CN107967445A - A kind of car installs the intelligent checking system and method for skylight additional - Google Patents
A kind of car installs the intelligent checking system and method for skylight additional Download PDFInfo
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- CN107967445A CN107967445A CN201710949529.1A CN201710949529A CN107967445A CN 107967445 A CN107967445 A CN 107967445A CN 201710949529 A CN201710949529 A CN 201710949529A CN 107967445 A CN107967445 A CN 107967445A
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- skylight
- vehicle
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- detection
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
- G06V20/58—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
- G06V20/584—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads of vehicle lights or traffic lights
-
- 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
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/22—Image preprocessing by selection of a specific region containing or referencing a pattern; Locating or processing of specific regions to guide the detection or recognition
Abstract
The invention discloses the intelligent checking system and method that a kind of car installs skylight additional, including module of target detection and judgment module, wherein, the module of target detection includes vehicle target detection unit, skylight object detection unit and skylight detection mark judging unit；The vehicle target detection unit obtains vehicle region image, the skylight object detection unit is detected vehicle region image and identifies skylight, archives picture is compared skylight detection mark judging unit with picture skylight to be detected detection mark, and the determination module carries out integrated judgment to the result of whole testing process.Present invention is mainly applied to car in automotive vehicle annual test to install skylight detection additional, realize the whole-process automatic verification in detection process, unsanctioned detection image and reason can be passed back to server at the same time preserve and remain to collect evidence, both save manpower, in turn ensure that the just, openly of verifying work.
Description
Technical field
The present invention relates to the artificial intelligence judgment technology field of automotive vehicle annual test, more particularly to a kind of car installs day additional
Window intelligent checking system and method.
Background technology
Constantly improve with living standards of the people with the continuous social and economic development, Urban vehicles poputation rapidly increases
It is long.Motor vehicle is as important traffic participant, it is necessary to possesses good safety and reliability.However, part motor vehicle is protected
The person's of having awareness of safety is thin, and refitted vehicles are carried out to motor vehicle.Vehicle after refitted vehicles, may without security test
Increase probability and the seriousness that traffic accident occurs.Therefore, strictly whether detection vehicle is reequiped for safeguarding that traffic safety is non-
It is often important.
Traditional vehicle installs skylight detection additional mainly by being accomplished manually, and this method cost of labor is higher, less efficient,
And repeated verification operation easily produces fatigue for a long time, the defective mode such as easy carelessness, influences to verify accuracy rate.
How accurately and rapidly skylight whether is installed additional to vehicle to verify, while avoid desk checking of high cost, it is easily tired
Labor, the easily drawback such as carelessness, are to continue with the technical problem solved.
The content of the invention
The purpose of the present invention is：It is proposed that a kind of car installs skylight intelligent checking system and method, automatic detection vehicle additional and is
No installation skylight, to meet nowadays the needs of to vehicle annual test work efficiency, accuracy rate.
The technical solution adopted by the present invention to solve the technical problems is：
A kind of car installs the intelligent checking system of skylight, including module of target detection and judgment module additional, wherein, the mesh
Marking detection module includes vehicle target detection unit, skylight object detection unit and skylight detection mark judging unit；The car
Object detection unit passes through vehicle target detection model and detects vehicle image, obtains vehicle region image, the skylight target
Detection unit is detected vehicle region image using skylight target detection model, and identifies skylight, the skylight detection
Archives picture is compared mark judging unit with picture skylight to be detected detection mark, and the determination module is to whole detection
The result of flow carries out integrated judgment, and feeds back unsanctioned reason and picture.
A kind of car installs the intelligent detecting method of skylight additional, includes the following steps：
S1, from server download vehicle pictures to be detected and map file picture；
S2, using based on deep learning network vehicle target detection model detect vehicle, judge vehicle pictures to be detected
Middle vehicle target whether there is, and is 0 if recording this mark in the presence of if, extracts vehicle region image；This is recorded if being not present
Bar mark is 1, and preserves picture concerned, into statistical analysis flow；
S3, using based on deep learning network vehicle target detection model detect vehicle, judge vehicle in archives picture
Target whether there is, and is 0 if recording this mark in the presence of if, extracts vehicle region image；Indicate if recording this there is no if
For 1, and picture concerned is preserved, into statistical analysis flow；
S4, the car extracted using the skylight target detection model inspection based on deep learning network from vehicle pictures to be checked
Area image, judges that skylight whether there is, and is 0 if recording this mark in the presence of if, extracts skylight area image；If it is not present
It is 1 then to record this mark；
S5, the vehicle area extracted using the skylight target detection model inspection based on deep learning network from archives picture
Area image, judges that skylight whether there is, and is 0 if recording this mark in the presence of if, extracts skylight area image；Remember if there is no if
It is 1 to record this mark；
S6, judge whether vehicle pictures skylight detection mark to be detected and archives picture skylight detection mark are consistent, if one
Cause, then it is 0 to record this mark；If inconsistent, judge whether archives picture skylight detection mark is 0, this is recorded if 0
Bar mark is 0, and if 1, then it is 1 to record this mark；
S7, the result of the action to whole process carry out statistical analysis, if it is 0 to input this module identification, detection passes through；If
The mark of this module is inputted there are mark 1, then is detected not by the way that while detection can be obtained not for 1 position occurred according to mark
Pass through reason and problem picture.
Further, the obtaining step of the vehicle target detection model is as follows：
S21, obtain different automobile types in different illumination conditions, the vehicle image of different angle shooting；
S22, using rectangle frame marked vehicle area image position；
S23, detect deep neural network model, acquisition vehicle detection mould using the vehicle region image training objective
Type.
Further, the obtaining step of the skylight target detection model is as follows：
S31, the vehicle image for obtaining the different automobile types equipped with skylight, vehicle body top skylight region need complete；
S32, interception vehicle region image；
S33, using rectangle frame mark skylight position；
S34, detect deep neural network model, acquisition skylight detection mould using the skylight area image training objective
Type.
The beneficial effects of the invention are as follows：Present invention is mainly applied to car in automotive vehicle annual test to install skylight detection additional,
The whole-process automatic verification in detection process is realized, while unsanctioned detection image and reason can be passed back to server preservation
Remain to collect evidence, both saved manpower, in turn ensure that the just, openly of verifying work.
Brief description of the drawings
Fig. 1 is the structure diagram of the intelligent checking system of the present invention.
Fig. 2 is the detection decision flow chart of the installation skylight of the present invention.
Fig. 3 is the structure diagram of vehicle target detection unit of the present invention.
Fig. 4 is the structure diagram of skylight object detection unit of the present invention.
Embodiment
Below in conjunction with attached drawing.The present invention will be further described.
The intelligent checking system structure of the present invention is as shown in Figure 1, including module of target detection and determination module.
Wherein, module of target detection includes：Vehicle target detection unit, skylight object detection unit and skylight detection mark
Judging unit；
Vehicle target detection unit applies vehicle target detection model on vehicle image, obtains vehicle region image.So
Vehicle region image is passed to skylight object detection unit afterwards, skylight target detection model is applied on vehicle region image, is known
Other skylight.Module of target detection detects vehicle target first, and skylight target, this distribution are then detected in vehicle target image
Detection means can be effectively prevented from the flase drop caused by image background is complicated, includes the factors such as other skylight of vehicle in background,
Improve the accuracy rate of skylight detection.
Archives picture is compared skylight detection mark judging unit with picture skylight to be detected detection mark, judges mould
Block carries out integrated judgment to the result of whole testing process, and feeds back unsanctioned reason and picture.
The specific detection method of vehicle target detection unit includes：As shown in figure 3, detection module is first by vehicle to be detected
Image inputs vehicle target detection model, obtains N number of one-dimension array [class, x, y, width, height], array first first
A element represents object type, is that vehicle is then 1, is not that vehicle is then 0, square where four element characterization destination objects after array
Shape region, x, y represent rectangle upper left angular coordinate, and width represents rectangle width, and height represents rectangular elevation.Each array
A vehicle target is corresponded to, builds headlight for vehicle information using vehicle region rectangle frame size, with rectangle frame area most
Big array is exported as detection module, and vehicle region image is then extracted from image by rectangle frame positional information.This side
Method can effectively pick out other non-annual test target vehicles in background.
Vehicle target detection model acquisition methods are as follows：
S1, training data prepare：Acquisition different automobile types (vehicle such as such as car, sport car, offroad vehicle, minibus, commercial vehicle),
Different brands, the vehicle image for specifying shooting angle scope (being needed completely equipped with skylight area image at the top of vehicle body)；
S2, data mark：Vehicle target is marked in the picture using rectangle frame, every image corresponds to a rectangle frame,
Vehicle target is included in frame；
S3, model training：Using the training data marked, vehicle target detection mould of the training based on deep learning network
Type (common knowledge, does not repeat hereby)；
The specific detection method of skylight object detection unit includes：As shown in figure 4, obtained vehicle region image is inputted
Skylight target detection model, obtains an one-dimension array [class, x, y, width, height], and first element of array represents
Object type, is that skylight is then 1, is not that skylight is then 0, rectangular area, x, y where four element characterization destination objects after array
Rectangle upper left angular coordinate is represented, width represents rectangle width, and height represents rectangular elevation, passes through rectangle frame positional information
Skylight area image is extracted from vehicle region image.
Skylight target detection model acquisition methods are as follows：
S1, training data prepare：Obtain different automobile types, different brands, specify shooting angle scope (day to be housed at the top of vehicle body
Window area image needs complete) image, using the above-mentioned image of vehicle region target detection model batch processing, obtain vehicle region
Area image；
S2, data mark：Skylight is marked in vehicle region image using rectangle frame, every vehicle region image corresponds to
One rectangle frame, frame is interior to include skylight target；
S3, model training：Using the training data marked, skylight target detection mould of the training based on deep learning network
Type (common knowledge, does not repeat hereby)；
The installation skylight examination criteria of the present invention is as follows：Vehicle target whether there is in picture to be detected；In archives picture
Vehicle target whether there is；Skylight whether there is in picture vehicle region image to be detected；In archives picture vehicle region image
Skylight whether there is；Archives picture and picture skylight to be detected detection mark comparative result；The present invention uses an one-dimension array
[x1, x2, x3, x4, x5] represents verification state, and initial value is [0,0,0,0,0].Flag bit x1 represents vehicle in picture to be detected
Target whether there is, if in the presence of if x1 be 0, if there is no x1 be 1；Whether flag bit x2 represents in archives picture skylight target
In the presence of, if in the presence of if x2 be 0, if there is no x2 be 1；Flag bit x3 represents skylight in picture vehicle region image to be detected
No presence, if in the presence of if x3 be 0, if there is no x3 be 1；Flag bit x4 represents skylight in archives picture vehicle region image
No presence, if in the presence of if x4 be 0, if there is no x4 be 1；Flag bit x5 represents archives picture and is detected with picture skylight to be detected
Indicate comparative result, if x3 is identical with x4 values, x5 0, if x3 is different from x4 values, and x4 is 0, then x5 is 0, this situation corresponds to
There are skylight in archives picture, and there is no skylight in picture to be detected, be not belonging to increase checking scope；If x3 is different from x4 values,
And x4 is 1, then x5 is 1, there are skylight target in this situation map file picture to be detected, and day is not present in archives picture
Window, belongs to installation skylight；Finally, statistical mark position [x1, x2, x5] state, if mark is is 0, verification passes through, if in the presence of
1, then verify and do not pass through.It can obtain verifying unsanctioned reason according to the position that state 1 occurs.If x1 is 1, mapping to be checked
Be not detected by vehicle target as in, it is possible the reason for have：Image to be detected obtains stage error, vehicle shooting angle does not meet rule
It is fixed, it is not bad comprising complete vehicle body or picture quality, there is overexposure or excessively dark, therefore cause that the audit fails；If x2 is 1,
Vehicle target is not detected by archival image, possible cause obtains stage error, or server storage archives figure for file data
Mistake occurs for sorting phase during piece, deposits other classification images by mistake, therefore causes that the audit fails.If x3 is 1, show the vehicle
Illegal to install skylight additional, examination ＆ verification does not pass through.
Determination module according to verification standard judge skylight verify whether by, if if direct back-checking successfully mark
Know, according to the position back-checking failure cause and corresponding picture that flag bit is 1, remain later stage examination ＆ verification if not and if investigate.
The implementation idiographic flow of the present invention is as shown in Fig. 2, include the following steps：
S1, from server download vehicle pictures to be detected and map file picture；
S2, using based on deep learning network vehicle target detection model detect vehicle, judge vehicle pictures to be detected
Middle vehicle target whether there is, and is 0 if recording this mark in the presence of if, extracts vehicle region image；This is recorded if being not present
Bar mark is 1, and preserves picture concerned, into statistical analysis flow；
S3, using based on deep learning network vehicle target detection model detect vehicle, judge vehicle in archives picture
Target whether there is, and is 0 if recording this mark in the presence of if, extracts vehicle region image；Indicate if recording this there is no if
For 1, and picture concerned is preserved, into statistical analysis flow；
S4, the car extracted using the skylight target detection model inspection based on deep learning network from vehicle pictures to be checked
Area image, judges that skylight whether there is, and is 0 if recording this mark in the presence of if, extracts skylight area image；If it is not present
It is 1 then to record this mark；
S5, the vehicle area extracted using the skylight target detection model inspection based on deep learning network from archives picture
Area image, judges that skylight whether there is, and is 0 if recording this mark in the presence of if, extracts skylight area image；Remember if there is no if
It is 1 to record this mark；
S6, judge whether vehicle pictures skylight detection mark to be detected and archives picture skylight detection mark are consistent, if one
Cause, then it is 0 to record this mark；If inconsistent, judge whether archives picture skylight detection mark is 0, this is recorded if 0
Bar mark is 0, and if 1, then it is 1 to record this mark；
S7, the result of the action to whole process carry out statistical analysis, if it is 0 to input this module identification, detection passes through；If
The mark of this module is inputted there are mark 1, then is detected not by the way that while detection can be obtained not for 1 position occurred according to mark
Pass through reason and problem picture.
The advantages of basic principle and main feature and this programme of this programme has been shown and described above.The technology of the industry
Personnel are it should be appreciated that this programme is not restricted to the described embodiments, and the above embodiments and description only describe this
The principle of scheme, on the premise of this programme spirit and scope are not departed from, this programme also has various changes and modifications, these changes
Change and improve and both fall within the range of claimed this programme.This programme be claimed scope by appended claims and its
Equivalent thereof.
Claims (4)
1. a kind of car installs the intelligent checking system of skylight additional, it is characterised in that including module of target detection and judgment module, its
In, the module of target detection includes vehicle target detection unit, skylight object detection unit and skylight detection mark and judges list
Member；The vehicle target detection unit detects vehicle image by vehicle target detection model, obtains vehicle region image, described
Skylight object detection unit is detected vehicle region image using skylight target detection model, and identifies skylight, described
Archives picture is compared skylight detection mark judging unit with picture skylight to be detected detection mark, the determination module pair
The result of whole testing process carries out integrated judgment, and feeds back unsanctioned reason and picture.
2. a kind of car installs the intelligent detecting method of skylight additional, it is characterised in that includes the following steps：
S1, from server download vehicle pictures to be detected and map file picture；
S2, using based on deep learning network vehicle target detection model detect vehicle, judge car in vehicle pictures to be detected
Target whether there is, and is 0 if recording this mark in the presence of if, extracts vehicle region image；Marked if recording this there is no if
Will is 1, and preserves picture concerned, into statistical analysis flow；
S3, using based on deep learning network vehicle target detection model detect vehicle, judge vehicle target in archives picture
It whether there is, be 0 if recording this mark in the presence of if, extract vehicle region image；It is 1 if recording this mark there is no if,
And picture concerned is preserved, into statistical analysis flow；
S4, the vehicle area extracted using the skylight target detection model inspection based on deep learning network from vehicle pictures to be checked
Area image, judges that skylight whether there is, and is 0 if recording this mark in the presence of if, extracts skylight area image；Remember if there is no if
It is 1 to record this mark；
S5, the vehicle region figure extracted using the skylight target detection model inspection based on deep learning network from archives picture
Picture, judges that skylight whether there is, and is 0 if recording this mark in the presence of if, extracts skylight area image；This is recorded if being not present
Bar mark is 1；
S6, judge whether vehicle pictures skylight detection mark to be detected and archives picture skylight detection mark are consistent, if unanimously,
It is 0 to record this mark；If inconsistent, judge whether archives picture skylight detection mark is 0, this mark is recorded if 0
Will is 0, and if 1, then it is 1 to record this mark；
S7, the result of the action to whole process carry out statistical analysis, if it is 0 to input this module identification, detection passes through；If input
The mark of this module is then detected not by that while can obtain detection for 1 position occurred according to mark and not pass through there are mark 1
Reason and problem picture.
3. intelligent detecting method as claimed in claim 2, it is characterised in that the obtaining step of the vehicle target detection model
It is as follows：
S21, obtain different automobile types in different illumination conditions, the vehicle image of different angle shooting；
S22, using rectangle frame marked vehicle area image position；
S23, detect deep neural network model, acquisition vehicle detection model using the vehicle region image training objective.
4. intelligent detecting method as claimed in claim 2, it is characterised in that the obtaining step of the skylight target detection model
It is as follows：
S31, the vehicle image for obtaining the different automobile types equipped with skylight, vehicle body top skylight region need complete；
S32, interception vehicle region image；
S33, using rectangle frame mark skylight position；
S34, detect deep neural network model, acquisition skylight detection model using the skylight area image training objective.
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| CN201710949529.1A CN107967445A (en) | 2017-10-13 | 2017-10-13 | A kind of car installs the intelligent checking system and method for skylight additional |
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| CN201710949529.1A CN107967445A (en) | 2017-10-13 | 2017-10-13 | A kind of car installs the intelligent checking system and method for skylight additional |
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Application publication date: 20180427 |