CN107967446A - A kind of intelligent checking system and method for installing engine protection device additional - Google Patents
A kind of intelligent checking system and method for installing engine protection device additional Download PDFInfo
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- CN107967446A CN107967446A CN201710949543.1A CN201710949543A CN107967446A CN 107967446 A CN107967446 A CN 107967446A CN 201710949543 A CN201710949543 A CN 201710949543A CN 107967446 A CN107967446 A CN 107967446A
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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
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V30/00—Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
- G06V30/10—Character recognition
- G06V30/19—Recognition using electronic means
- G06V30/192—Recognition using electronic means using simultaneous comparisons or correlations of the image signals with a plurality of references
- G06V30/194—References adjustable by an adaptive method, e.g. learning
Abstract
The invention discloses a kind of intelligent checking system and method for installing engine protection device additional, including module of target detection and judgment module, wherein, the module of target detection includes vehicle target detection unit, protective device object detection unit and protective device detection mark judging unit；The vehicle target detection unit is used to obtain vehicle region image, the protective device object detection unit is used to identify engine protection device, archives picture is compared the protective device detection mark judging unit with picture engine protection device 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 the detection of engine protection device additional, realize the whole-process automatic verification in detection process, unsanctioned detection image and reason can be passed back to server preservation at the same time to remain to collect evidence, both manpower has been saved, in turn ensure that the just, openly of verifying work.
Description
Technical field
It is more particularly to a kind of to install engine additional the present invention relates to the artificial intelligence judgment technology field of automotive vehicle annual test
The intelligent checking system and method for protective device.
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 the detection of engine protection device additional mainly by being accomplished manually, this method cost of labor compared with
Height, it is less efficient, and repeated verification operation easily produces fatigue for a long time, the defective mode such as easy carelessness, it is accurate to influence verification
Rate.
How accurately and rapidly engine protection device whether is installed additional to vehicle to verify, while avoid desk checking
Of high cost, fatiguability, 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 a kind of intelligent checking system and method for installing engine protection device additional, automatic inspection
Whether measuring car installs engine protection device additional, 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 intelligent checking system for installing engine protection device additional, including module of target detection and determination module, wherein,
The module of target detection includes vehicle target detection unit, protective device object detection unit and protective device detection mark and sentences
Disconnected unit；The vehicle target detection unit detects vehicle image by vehicle target detection model, obtains vehicle region image,
The protective device object detection unit is detected vehicle region image using engine protection device target detection model,
And identifying engine protection device, the protective device detection mark judging unit starts archives picture and picture to be detected
Machine protective device detection mark is compared, and the determination module carries out integrated judgment to the result of whole testing process, and instead
Present unsanctioned reason and picture.
A kind of intelligent detecting method for installing engine protection device 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, using the engine protection device target detection model inspection based on deep learning network from vehicle pictures to be checked
The vehicle region image of middle extraction, judges that engine protection device whether there is, and is 0 if recording this mark in the presence of if, extraction
Engine protection device area image；It is 1 if recording this mark there is no if；
S5, carried using the engine protection device target detection model inspection based on deep learning network from archives picture
The vehicle region image taken, judges that engine protection device whether there is, and is 0 if recording this mark in the presence of if, extraction is started
Machine protective device area image；It is 1 if recording this mark there is no if；
S6, judge that vehicle pictures engine protection device detection mark to be detected is examined with archives picture engine protection device
Whether mark will is consistent, if unanimously, it is 0 to record this mark；If inconsistent, archives picture engine protection device is judged
Detect whether mark is 0, it is 0 to record this mark if 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 vehicle target detection model obtaining step 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 engine protection device target detection model obtaining step is as follows：
S31, the vehicle image for obtaining the different automobile types equipped with engine protection device, body forward structure engine protection device
Region needs complete；
S32, interception vehicle region image；
S33, using rectangle frame mark engine protection device position；
S34, using the engine protection device area image training objective detect deep neural network model, sent out
Motivation protective device detection model.
The beneficial effects of the invention are as follows：Present invention is mainly applied to car in automotive vehicle annual test to install engine protection additional
Device detects, and realizes the whole-process automatic verification in detection process, while can pass unsanctioned detection image and reason back
Server preserves and remains to collect evidence, and has 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 installation engine protection device detection decision flow chart 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 inventive engine protective device object detection unit.
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, protective device object detection unit and protective device
Detection mark judging unit；
Vehicle target detection unit detects vehicle image by vehicle target detection model, obtains vehicle region image；So
Afterwards, vehicle region image is passed to protective device object detection unit, protective device object detection unit utilizes engine protection
Device target detection model is detected vehicle region image, and identifies engine protection device.Module of target detection is first
Vehicle target is first detected, engine protection device target is then detected in vehicle target image, this distribution detection means can
To be effectively prevented from the flase drop caused by image background is complicated, includes the factors such as other vehicle motor protective devices in background,
Improve the accuracy rate of engine protection device detection.
Protective device detection mark judging unit indicates archives picture and picture engine protection device to be detected detection
It is compared, determination module carries out integrated judgment to the result of whole testing process, and feeds back unsanctioned reason and picture.
The intelligent detecting method of the present invention implements idiographic flow as shown in Fig. 2, including 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, using the engine protection device target detection model inspection based on deep learning network from vehicle pictures to be checked
The vehicle region image of middle extraction, judges that engine protection device whether there is, and is 0 if recording this mark in the presence of if, extraction
Engine protection device area image；It is 1 if recording this mark there is no if；
S5, carried using the engine protection device target detection model inspection based on deep learning network from archives picture
The vehicle region image taken, judges that engine protection device whether there is, and is 0 if recording this mark in the presence of if, extraction is started
Machine protective device area image；It is 1 if recording this mark there is no if；
S6, judge that vehicle pictures engine protection device detection mark to be detected is examined with archives picture engine protection device
Whether mark will is consistent, if unanimously, it is 0 to record this mark；If inconsistent, archives picture engine protection device is judged
Detect whether mark is 0, it is 0 to record this mark if 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；
Wherein, the specific detection method of vehicle target detection unit includes：As shown in figure 3, detection module first will be to be checked
Vehicle image input vehicle target detection model is surveyed, obtains N number of one-dimension array [class, x, y, width, height] first, number
First element of group represents object type, is that vehicle is then 1, is not that vehicle is then 0, four element characterization destination objects after array
Place rectangular area, x, y represent rectangle upper left angular coordinate, and width represents rectangle width, and height represents rectangular elevation.Often
A array corresponds to a vehicle target, headlight for vehicle information is built using vehicle region rectangle frame size, with rectangle frame
The array of area maximum is exported as detection module, and vehicle region figure is then extracted from image by rectangle frame positional information
Picture.The 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 figure for specifying shooting angle scope (body forward structure needs complete equipped with engine protection device area image)
Picture；
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 engine protection device object detection unit includes：As shown in figure 4, the vehicle area that will be obtained
Area image input engine protection device target detection model, obtain an one-dimension array [class, x, y, width,
Height], first element of array represents object type, be engine protection device then be 1, be not engine protection device then
For 0, rectangular area, x, y represent rectangle upper left angular coordinate where four elements characterization destination objects after array, and width is represented
Rectangle width, height represent rectangular elevation, and engine protection is extracted from vehicle region image by rectangle frame positional information
Device area image.
Engine protection device target detection model acquisition methods are as follows：
S1, training data prepare：Obtain different automobile types, different brands, specifying shooting angle scope, (body forward structure is equipped with hair
Motivation protective device area image needs complete) image, using the above-mentioned image of vehicle region target detection model batch processing, obtain
To vehicle region area image；
S2, data mark：Engine protection device is marked in vehicle region image using rectangle frame, every vehicle area
Area image corresponds to a rectangle frame, and engine protection device target is included in frame；
S3, model training：Using the training data marked, engine protection device of the training based on deep learning network
Target detection model (common knowledge, does not repeat hereby).
The installation engine protection device examination criteria of the present invention is as follows：Vehicle target whether there is in picture to be detected；
Vehicle target whether there is in archives picture；Picture vehicle region image intrinsic motivation protective device to be detected whether there is；Shelves
Case picture vehicle region image intrinsic motivation protective device whether there is；Archives picture and picture engine protection device to be detected
Detection mark comparative result；The present invention represents verification state using an one-dimension array [x1, x2, x3, x4, x5], and initial value is
[0,0,0,0,0].Flag bit x1 represents vehicle target in picture to be detected and whether there is, if in the presence of if x1 be 0, if there is no
X1 is 1；Flag bit x2 represents archives picture intrinsic motivation protective device target and whether there is, if in the presence of if x2 be 0, if being not present
Then x2 is 1；Flag bit x3 represents picture vehicle region image intrinsic motivation protective device to be detected and whether there is, if the x3 in the presence of if
For 0, if there is no if x3 be 1；Flag bit x4 represents archives picture vehicle region image intrinsic motivation protective device and whether there is,
If in the presence of if x4 be 0, if there is no x4 be 1；Flag bit x5 represents archives picture and picture engine protection device to be detected
Detection mark 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
There are engine protection device in map file picture, and there is no engine protection device in picture to be detected, be not belonging to install additional
Detection range；If x3 is different from x4 values, and x4 is 1, then x5 is 1, and there are engine in this situation map file picture to be detected
Protective device target, and engine protection device is not present in archives picture, belong to and install engine protection device additional；Finally, unite
Flag bit [x1, x2, the x5] state of counting, if mark is is 0, verification passes through, if there are 1, verifies and does not pass through.According to state
1 position occurred can obtain verifying unsanctioned reason.If x1 is 1, vehicle target is not detected by image to be detected, can
The reason for energy, has：Image to be detected acquisition stage error, vehicle shooting angle are against regulation, not comprising complete vehicle body or picture
Quality is bad, overexposure or excessively dark occurs, therefore causes that the audit fails；If x2 is 1, vehicle mesh is not detected by archival image
Mark, possible cause obtains stage error for file data, or mistake occurs for sorting phase during server storage archives picture, deposits by mistake
Other classification images, therefore cause that the audit fails.If x3 is 1, show that the vehicle illegally installs engine protection device additional, examine
Core does not pass through.
Determination module according to verification standard judge engine protection device verification whether by, if if directly return school
Success flag is tested, if, according to the position back-checking failure cause and corresponding picture that flag bit is 1, remaining the later stage examines not if
Verify card.
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 intelligent checking system for installing engine protection device additional, it is characterised in that including module of target detection and judgement
Module, wherein, the module of target detection includes vehicle target detection unit, protective device object detection unit and protective device
Detection mark judging unit；The vehicle target detection unit detects vehicle image by vehicle target detection model, obtains car
Area image, the protective device object detection unit is using engine protection device target detection model to vehicle region figure
As being detected, and identify engine protection device, the protective device detection mark judging unit is by archives picture with treating
Detection picture engine protection device detection mark is compared, and the determination module carries out the result of whole testing process comprehensive
Close and judge, and feed back unsanctioned reason and picture.
2. a kind of intelligent detecting method for installing engine protection device additional, it is characterised in that 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 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, carried using the engine protection device target detection model inspection based on deep learning network from vehicle pictures to be checked
The vehicle region image taken, judges that engine protection device whether there is, and is 0 if recording this mark in the presence of if, extraction is started
Machine protective device area image；It is 1 if recording this mark there is no if；
S5, extracted using the engine protection device target detection model inspection based on deep learning network from archives picture
Vehicle region image, judges that engine protection device whether there is, and is 0 if recording this mark in the presence of if, extraction engine is prevented
Protection unit area image；It is 1 if recording this mark there is no if；
S6, judge that vehicle pictures engine protection device detection mark to be detected is marked with the detection of archives picture engine protection device
Whether will is consistent, if unanimously, it is 0 to record this mark；If inconsistent, judge that archives picture engine protection device detects
Whether mark is 0, and it is 0 to record this mark if 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 vehicle target detection model obtaining step 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 engine protection device target detection model
Obtaining step is as follows：
S31, the vehicle image for obtaining the different automobile types equipped with engine protection device, body forward structure engine protection device region
Need complete；
S32, interception vehicle region image；
S33, using rectangle frame mark engine protection device position；
S34, detect deep neural network model, acquisition engine using the engine protection device area image training objective
Protective device detection model.
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Application publication date: 20180427 |