image processing method, device and system for intelligent image judgment |
| CN114332030B (en) * | 2021-12-30 | 2024-09-06 | 华中科技大学 | Valve body welding seam X-ray image denoising method, system and device |
| CN114459971B (en) * | 2022-01-12 | 2024-07-26 | 武汉怡特环保科技有限公司 | Online operation and maintenance method and system for particulate matter tester by beta-ray method |
| CN114660097B (en) * | 2022-03-23 | 2023-06-02 | 成都智元汇信息技术股份有限公司 | Synchronous correction method and system based on double sources and double visual angles |
| CN114898131B (en) * | 2022-06-17 | 2024-07-12 | 北京师范大学 | Method and system for detecting forbidden articles based on natural guidance and data enhancement |
| CN115718888B (en) * | 2022-11-29 | 2023-06-23 | 同方威视技术股份有限公司 | Detection method, detection device, electronic equipment and medium for trace substance |
| CN118135347A (en) * | 2022-12-01 | 2024-06-04 | 同方威视技术股份有限公司 | Online training method, security inspection image recognition method, device, equipment and medium |
| CN117495861B (en) * | 2024-01-02 | 2024-06-14 | 同方威视科技江苏有限公司 | Security check image checking method and device |
| CN118365990B (en) * | 2024-06-19 | 2024-08-30 | 浙江啄云智能科技有限公司 | Model training method and device applied to contraband detection and electronic equipment |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103308538A (en) * | 2013-06-17 | 2013-09-18 | 开平市中铝实业有限公司 | Security check system for bus |
| CN205210316U (en) * | 2015-11-18 | 2016-05-04 | 上海英迈吉东影图像设备有限公司 | Security check machine with energy -conserving function |
| CN106198580A (en) * | 2016-08-26 | 2016-12-07 | 合肥丁点网络科技有限责任公司 | A kind of X-ray screening machine luggage dangerous materials fast automatic detecting alarm device and method |
| 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 |
| US20170337467A1 (en) * | 2016-05-18 | 2017-11-23 | Nec Laboratories America, Inc. | Security system using a convolutional neural network with pruned filters |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103744120A (en) * | 2013-12-30 | 2014-04-23 | 中云智慧（北京）科技有限公司 | Method and device for assisting identification of contraband |
| CN106485268B (en) * | 2016-09-27 | 2020-01-21 | 东软集团股份有限公司 | Image identification method and device |
| CN106886054A (en) * | 2017-04-13 | 2017-06-23 | 西安邮电大学 | Dangerous material automatic identification equipment and method based on 3 D X-ray imaging |
| CN107871122A (en) * | 2017-11-14 | 2018-04-03 | 深圳码隆科技有限公司 | Safety check detection method, device, system and electronic equipment |
-
2017
- 2017-11-14 CN CN201711126618.2A patent/CN107871122A/en active Pending
-
2018
- 2018-11-14 WO PCT/CN2018/115498 patent/WO2019096181A1/en active Application Filing
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103308538A (en) * | 2013-06-17 | 2013-09-18 | 开平市中铝实业有限公司 | Security check system for bus |
| CN205210316U (en) * | 2015-11-18 | 2016-05-04 | 上海英迈吉东影图像设备有限公司 | Security check machine with energy -conserving function |
| US20170337467A1 (en) * | 2016-05-18 | 2017-11-23 | Nec Laboratories America, Inc. | Security system using a convolutional neural network with pruned filters |
| 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 |
| CN106198580A (en) * | 2016-08-26 | 2016-12-07 | 合肥丁点网络科技有限责任公司 | A kind of X-ray screening machine luggage dangerous materials fast automatic detecting alarm device and method |
Non-Patent Citations (2)
| Title |
|---|
| 李莲 等: "基于卷积神经网络的辣椒识别", 《天津理工大学学报》 * |
| 顾月清 等: "《 全国普通高等院校生物医学工程规划教材 生物医学工程技术》", 30 June 2017 * |
Cited By (48)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2019096181A1 (en) * | 2017-11-14 | 2019-05-23 | 深圳码隆科技有限公司 | Detection method, apparatus and system for security inspection, and electronic device |
| CN108519625A (en) * | 2018-04-08 | 2018-09-11 | 安徽理工大学 | Recognizable prohibited items safe examination system based on ray technology and deep learning |
| CN109001833A (en) * | 2018-06-22 | 2018-12-14 | 天和防务技术（北京）有限公司 | A kind of Terahertz hazardous material detection method based on deep learning |
| CN108830276A (en) * | 2018-07-02 | 2018-11-16 | 合肥格泉智能科技有限公司 | A kind of intelligent identifying system based on X-ray machine image |
| CN110261923A (en) * | 2018-08-02 | 2019-09-20 | 浙江大华技术股份有限公司 | A kind of contraband detecting method and device |
| CN110261923B (en) * | 2018-08-02 | 2024-04-26 | 浙江大华技术股份有限公司 | Contraband detection method and device |
| CN109101937A (en) * | 2018-08-22 | 2018-12-28 | 海尔优家智能科技（北京）有限公司 | Item identification method, device, cloud server, intelligent appliance equipment and system |
| CN109086740A (en) * | 2018-08-25 | 2018-12-25 | 李叶 | Stagewise ferry safety check mechanism |
| CN109086740B (en) * | 2018-08-25 | 2019-08-23 | 上海首安工程技术有限公司 | Stagewise ferry safety check mechanism |
| CN111103629A (en) * | 2018-10-25 | 2020-05-05 | 杭州海康威视数字技术股份有限公司 | Target detection method and device, NVR (network video recorder) equipment and security check system |
| CN109584339A (en) * | 2018-12-04 | 2019-04-05 | 中国民航大学 | A kind of safety check contraband x-ray image generation method based on production confrontation network |
| CN109884721A (en) * | 2018-12-10 | 2019-06-14 | 深圳极视角科技有限公司 | Safety check prohibited items detection method, device and electronic equipment based on artificial intelligence |
| CN111435119A (en) * | 2019-01-11 | 2020-07-21 | 合肥美亚光电技术股份有限公司 | X-ray foreign matter detection system and method based on block chain and computer equipment |
| CN109975332A (en) * | 2019-02-25 | 2019-07-05 | 北京声迅电子股份有限公司 | A kind of intelligent quality testing system applied to X-ray machine |
| CN109978827A (en) * | 2019-02-25 | 2019-07-05 | 平安科技（深圳）有限公司 | Violated object recognition methods, device, equipment and storage medium based on artificial intelligence |
| WO2020173021A1 (en) * | 2019-02-25 | 2020-09-03 | 平安科技（深圳）有限公司 | Artificial intelligence-based forbidden object identification method, apparatus and device, and storage medium |
| CN109946746A (en) * | 2019-03-21 | 2019-06-28 | 长安大学 | A kind of safe examination system and method based on deep neural network |
| WO2020187077A1 (en) * | 2019-03-21 | 2020-09-24 | 长安大学 | Deep neural network-based security check system and method |
| 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 |
| CN109948565B (en) * | 2019-03-26 | 2021-05-25 | 浙江啄云智能科技有限公司 | Method for detecting contraband in postal industry without opening box |
| CN110059618A (en) * | 2019-04-17 | 2019-07-26 | 北京易达图灵科技有限公司 | A kind of recognition methods of prohibited items and device |
| CN110163191B (en) * | 2019-06-17 | 2021-04-02 | 北京航星机器制造有限公司 | Intelligent identification method and system for dangerous goods and safety inspection system for dangerous goods |
| CN110163191A (en) * | 2019-06-17 | 2019-08-23 | 北京航星机器制造有限公司 | A kind of dangerous material intelligent identification Method, system and dangerous material safe examination system |
| CN110286415A (en) * | 2019-07-12 | 2019-09-27 | 广东工业大学 | Safety check contraband detecting method, apparatus, equipment and computer readable storage medium |
| CN110378912A (en) * | 2019-07-12 | 2019-10-25 | 深圳码隆科技有限公司 | Wrap up detection method, device, computer readable storage medium and computer equipment |
| CN110378912B (en) * | 2019-07-12 | 2021-11-16 | 深圳码隆科技有限公司 | Package detection method and device, computer readable storage medium and computer equipment |
| CN110794466A (en) * | 2019-07-16 | 2020-02-14 | 中云智慧（北京）科技有限公司 | X-ray machine picture acquisition auxiliary device and processing method |
| CN110580710A (en) * | 2019-08-21 | 2019-12-17 | 深圳码隆科技有限公司 | object tracking method, device, computer readable storage medium and computer equipment |
| CN110738096A (en) * | 2019-08-23 | 2020-01-31 | 银河水滴科技（北京）有限公司 | intelligent security inspection method, equipment and computer readable storage medium |
| CN110543857A (en) * | 2019-09-05 | 2019-12-06 | 安徽启新明智科技有限公司 | Contraband identification method, device and system based on image analysis and storage medium |
| CN110781812A (en) * | 2019-10-24 | 2020-02-11 | 谷琛 | Method for automatically identifying target object by security check instrument based on machine learning |
| CN110850493A (en) * | 2019-10-24 | 2020-02-28 | 武汉艾崴科技有限公司 | Linear array high-speed security inspection machine for visual image judgment of scanning imaging machine |
| CN111126447A (en) * | 2019-11-29 | 2020-05-08 | 中国船舶重工集团公司第七一三研究所 | Intelligent passenger security check luggage image automatic identification method |
| CN111126238B (en) * | 2019-12-19 | 2023-06-20 | 华南理工大学 | X-ray security inspection system and method based on convolutional neural network |
| CN111126238A (en) * | 2019-12-19 | 2020-05-08 | 华南理工大学 | X-ray security inspection system and method based on convolutional neural network |
| CN111167733A (en) * | 2020-01-14 | 2020-05-19 | 东莞理工学院 | Automatic visual defect detection system |
| CN111901577A (en) * | 2020-01-15 | 2020-11-06 | 吴庆芹 | Instant projection system based on signal analysis and corresponding terminal |
| CN111310635B (en) * | 2020-02-10 | 2024-04-19 | 上海应用技术大学 | TensorFlow-based security inspection contraband identification system and TensorFlow-based security inspection contraband identification method |
| CN111310635A (en) * | 2020-02-10 | 2020-06-19 | 上海应用技术大学 | Security inspection contraband identification system and method based on TensorFlow |
| CN113640321A (en) * | 2020-05-11 | 2021-11-12 | 同方威视技术股份有限公司 | Security check delay optimization method and device |
| CN113640321B (en) * | 2020-05-11 | 2024-04-02 | 同方威视技术股份有限公司 | Security inspection delay optimization method and equipment |
| CN111665572A (en) * | 2020-05-30 | 2020-09-15 | 中国船舶重工集团公司第七一三研究所 | Airport passenger security inspection intelligent auxiliary system and method based on X-ray machine image |
| CN113960687A (en) * | 2020-07-03 | 2022-01-21 | 株式会社日立解决方案 | X-ray image processing device and X-ray image processing method |
| CN113436151A (en) * | 2021-06-07 | 2021-09-24 | 中国铁道科学研究院集团有限公司电子计算技术研究所 | Security check method and device, electronic equipment and storage medium |
| CN117409199A (en) * | 2023-10-19 | 2024-01-16 | 中南大学 | Growing intelligent security inspection system and method based on cloud big data technology |
| CN117409199B (en) * | 2023-10-19 | 2024-05-14 | 中南大学 | Growing intelligent security inspection system and method based on cloud big data technology |
| CN117422881A (en) * | 2023-11-15 | 2024-01-19 | 山东衡昊信息技术有限公司 | Intelligent centralized security check graph judging system |
| CN117422881B (en) * | 2023-11-15 | 2024-09-24 | 山东衡昊信息技术有限公司 | Intelligent centralized security check graph judging system |
Also Published As
| Publication number | Publication date |
|---|---|
| WO2019096181A1 (en) | 2019-05-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107871122A (en) | Safety check detection method, device, system and electronic equipment | |
| CN108364017B (en) | A kind of picture quality classification method, system and terminal device | |
| CN105574513B (en) | Character detecting method and device | |
| CN104636707B (en) | The method of automatic detection cigarette | |
| US20180196158A1 (en) | Inspection devices and methods for detecting a firearm | |
| CN110543857A (en) | Contraband identification method, device and system based on image analysis and storage medium | |
| CN101281597B (en) | Apparatus and method for on-line real time capturing and recognizing product package pattern identification information | |
| CN107871125A (en) | Architecture against regulations recognition methods, device and electronic equipment | |
| CN107967475A (en) | A kind of method for recognizing verification code based on window sliding and convolutional neural networks | |
| CN103049763A (en) | Context-constraint-based target identification method | |
| CN110349167A (en) | A kind of image instance dividing method and device | |
| CN113841161A (en) | Extensible architecture for automatically generating content distribution images | |
| CN105261030A (en) | Method and device for detecting flame from infrared video | |
| CN115601682A (en) | Method and device for detecting foreign matters of underground belt conveyor | |
| CN108108703A (en) | Deceleration strip missing detection method, device and electronic equipment | |
| CN110287888A (en) | A kind of TV station symbol recognition method and system | |
| JP2024529423A (en) | Security inspection CT object recognition method and device | |
| CN111444788A (en) | Behavior recognition method and device and computer storage medium | |
| CN106997590A (en) | A kind of image procossing and detecting system based on detection product performance | |
| CN114419006A (en) | Method and system for removing watermark of gray level video characters changing along with background | |
| CN206897873U (en) | A kind of image procossing and detecting system based on detection product performance | |
| CN111985269A (en) | Detection model construction method, detection device, server and medium | |
| CN113361657A (en) | Device and method for identifying box number of cigarette box, tobacco shred boxing equipment and industrial personal computer | |
| US20170352170A1 (en) | Nearsighted camera object detection | |
| CN104102911A (en) | Image processing for AOI (automated optical inspection)-based bullet appearance defect detection system |
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 | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20191104 Address after: 518081 Yantian Modern Industrial Service Center, 3018 Shayan Road, Shatoujiao Street, Yantian District, Shenzhen City, Guangdong Province, 33 floors Applicant after: SHENZHEN MALONG TECHNOLOGY CO., LTD. Applicant after: Shenzhen Malong artificial intelligence research center Address before: 518000, Shenzhen, Yantian District, Guangdong Province Industrial Street East International Creative port 2, 5 B Applicant before: SHENZHEN MALONG TECHNOLOGY CO., LTD. |
|
| TA01 | Transfer of patent application right | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20180403 |
|
| RJ01 | Rejection of invention patent application after publication |