CN109543582A - Human body foreign body detection method based on millimeter-wave image - Google Patents
Human body foreign body detection method based on millimeter-wave image Download PDFInfo
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- 238000001514 detection method Methods 0.000 title claims abstract description 25
- 238000011946 reduction process Methods 0.000 claims abstract description 16
- 238000003384 imaging method Methods 0.000 claims abstract description 13
- 238000001228 spectrum Methods 0.000 claims description 30
- 239000000284 extract Substances 0.000 claims description 9
- 230000000877 morphologic effect Effects 0.000 claims description 9
- 230000007547 defect Effects 0.000 claims description 5
- 230000015572 biosynthetic process Effects 0.000 claims description 3
- 230000009286 beneficial effect Effects 0.000 abstract description 2
- 238000000034 method Methods 0.000 description 8
- 238000010586 diagram Methods 0.000 description 5
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- 230000011218 segmentation Effects 0.000 description 3
- 230000009466 transformation Effects 0.000 description 3
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Classifications
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
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- G06T5/70—
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/136—Segmentation; Edge detection involving thresholding
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/155—Segmentation; Edge detection involving morphological operators
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/187—Segmentation; Edge detection involving region growing; involving region merging; involving connected component labelling
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/107—Static hand or arm
- G06V40/11—Hand-related biometrics; Hand pose recognition
-
- 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
- G06T2207/20021—Dividing image into blocks, subimages or windows
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- 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
- G06T2207/20036—Morphological image processing
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- 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
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30196—Human being; Person
Abstract
The human body foreign body detection method based on millimeter-wave image that the invention discloses a kind of comprising the steps of: human body image is acquired by millimeter wave imaging system；Identify the head zone and hand region of collected human body image；Adjust the gray value of the head zone and hand region that recognize；To treated, human body image carries out noise reduction process；The classification of human body image after identifying processing.The invention has the beneficial effects that the human body foreign body detection method based on millimeter-wave image provided passes through the head zone and hand region for identifying collected human body image in advance, and the speed and accuracy that identification is improved to reduce the difficulty of subsequent image recognition are pre-processed to the head and hand region that recognize.
Description
Technical field
The human body foreign body detection method based on millimeter-wave image that the present invention relates to a kind of.
Background technique
Millimeter wave imaging security inspection device generally carries danger for detecting whether personnel to be tested's body carries dangerous goods
The personnel of product can't be held in dangerous goods on hand, or be hidden in the position that shoulder or more is easy to be observed, practical operation
In, a possibility that hand and head zone conceal dangerous goods, is negligible, however, existing image-recognizing method is identified as
As all areas of image are to judge whether personnel to be detected carry dangerous goods, the operand of image recognition is increased.
Summary of the invention
To solve the deficiencies in the prior art, the present invention provides a kind of people based on millimeter-wave image to solve the above problems
Body foreign matter detecting method.
In order to achieve the above objectives, the present invention adopts the following technical scheme that:
A kind of human body foreign body detection method based on millimeter-wave image comprising the steps of: pass through millimeter wave imaging system
Acquire human body image；Identify the head zone and hand region of collected human body image；Adjust the head zone that recognizes and
The gray value of hand region；To treated, human body image carries out noise reduction process；The classification of human body image after identifying processing.
Further, the head zone and hand region for identifying collected human body image, specifically by image recognition
Algorithm is automatically positioned head zone and hand region in human body image；
Further, the head zone and hand region for identifying collected human body image, specifically by filming apparatus
It takes pictures to human body, the head zone and hand in the human body photo of device shooting is taken the photograph by image recognition algorithm automatic positioning
Region, by the human body photo navigated to head zone and hand region be mapped to the collected people of millimeter wave imaging system
To identify the head region and hand region on human body image on body image.
Further, the gray value of the head zone and hand region that recognize is adjusted, specially by collected human body
The head zone of image and the gray value of hand region are set to 0.
Further, noise reduction process is carried out to treated human body image, comprising the following steps: to treated human figure
The bianry image of human body image is obtained as carrying out threshold division；Morphology behaviour is carried out to the bianry image after threshold division
Make；The biggish region of threshold value in treated bianry image is substituted for the corresponding grey scale value of former collected human body image.
Further, to treated, human body image progress threshold division obtains the bianry image of image, specially will
The gray value in the region that the gray value in treated image is less than preset threshold is set to 255, will be greater than the region of preset threshold
Gray value set 0, so as to carry out threshold division to obtain the bianry image of image being black white image to treated image.
Further, morphological operation is carried out to the bianry image after threshold division, is closed specifically by closed operation
Image defect, by opening the small and weak connection for operating and disconnecting between different connected regions, the zonule for keeping the formation of noise isolated, then
Noise is removed by setting area threshold.
Further, the classification of the human body image after identifying processing, specifically, the human body image after noise reduction process is divided
At multiple subgraphs；Extract each subgraph 2-d spectrum；Extract the feature vector of subgraph 2-d spectrum；To the spy extracted
Sign vector is classified.
Further, the feature vector for extracting subgraph 2-d spectrum, specifically, 2-d spectrum is divided into multiple fans
A characteristic constitutive characteristic vector is extracted in area, each sector.
Further, the feature vector of subgraph 2-d spectrum left-half is extracted.
The invention has the beneficial effects that the human body foreign body detection method based on millimeter-wave image provided by knowing in advance
The head zone and hand region of not collected human body image, and to the head and hand region that recognize pre-processed with
The difficulty for reducing subsequent image recognition improves the speed and accuracy of identification.
Detailed description of the invention
Fig. 1 is the figure of the millimeter wave imaging system acquisition of the human body foreign body detection method of the invention based on millimeter-wave image
The schematic diagram of picture；
Fig. 2 be the human body foreign body detection method of the invention based on millimeter-wave image to acquired image carry out identification and
The schematic diagram of gray proces；
Fig. 3 is that the human body foreign body detection method of the invention based on millimeter-wave image carries out thresholding to acquired image
Divide the schematic diagram of obtained bianry image；
Fig. 4 is that the human body foreign body detection method of the invention based on millimeter-wave image carries out morphological operation to bianry image
Treated schematic diagram；
The signal of image in Fig. 5 after the human body foreign body detection method noise reduction process of the invention based on millimeter-wave image
Figure；
Fig. 6 is the schematic diagram of the division 2-d spectrum of the human body foreign body detection method the present invention is based on millimeter-wave image.
Specific embodiment
Specific introduce is made to the present invention below in conjunction with the drawings and specific embodiments.
As shown in Figures 1 to 4, a kind of human body foreign body detection method based on millimeter-wave image comprising the steps of: S1 is logical
Cross millimeter wave imaging system acquisition human body image；S2 identifies the head zone and hand region of collected human body image；S3 tune
The gray value of the whole head zone recognized and hand region；To treated, human body image carries out noise reduction process to S4；S5 identification
The classification of treated human body image.
Human body image is acquired by millimeter wave imaging system for step S1:
A large amount of human body target image is acquired using millimeter wave imaging system, as shown in Figure 1.
The head zone and hand region of collected human body image are identified for step S2:
In embodiments of the present invention, the head zone and hand region for identifying collected human body image, specifically by
Image recognition algorithm is automatically positioned head zone and hand region in human body image, as shown in Figure 2.
Since the image that millimeter wave imaging system directly acquires compares the photograph directly acquired by filming apparatus (such as CCD)
Human body head and hand important information are lost in sector-meeting, are unfavorable for image recognition algorithm identification head and hand information, as one kind
Preferred mode can also carry out imaging of taking pictures to human body by CCD, be identified in photo by image recognition algorithm first
Head and hand region, then the head identified in the photo positioned and hand region are mapped to millimeter wave imaging system acquisition
To human body image on, so can be improved recognizer identification speed and accuracy.
For the gray value of head zone and hand region that step S3 adjustment recognizes；
After recognizing the head zone and hand region in human body image, the gray value in the region is adjusted, below for cooperation
Noise reduction process in step in this step sets the gray value of the head zone of collected human body image and hand region
It is 0.
For step S4, to treated, human body image carries out noise reduction process:
In order to which subsequent image enhancement and feature extraction, automatic target detection can obtain good effect, need to pass through figure
Image intensifying algorithm removes noise, improving image quality.The main target of denoising is removal ambient noise, and is kept to the maximum extent
Human body image it is complete.Noise reduction process is carried out to by the step S3 image that treated, comprising the following steps: to treated
Image carries out threshold division and obtains the bianry image of image, carries out morphological operation to the bianry image after threshold division,
The biggish region of gray value in treated bianry image is substituted for the gray value of the corresponding region of former acquired image.
Threshold segmentation method is common image partition method, is all compared in foreground object and background object grey value profile
Good effect can be obtained when uniformly.In order to which good segmentation effect can be obtained to different terahertz images, the present invention
Embodiment uses soft-threshold, and soft-threshold is obtained by OTSU method, to realize global optimum.
Threshold division is carried out to treated image and obtains the bianry image of image, it specially will be in treated image
Gray value be less than the gray value in region of preset threshold and be set to 255, the gray value that will be greater than the region of preset threshold sets 0, with
Make to carry out threshold division to treated image to obtain the bianry image black white image of image, as shown in Figure 2.
Bianry image is obtained after Threshold segmentation.Due to, there is also the pixel of high gray value, leading to background in background pixel
Noise cannot completely remove, and there is also the pixels of low ash angle value in human body image, and human body image is caused defect occur, therefore need
Noise is further removed by Morphological scale-space and fills up human body defect part.
Morphological operation is carried out to the bianry image after threshold division, is closed image defect specifically by closed operation,
By opening the small and weak connection for operating and disconnecting between different connected regions, the zonule for keeping the formation of noise isolated, then pass through setting
Area threshold removes noise.
Since ambient noise is more complex, in actual items realization, in order to reach optimal denoising effect, need by not
Disconnected to attempt different structural elements, the morphological operations such as open and close, burn into expansion can also carry out repeatedly.
In embodiments of the present invention, collected is human body image, for the feature that two leg of human body is elongated, morphology
Operation can carry out the upper part of the body with the lower part of the body respectively, wherein using square structure element above the waist, the lower part of the body is using rectangular
Shape structural element, after morphological operation processing as shown in Figure 3.
The biggish region of gray value in treated bianry image is substituted for the corresponding region of former acquired image
Gray value, in the embodiment of the present invention, specifically, the white area that the gray value in treated bianry image is 255 is replaced
Change the gray value of the corresponding region of former acquired image into.
In order to allow the image after denoising to seem more natural, first former noisy image degree of comparing is stretched, restores ash
Angle value.Contrast stretching is carried out by top cap transformation and the transformation of bottom cap:
Im'=im+tophat (im, se)-bothat (im, se),
Wherein, se indicates structural element, and the structural element used here is the square of side length 60.Last denoising result
As shown in Figure 4.
For the classification of the human body image after step S5 identifying processing:
The classification of human body image after identifying processing, specifically, S51 the human body image after noise reduction process is divided into it is multiple
Subgraph, S51 extract each subgraph 2-d spectrum, and S53 extracts the feature vector of subgraph 2-d spectrum, and S54 is to extracting
Feature vector classify.
Multiple subgraphs are divided by the image after noise reduction process for step S51:
Since target only occupies a small region of entire image, multiple subgraphs can be divided an image into, respectively
Judge in every subgraph with the presence or absence of target.The difficulty on the one hand reducing identification in this way, on the other hand can be realized to target
Positioning.
Each subgraph 2-d spectrum is extracted for step S52:
For every subgraph, its 2-d spectrum can be extracted as knowledge another characteristic.Natural human body surface is that variation is flat
Slow, corresponding 2-d spectrum amplitude will focus on low frequency part, and amplitude approximation obeys 1/f distribution；When human body surface exists
When other objects, corresponding gray level image will appear gray value jump, and the distribution of corresponding 2-d spectrum will also change.
The feature vector of subgraph 2-d spectrum is extracted for step S53:
The feature vector of 2-d spectrum is extracted specifically, 2-d spectrum is divided into multiple sectors, each sector extracts one
A characteristic constitutive characteristic vector, so processing can reduce the dimension of feature vector.In embodiments of the present invention, by two-dimentional frequency
Spectrum is divided into 8 rings, and each 45 ° of subtended angle of sector, then the corresponding feature vector of 2-d spectrum only has 64 dimensions.What this sector divided
Method can embody the difference of high and low frequency and embody the difference on frequency spectrum different directions, therefore can be well reflected
The distribution character of frequency spectrum, and the extracting method of this spectrum signature has the advantages that operand is small, fireballing.
There is centrosymmetric property in view of 2-d spectrum, therefore only need to take the half Partial Feature of 2-d spectrum
Reflect the feature of complete 2-d spectrum, therefore, the left-half of 2-d spectrum can be chosen, then the corresponding spy of 2-d spectrum
Sign vector only has 32 dimensions.
Classify for step S54 to the feature vector extracted:
Classify to the feature vector extracted, specially automatic target knows the feature vector extracted, and one kind is 1,
Indicate that target, one kind are 0, expression there is not target.The embodiment of the present invention is quasi- to carry out Classification and Identification with by network, by reversely missing
Poor Law of Communication is come the multi-layer perception (MLP) trained.Usually, identification image data needs 10 to 20 layers of hidden layer.Output uses
Sigmoid function, acquiring each subgraph, there are the probability of target.
In collected somatic data, there are the sub-fraction that the subgraph of target only accounts for whole subgraphs, a usual target
The subgraph number occupied is no more than 4.Generally speaking, the subgraph only about no more than 10% can be used as neural network point
The training and identification of class device.In order to keep training result more reliable, be both not biased towards target as a result, or be not biased towards the knot for not having target
Fruit, training in have target subgraph number and do not have the subgraph number of target should be equal.Therefore, in order to enough available subgraphs
Number, the human body picture of acquisition should be enough.
For the present invention program, also can choose first to acquired image carry out noise reduction process after again to noise reduction process after
Image carry out head and hand region identification and gray scale adjustment.
The basic principles, main features and advantages of the invention have been shown and described above.The technical staff of the industry should
Understand, the above embodiments do not limit the invention in any form, all obtained by the way of equivalent substitution or equivalent transformation
Technical solution is fallen within the scope of protection of the present invention.
Claims (10)
1. a kind of human body foreign body detection method based on millimeter-wave image, it is characterised in that comprise the steps of:
Human body image is acquired by millimeter wave imaging system；Identify the head zone and hand region of collected human body image；
Adjust the gray value of the head zone and hand region that recognize；To treated, human body image carries out noise reduction process；At identification
The classification of human body image after reason.
2. the human body foreign body detection method according to claim 1 based on millimeter-wave image, which is characterized in that
The head zone and hand region of the collected human body image of identification, it is automatically fixed specifically by image recognition algorithm
Head zone and hand region in the human body image of position.
3. the human body foreign body detection method according to claim 1 based on millimeter-wave image, which is characterized in that
The head zone and hand region of the collected human body image of identification carry out human body specifically by filming apparatus
It takes pictures, head zone and hand region in the human body photo of device shooting is taken the photograph by image recognition algorithm automatic positioning, by institute
The head zone in human body photo and hand region navigated to is mapped on the collected human body image of millimeter wave imaging system
To identify the head region and hand region on human body image.
4. the human body foreign body detection method according to claim 1 based on millimeter-wave image, which is characterized in that
The gray value for adjusting the head zone and hand region that recognize, specially by the head of collected human body image
The gray value of region and hand region is set to 0.
5. the human body foreign body detection method according to claim 1 based on millimeter-wave image, which is characterized in that
Described to treated, human body image carries out noise reduction process, comprising the following steps: to treated, human body image carries out threshold
The bianry image for obtaining human body image is divided in value；Morphological operation is carried out to the bianry image after threshold division；It will processing
The biggish region of the threshold value in bianry image afterwards is substituted for the corresponding grey scale value of former collected human body image.
6. the human body foreign body detection method according to claim 5 based on millimeter-wave image, which is characterized in that
It is described threshold division is carried out to treated human body image to obtain the bianry image of image, it specially will treated figure
The gray value in the region that the gray value as in is less than preset threshold is set to 255, and the gray value that will be greater than the region of preset threshold is set
0, so that image carries out threshold division to obtain the bianry image of image being black white image to treated.
7. the human body foreign body detection method according to claim 5 based on millimeter-wave image, which is characterized in that
The bianry image to after threshold division carries out morphological operation, is closed image defect specifically by closed operation,
By opening the small and weak connection for operating and disconnecting between different connected regions, the zonule for keeping the formation of noise isolated, then pass through setting
Area threshold removes noise.
8. the human body foreign body detection method according to claim 1 based on millimeter-wave image, which is characterized in that
The classification of human body image after the identifying processing, specifically, the human body image after noise reduction process is divided into multiple sons
Image；Extract each subgraph 2-d spectrum；Extract the feature vector of subgraph 2-d spectrum；To the feature vector extracted into
Row classification.
9. the human body foreign body detection method according to claim 1 based on millimeter-wave image, which is characterized in that
The feature vector for extracting subgraph 2-d spectrum, specifically, 2-d spectrum is divided into multiple sectors, each sector
Extract a characteristic constitutive characteristic vector.
10. the human body foreign body detection method according to claim 1 based on millimeter-wave image, which is characterized in that
Extract the feature vector of subgraph 2-d spectrum left-half.
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| CN201911116812.1A CN111476074A (en) | 2018-11-15 | 2019-11-15 | Human body foreign matter detection method based on millimeter wave image |
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