CN107085964A - Vehicular automatic driving system based on image enhaucament - Google Patents
Vehicular automatic driving system based on image enhaucament Download PDFInfo
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- 230000002708 enhancing effect Effects 0.000 claims abstract description 32
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- 238000006243 chemical reaction Methods 0.000 claims 1
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Classifications
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
- G08G1/096708—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control
- G08G1/096725—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control where the received information generates an automatic action on the vehicle control
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/90—Dynamic range modification of images or parts thereof
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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
- G06T2207/10—Image acquisition modality
- G06T2207/10024—Color image
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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
- G06T2207/30248—Vehicle exterior or interior
- G06T2207/30252—Vehicle exterior; Vicinity of vehicle
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- Traffic Control Systems (AREA)
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Abstract
The invention provides a kind of Vehicular automatic driving system based on image enhaucament, including traffic camera mixed-media network modules mixed-media, central processing module, wireless transport module and vehicle, the traffic camera module is used for the road conditions image and accident image for obtaining position；The central processing module is used to the road conditions image and accident image carrying out image enhancement processing, obtains road conditions image and accident image and corresponding positional information and temporal information after enhancing processing；The wireless transport module is used for the road conditions image after the enhancing is handled and sent with accident image and corresponding positional information with temporal information to vehicle.The present invention carries out enhancing processing using the mode of software to road conditions image and accident image, obtain high-quality road conditions image and accident image, do not need expensive hardware to obtain high-definition image, the cost for obtaining high quality graphic is reduced, so as to reduce the cost of whole system.
Description
Technical field
The present invention relates to automatic Pilot field, and in particular to a kind of Vehicular automatic driving system based on image enhaucament.
Background technology
The automatic driving mode system of vehicle in correlation technique generally obtains road conditions letter using fixed high-definition camera
Breath, although result in the road conditions image of high definition, but utilize the cost of high-definition camera high, and maintenance cost is expensive.Also
There is the automatic driving mode system for the vehicle that traffic information is obtained using vehicle-mounted camera, but its shortcoming is fairly obvious, exactly obtains
Win the confidence breath distance it is extremely limited, it is impossible to accomplish remote detection, the reaction speed of driver influenceed to a certain extent.
The content of the invention
In view of the above-mentioned problems, a kind of the present invention is intended to provide Vehicular automatic driving system based on image enhaucament.
The purpose of the present invention is realized using following technical scheme：
A kind of Vehicular automatic driving system based on image enhaucament, including traffic camera mixed-media network modules mixed-media, center processing mould
Block, wireless transport module and vehicle, the traffic camera module are used for the road conditions image and accident image for obtaining position；
The central processing module is used to the road conditions image and accident image carrying out image enhancement processing, obtains after enhancing processing
Road conditions image and accident image and corresponding positional information and temporal information；The wireless transport module is used at the enhancing
Road conditions image after reason is sent to vehicle with accident image and corresponding positional information with temporal information.
Beneficial effects of the present invention are：The present invention is carried out at enhancing using the mode of software to road conditions image and accident image
Reason, obtains high-quality road conditions image and accident image, it is not necessary to which expensive hardware reduces to obtain high-definition image and obtains high-quality
The cost of spirogram picture, so as to reduce the cost of whole system.
Brief description of the drawings
Using accompanying drawing, the invention will be further described, but the embodiment in accompanying drawing does not constitute any limit to the present invention
System, for one of ordinary skill in the art, on the premise of not paying creative work, can also be obtained according to the following drawings
Other accompanying drawings.
Fig. 1 is the frame construction drawing of the present invention；
Fig. 2 is the frame construction drawing of the central processing module of the present invention.
Reference：
Traffic camera mixed-media network modules mixed-media 1, central processing module 2, wireless transport module 3, vehicle 4, image transformation submodule
21st, picture content processing submodule 22, picture content enhancing submodule 23 and image reconstruction submodule 24.
Embodiment
With reference to following application scenarios, the invention will be further described.
Referring to Fig. 1, a kind of Vehicular automatic driving system based on image enhaucament of the present embodiment, including traffic camera net
Network module 1, central processing module 2, wireless transport module 3 and vehicle 4, the traffic camera module 1 are used to obtain institute in place
The road conditions image and accident image put；The central processing module 2 is used to the road conditions image and accident image carrying out image
Enhancing is handled, and obtains road conditions image and accident image and corresponding positional information and temporal information after enhancing processing；The nothing
Line transport module 3 is used for the road conditions image after the enhancing is handled to be believed with accident image and corresponding positional information with the time
Breath is sent to vehicle 4.
Preferably, the traffic camera mixed-media network modules mixed-media includes the camera for being distributed in different streets, and the camera is all
Using standard POE cameras.
Preferably, the vehicle includes positioner and display screen, and the positioner is used for the position letter for obtaining vehicle
Breath；The display screen be used to showing vehicle location nearby road conditions image and accident image and the corresponding positional information in street and when
Between information.
The above embodiment of the present invention, carries out enhancing processing to road conditions image and accident image using the mode of software, obtains
High-quality road conditions image and accident image, it is not necessary to which expensive hardware obtains high-definition image, reduces acquisition high quality graphic
Cost, so as to reduce the cost of whole system.
Preferably, referring to Fig. 2, the central processing module include image transformation submodule, picture content processing submodule,
Picture content strengthens submodule and image reconstruction submodule；
Described image transformation submodule be used for by the traffic camera module obtain position road conditions image with
Accident image is transformed into reference to space X YZ spaces from RGB color, then by the road conditions image and accident image in XYZ space
Reconvert obtains road conditions image with accident image in three kinds of components of La*b* color spaces, is respectively to La*b* color spaces
Luminance component, a* components and b* components, be specially：
In formula, R, G and B be the value of road conditions image and accident image in RGB color on tri- passages of R, G and B, X,
Y and Z is road conditions image and value of the accident image in XYZ space on tri- passages of X, Y and Z, Xw, Yw, ZwFor the ginseng of XYZ space
Examine white point value, Xw=0.950456, Yw=1.000000, Zw=1.088754, L are luminance component, L ∈ [0,100], a*To be red
The difference of green colored pixel value, a*∈ [- 120,120], b*For the difference of turquoise color pixel values, b*∈ [- 120,120], h (q) is about
Beam function, q is the variable of constraint function；
Described image component processing submodule be used for will after the processing of image transformation submodule obtained road conditions image with
The La*b* color spaces luminance component of accident image carries out horizontal gradient again and the component of vertical gradient is handled, and passes through brightness point
Measure to obtain gradient information, the first-order partial derivative on m and n is asked at position (m, n) to image pixel respectively, horizontal ladder is tried to achieve
Component and vertical gradient component are spent, then using gradient component information come the rate of change of the pixel value of detection image, image is carried out
Rim detection, be specially：
In formula, f (m, n) represents the detection function in position (m, n) place image pixel T (m, n), Sm(m, n) is horizontal ladder
Spend component, Sn(m, n) is vertical gradient component, and T (m, n) is image pixel.
The above embodiment of the present invention, two-dimensional gradient value is asked for using first-order partial derivative, is conducive to simplifying picture content processing
Gradient information of the submodule when the component for carrying out road conditions image and accident image is handled, reaches efficient process road conditions image and thing
Therefore luminance component of the image in La*b* color spaces, the expression that overcomes traditional gradient calculation method is complicated, operand is big and
The slow shortcoming of arithmetic speed, is that this Vehicular automatic driving system realization based on image enhaucament utilizes software to carry out efficient image side
Edge detects raising condition, while carrying out Image Edge-Detection using custom images edge indicator function, identifies road conditions figure
As changing obvious point with brightness in accident image, road conditions image and incoherent data volume in accident image is greatly reduced,
Remain the important architectural characteristic of image simultaneously.
Preferably, described image component enhancing submodule is used to described image component handling what is obtained after submodule processing
Horizontal gradient component carries out enhancing processing with vertical gradient component using self-defined tone mapping function, obtains after enhancing processing
Horizontal gradient component and vertical gradient component, the self-defined tone mapping function used for：
In formula, Sout(m, n, r) represents self-defined tone mapping function, Sin(m, n) is the gradient component of input, Sin(m,
n)∈(Sm(m,n),Sn(m, n)), m and n represent the horizontally and vertically coordinate of road conditions image and accident image respectively, and r schemes for input
The intensity level of picture, mminAnd mmaxThe maximum and minimum value of the abscissa of respectively defeated image pixel；
Enhanced horizontal gradient component and enhanced vertical gradient component are obtained after gradient information enhancing processing.
The above embodiment of the present invention, what is utilized is that self-defined tone mapping function is bright to road conditions image and accident image respectively
Spending the horizontal gradient component and vertical gradient component of component is strengthened, rather than directly luminance component is strengthened in itself,
Road conditions image and the weakening of contrast in accident image are avoided, keeps the details of road conditions image and accident image special well
Levy so that there is the low quality road conditions image that common camera is obtained to be highlighted with original sightless information in accident image in human eye
Details in visual range without destruction road conditions image and accident image.
Preferably, described image is rebuild submodule and is used for enhanced horizontal gradient component and enhanced vertical gradient
Component synthesizes enhanced image gradient using custom images gradient composite formula, and obtained enhanced image gradient is made
For road conditions image and accident image it is separated go out luminance component weight, realize brightness strengthen, obtain it is enhanced bright
Spend component, luminance component and two color component a after enhancing is handled*Component and b*Component combination is got up, reconstruction image, tool
Body is：
In formula, δ (m, n) represents image gradient angle changing, and S (m, n) represents enhanced image gradient, Sm' (m, n) be
Enhanced horizontal gradient component, Sn' (m, n) is enhanced vertical gradient component；
The enhancing image in La*b* spaces is obtained after reconstruction image, finally again turns this enhancing image from La*b* color spaces
RGB color is changed to, the enhancing image in RGB color is obtained, and exported.
The above embodiment of the present invention, is calculated by custom images gradient composite formula and obtains road conditions image and accident image
Gradient, and this gradient is realized into road conditions image and the accident in La*b* color spaces as the weight factor of luminance component
The enhancing of brightness of image, is conducive to simplified operation, the arithmetic speed of image reconstruction submodule is improved, while making obtained RGB face
Road conditions image in the colour space is shown that the effect that image is shown more meets human eye vision with hiding information in accident image.
Finally it should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention, rather than to present invention guarantor
The limitation of scope is protected, although being explained with reference to preferred embodiment to the present invention, one of ordinary skill in the art should
Work as understanding, technical scheme can be modified or equivalent substitution, without departing from the reality of technical solution of the present invention
Matter and scope.
Claims (6)
1. a kind of Vehicular automatic driving system based on image enhaucament, it is characterized in that, including traffic camera mixed-media network modules mixed-media, center
Processing module, wireless transport module and vehicle, the traffic camera module are used to obtain position road conditions image and accident
Image；The central processing module is used to the road conditions image and accident image carrying out image enhancement processing, obtains at enhancing
Road conditions image and accident image and corresponding positional information and temporal information after reason；The wireless transport module is used for will be described
Road conditions image after enhancing processing is sent to vehicle with accident image and corresponding positional information with temporal information.
2. a kind of Vehicular automatic driving system based on image enhaucament according to claim 1, it is characterized in that, the traffic
Camera mixed-media network modules mixed-media includes being distributed in the camera group in different streets, and the camera that the camera group is used is all standard
POE cameras.
3. a kind of Vehicular automatic driving system based on image enhaucament according to claim 1, it is characterized in that, the vehicle
Including positioner and display screen, the positioner is used for the positional information for obtaining vehicle；The display screen is used to show car
The road conditions image and accident image and corresponding positional information and temporal information in the neighbouring street in position.
4. a kind of Vehicular automatic driving system based on image enhaucament according to claim 1, it is characterized in that, the center
Processing module includes image transformation submodule, picture content processing submodule, picture content enhancing submodule and image reconstruction
Module；
Described image transformation submodule is used for the road conditions image and accident for the position for obtaining the traffic camera module
Image is transformed into reference to space X YZ spaces from RGB color, then the road conditions image in XYZ space is turned again with accident image
La*b* color spaces are changed to, road conditions image are obtained with accident image in three kinds of components of La*b* color spaces, respectively brightness
Component, a* components and b* components；
Described image component processing submodule is used for the road conditions image and accident that will be obtained after the processing of image transformation submodule
The component that the La*b* color spaces luminance component of image carries out horizontal gradient and vertical gradient again is handled, by luminance component come
Gradient information is obtained, the first-order partial derivative on m and n is asked at position (m, n) to image pixel T (m, n) respectively, level is tried to achieve
Gradient component and vertical gradient component, are then schemed using gradient component information come the rate of change of the pixel value of detection image
As rim detection, it is specially：
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In formula, f (m, n) represents the detection function in position (m, n) place image pixel T (m, n), Sm(m, n) is horizontal gradient point
Amount, Sn(m, n) is vertical gradient component, and T (m, n) is image pixel.
5. a kind of Vehicular automatic driving system based on image enhaucament according to claim 4, it is characterized in that, described image
Component enhancing submodule is used to described image component handling the horizontal gradient component obtained after submodule processing and vertical gradient
Component carries out enhancing processing using self-defined tone mapping function, obtains horizontal gradient component and vertical gradient after enhancing processing
Component, wherein the self-defined tone mapping function formula used for：
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In formula, Sout(m, n, r) represents self-defined tone mapping function, Sin(m, n) is the gradient component of input, Sin(m,n)∈
(Sm(m,n),Sn(m, n)), m and n represent the horizontally and vertically coordinate of road conditions image and accident image respectively, and r is input picture
Intensity level, mminAnd mmaxThe maximum and minimum value of the abscissa of respectively defeated image pixel；
Enhanced horizontal gradient component and enhanced vertical gradient component are obtained after gradient information enhancing processing.
6. a kind of Vehicular automatic driving system based on image enhaucament according to claim 5, it is characterized in that, described image
Rebuilding submodule is used to enhanced horizontal gradient component and enhanced vertical gradient component utilizing custom images gradient
Composite formula synthesizes enhanced image gradient, regard obtained enhanced image gradient as road conditions image and accident image institute
The weight for the luminance component isolated, realizes that brightness strengthens, and obtains enhanced luminance component, bright after enhancing is handled
Spend component and two color component a*Component and b*Component combination is got up, reconstruction image, is specially：
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<mo>,</mo>
<mi>n</mi>
<mo>)</mo>
</mrow>
<mo>-</mo>
<msup>
<msub>
<mi>S</mi>
<mi>n</mi>
</msub>
<mo>&prime;</mo>
</msup>
<mrow>
<mo>(</mo>
<mi>m</mi>
<mo>,</mo>
<mi>n</mi>
<mo>)</mo>
</mrow>
<mo>|</mo>
<mi>s</mi>
<mi>i</mi>
<mi>n</mi>
<mfrac>
<mrow>
<mi>&delta;</mi>
<mrow>
<mo>(</mo>
<mi>m</mi>
<mo>,</mo>
<mi>n</mi>
<mo>)</mo>
</mrow>
</mrow>
<mn>2</mn>
</mfrac>
</mrow>
</msqrt>
</mrow>
In formula, δ (m, n) represents image gradient angle changing, and S (m, n) represents enhanced image gradient, Sm' (m, n) is enhancing
Horizontal gradient component afterwards, Sn' (m, n) is enhanced vertical gradient component；
The enhancing image in La*b* spaces is obtained after reconstruction image, finally again by this enhancing image from La*b* color space conversions to
RGB color, obtains the enhancing image in RGB color, and exported.
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