CN109344835A - Altering detecting method based on vehicle VIN code character position - Google Patents
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Abstract
The invention discloses a kind of altering detecting methods based on vehicle VIN code character position, comprising the following steps: obtains the VIN code archival image in the vehicle VIN code image and server to be detected in vehicle annual test；VIN code region is detected, judges that target whether there is；VIN code is divided into single character, judge character whether with answer matches；By the placement configurations homography matrix of image to be detected and archival image two characters of head and the tail, by image to be detected space reflection to archival image space；Judge transformed image to be detected character position (character between left and right away from and upper and lower position) whether the position consistency with server archival image character；On the contrary if it exists, unanimously, then recording this mark is 0 for the above judgement, then to record this mark be 1, and saves picture concerned；If record mark is 0, vehicle VIN code tampering detection passes through, and shows that VIN code is not distorted, if record mark is that 1, VIN code tampering detection does not pass through, shows that VIN code is tampered.Meanwhile unacceptable reason and problem picture are verified according to the position acquisition that mark 1 occurs.The present invention realizes the automatic Verification of VIN code in vehicle annual test, and existing manual examination and verification mode is substituted, has saved manpower, accelerates audit speed, and can be effectively detected whether VIN code is tampered, and ensure that the disclosure of examination, just.
Description
Technical field
The present invention relates to the artificial intelligence judgment technology fields of automotive vehicle annual test, in particular to a kind of to be based on vehicle
The altering detecting method of VIN code character position.
Background technique
Constantly improve with living standards of the people with the continuous social and economic development, Urban vehicles poputation rapidly increases
It is long.The workload of automotive vehicle annual test also increases rapidly therewith.Vehicle VIN code tampering detection is main in traditional vehicle annual test
It is by artificial detection, the case where distorting for vehicle VIN code individual characters, such as character position is changed, general survey personnel
It is difficult with the naked eye to go to differentiate, influences to verify accuracy rate.
How accurately and rapidly vehicle VIN code to be verified, while avoiding desk checking at high cost, fatiguability is easily dredged
The drawbacks such as suddenly, are technical problems urgently to be solved.
Summary of the invention
The purpose of the present invention is: it proposes a kind of vehicle VIN code altering detecting method for vehicle annual test, audits vehicle automatically
Whether VIN code is consistent with server archive content, to meet nowadays the needs of to vehicle annual test working efficiency, accuracy rate.
The technical solution adopted by the present invention to solve the technical problems is:
A kind of altering detecting method based on vehicle VIN code character position, comprising the following steps:
Archive VIN code image in S1, acquisition vehicle VIN code image to be detected and server；
S2, using the target detection model inspection VIN code image based on deep learning, judge that VIN code region whether there is,
Then recording this mark if it exists is 0, extracts VIN code region；Then recording this mark if it does not exist is 1, and saves related figure
Piece, into statistical analysis process；
S3, using the Character segmentation model inspection VIN code region based on deep learning, VIN code is divided into single character,
Judge that character whether with answer matches, this is recorded if setting up and indicates to be 0, and extracts each character picture；If invalid
Recording this mark is 1, and saves picture concerned, into statistical analysis process；
S4, by image to be detected and archival image head and the tail two characters placement configurations homography matrix, by mapping to be checked
Image space is mapped to archival image space, judge transformed image to be detected character position (character between left and right away from it is upper and lower
Position) whether the position consistency with server archival image character, it is 0 that this mark is recorded if consistent；Remember if inconsistent
Recording this mark is 1, and saves picture concerned, into statistical analysis process；
It is S5, for statistical analysis to the result of the action of whole process, flag bit all 0 is recorded, then VIN code tampering detection
Pass through, if it exists mark 1, then VIN code tampering detection does not pass through；Meanwhile the position acquisition verification occurred according to mark 1 does not pass through
The reason of and problem picture.
Judge that VIN code region whether there is in the step S2 and use following method:
The classification information of vehicle VIN code image object to be detected is obtained using Softmax, uses bounding box
Regression obtains the location information of vehicle VIN code image object to be detected.
Target detection model obtaining step in the step S2 based on deep learning is as follows:
S21, training data prepare: obtaining the vehicle VIN code image of different shooting conditions (such as illumination, angle)；
S22, data mark: vehicle VIN code region is marked in the picture using rectangle frame, rectangle frame region domestic demand is complete
Include vehicle VIN code；
S23, model training: it utilizes and has used the trained VGG basic model of ImageNet, the vehicle that will have been marked
VIN code image inputs in SSD frame, is finely adjusted on basic model, to preferably train vehicle VIN code target detection mould
Type.
Described be finely adjusted on basic model includes the following steps:
S231. the mean value file of vehicle VIN code data set is calculated；
S232. the last output of modification SSD frame；
Basic studies rate is adjusted to 0.0001, weight_decay and is adjusted to 0.0005, learned by S233. regularized learning algorithm rate
Habit rate strategy is set as " multistep ", and gamma is set as 0.1, momentum and is set as 0.9；
S234. load grounding model is finely adjusted.
Character segmentation model obtaining step in the step 3 based on deep learning is as follows:
S31, training data prepare: obtaining the start-stop symbol image of different shooting conditions (such as illumination, angle)；
S32, data mark: start-stop symbol region is marked in the picture using rectangle frame；
S33, model training: it utilizes and has used the trained VGG basic model of ImageNet, the vehicle that will have been marked
VIN code image inputs in SSD frame, is finely adjusted on basic model, to preferably start-stop be trained to accord with target detection model.
Described be finely adjusted on basic model includes the following steps:
S231. the mean value file of vehicle VIN code data set is calculated；
S232. the last output of modification SSD frame；
Basic studies rate is adjusted to 0.0001, weight_decay and is adjusted to 0.0005, learned by S233. regularized learning algorithm rate
Habit rate strategy is set as " multistep ", and gamma is set as 0.1, momentum and is set as 0.9；
S234. load grounding model is finely adjusted.
Steps are as follows for character position comparison in the step S4:
S41, pass through the placement configurations homography matrix of two characters of VIN code head and the tail in image to be detected and archival image；
S42, by image to be detected space reflection to archival image space；
S43, calculate the variance of character height in image to be detected and archival image and between left and right away from variance, then count
The distance of two variances is calculated, if distance is less than certain threshold value, tampering detection is judged to and passes through；It is on the contrary then to be judged to tampering detection obstructed
It crosses.
The beneficial effects of the present invention are: present invention is mainly applied to vehicle VIN code tampering detection in automotive vehicle annual test,
It realizes the automatic Verification that VIN code is distorted, while unsanctioned verification image and reason can be passed back to server preservation and stayed
Wait collect evidence.Both manpower has been saved, has in turn ensured the just, openly of verifying work.
Detailed description of the invention
Fig. 1 is vehicle VIN code tampering detection flow chart of the invention.
Fig. 2 is structural schematic diagram of the invention.
Fig. 3 is the structural schematic diagram of object detection unit of the present invention.
Fig. 4 is the structural schematic diagram of image map unit of the present invention.
Fig. 5 is the structural schematic diagram of character position detection unit of the present invention.
Specific embodiment
Below in conjunction with attached drawing, the present invention will be further described.
Present invention is primarily based on module of target detection, character position detection module and determination modules.
As shown in Fig. 2, module of target detection is made of VIN code object detection unit and Character segmentation unit.Firstly, will figure
As being passed to object detection unit, VIN code target detection model is applied on the image, obtains vehicle VIN code area image.It uses again
VIN code is divided into single character by Character segmentation model.Module of target detection detects vehicle VIN code region first, then in vehicle
Character segmentation is carried out in VIN code region, this substep detection means can be effectively avoided because vehicle VIN code areas case is multiple
Miscellaneous bring erroneous detection influences, and improves the accuracy rate of vehicle VIN code zone location and Character segmentation, further improves vehicle VIN
Accuracy code detection and compared.
Vehicle VIN code object detection unit is based on SSD (Single Shot MultiBox Detector) frame come real
Existing.The frame uses VGG network as feature extractor.However, the effect of detection is also different for different characteristic patterns,
Therefore SSD uses feature pyramid structure, is classified and is returned simultaneously on multiple characteristic patterns.It is obtained using Softmax
The classification information of target obtains the location information of target using bounding box regression.As shown in figure 3, detection mould
Block first by vehicle VIN code area image input area detection model, obtain first N number of one-dimension array [class, x, y,
Width, height], first element of array represents object type, is obtained by Softmax, if target is vehicle VIN code
It is 1, if not being then 0.Rectangular area where four element characterization target objects, passes through bounding box after array
Regression is obtained, x, and y represents rectangle upper left angular coordinate, and width represents rectangle width, and height represents rectangular elevation.
Each array corresponds to a region, constructs region distance information using region rectangle frame size, most with rectangle frame area
Big array is exported as detection module, and vehicle VIN code region is then extracted from image by rectangle frame location information.This side
Method can effectively pick out other interference regions in background.
One new network of re -training is more complicated, and needs very big data volume, and parameter regulation also compares
Therefore difficulty is a good selection using fine tuning.So-called fine tuning is exactly that oneself is added on trained model
Data, the suitable model of training.Fine tuning is advantageous in that without re -training model, to greatly improve efficiency.Meanwhile
In the case that data volume itself is little, the feature that model learning can be made to arrive is finely tuned with more robustness.
Vehicle VIN code target detection model acquisition methods are as follows:
S21, training data prepare: obtaining the vehicle VIN code image of different shooting conditions (such as illumination, angle)；
S22, data mark: vehicle VIN code region is marked in the picture using rectangle frame, rectangle frame region domestic demand is complete
Include vehicle VIN code；
S23, model training: it utilizes and has used the trained VGG basic model of ImageNet, the vehicle that will have been marked
VIN code image inputs in SSD frame, is finely adjusted on basic model, to preferably train vehicle VIN code target detection mould
Type.Specifically: firstly, the mean value file of vehicle VIN code data set is calculated, because of the mean value file of vehicle VIN code data set
It is not quite alike with the mean value file of ImageNet data set.Then, the last output of modification SSD frame, ImageNet is one
The classification task of 1000 classes, and this model only has 2 classes, is background and vehicle VIN code respectively.It is followed by regularized learning algorithm rate, is learnt
Rate is very big corresponding to the performance influence of neural network, but by experience and can only test to obtain suitable value, by testing,
Basic studies rate is adjusted to 0.0001, weight_decay and is adjusted to 0.0005 by this patent, and learning rate strategy is set as
" multistep ", gamma is set as 0.1, momentum and is set as 0.9.Finally, load grounding model is finely adjusted.It is logical
Above-mentioned setting is crossed, model can restrain quickly and loss drops to 2 or so, improves the efficiency and precision of model training.
Character segmentation unit is also based on SSD (Single Shot MultiBox Detector) frame to realize.Such as
Shown in Fig. 3, detection module first by vehicle VIN code image input Character segmentation model, obtain N number of one-dimension array [class, x,
Y, width, height], first element of array represents object type, is obtained by Softmax.Character in vehicle VIN code
There are " 0~9 ", " A~N ", " P " and " R~Z " and totally 34 kinds, does not include " O " and " Q ", indicated respectively with 0~33, four after array
Rectangular area where a element characterization target object, is obtained by bounding box regression, and x, y represent a rectangle left side
Upper angular coordinate, width represent rectangle width, and height represents rectangular elevation.Each array corresponds to a region, passes through square
Shape frame location information extracts the single character position of vehicle VIN code from image.The method can effectively pick out other dry in background
Disturb region.
Character segmentation model acquisition methods are as follows:
S31, training data prepare: obtaining the vehicle VIN code character image of different shooting conditions (such as illumination, angle)；
S32, data mark: the single character of vehicle VIN code is marked in the picture using rectangle frame, rectangle frame region domestic demand
It completely include the single character zone of vehicle VIN code；
S33, model training: it utilizes and has used the trained VGG basic model of ImageNet, the character figure that will have been marked
As being finely adjusted on basic model in input SSD frame, to preferably start-stop be trained to accord with target detection model.Specifically:
Firstly, calculate vehicle VIN code character data set mean value file because the mean value file of vehicle VIN code character data set and
The mean value file of ImageNet data set is not quite alike.Then, the last output of modification SSD frame, ImageNet is one
The classification task of 1000 classes, and this model only has 2 classes, is background and vehicle VIN code character respectively.It is followed by regularized learning algorithm rate,
The performance influence that learning rate corresponds to neural network is very big, but can only pass through by experience and experiment to obtain suitable value
Basic studies rate is adjusted to 0.0001, weight_decay and is adjusted to 0.0005 by experiment, this patent, and learning rate strategy is set as
" multistep ", gamma is set as 0.1, momentum and is set as 0.9.Finally, load grounding model is finely adjusted.It is logical
Above-mentioned setting is crossed, model can restrain quickly and loss drops to 2 or so, improves the efficiency and precision of model training.
Character position detection module includes image map unit and character position detection unit.Image map unit such as Fig. 4
It is shown, according to the placement configurations homography matrix of image to be detected and archival image two characters of head and the tail, image to be detected is mapped
To archival image.Then character position detection unit is used, as shown in Figure 5.Calculate character in image to be detected and archival image
The variance of height and between left and right away from variance, then calculate two variances distance, if distance be less than certain threshold value, be judged to
Tampering detection passes through；It is on the contrary then be judged to tampering detection and do not pass through.
Vehicle VIN code tampering detection standard of the invention is as follows: vehicle VIN code region whether there is in image to be detected；
Whether the position of the character of image to be detected is consistent with the location conten of the character of server archival image after transformation；The present invention adopts
Verification state is indicated with an one-dimension array [x1, x2, x3], and initial value is [0,0,0], and flag bit x1 represents vehicle VIN code area
Domain whether there is, and then x1 is 0 if it exists, and then x1 is 1 if it does not exist；Whether flag bit x2 represents vehicle VIN code correct, if correctly
Then x2 is 0, and x2 is 1 if incorrect；The position of the character of image to be detected and server achieve after flag bit x3 representation transformation
Whether the position of the character of image is consistent, and x3 is 0 if consistent, if inconsistent x3 is 1.Finally, statistical mark position state, if
Mark is is 0, then verification passes through, and if it exists 1, then it verifies and does not pass through.The position occurred according to state 1 is available to be verified not
The reason of passing through.If x1 is 1, vehicle VIN code region may be not present in image or shooting angle is against regulation；If x2
It is 1, then the possible incorrect or shooting angle of the VIN code in image is against regulation；If x3 is 1, possible reason is vehicle
There may be distort for VIN code.
Determination module judges whether vehicle VIN code tampering detection passes through according to verification standard, and school is directly returned to if passing through
Success flag is tested, it is careful to remain the later period for the position back-checking failure cause and corresponding picture for being 1 according to flag bit if not passing through
Verify card.
Implementation detailed process of the invention is as shown in Figure 1, a kind of vehicle VIN code tampering detection side for vehicle annual test
Method includes the following steps:
Archive VIN code image in S1, acquisition vehicle VIN code image to be detected and server；
S2, using the target detection model inspection VIN code image based on deep learning, judge that VIN code region whether there is,
Then recording this mark if it exists is 0, extracts VIN code region；Then recording this mark if it does not exist is 1, and saves related figure
Piece, into statistical analysis process；
S3, using the Character segmentation model inspection VIN code region based on deep learning, VIN code is divided into single character,
Judge that character whether with answer matches, this is recorded if setting up and indicates to be 0, and extracts each character picture；If invalid
Recording this mark is 1, and saves picture concerned, into statistical analysis process；
S4, by image to be detected and archival image head and the tail two characters placement configurations homography matrix, by mapping to be checked
Image space is mapped to archival image space；
S5, judge transformed image to be detected character position (character between left and right away from and upper and lower position) whether with service
The position consistency of device archival image character, it is 0 that this mark is recorded if consistent；It is 1 that this mark is recorded if inconsistent, and
Picture concerned is saved, into statistical analysis process；
It is S6, for statistical analysis to the result of the action of whole process, flag bit all 0 is recorded, then VIN code tampering detection
Pass through, if it exists mark 1, then VIN code tampering detection does not pass through；Meanwhile the position acquisition verification occurred according to mark 1 does not pass through
The reason of and problem picture；
The advantages of basic principles and main features and this programme of this programme have 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, under the premise of not departing from this programme spirit and scope, this programme be will also have various changes and improvements, these changes
Change and improvement is both fallen within the scope of claimed this programme.This programme be claimed range by appended claims and its
Equivalent thereof.
Claims (7)
1. a kind of altering detecting method based on vehicle VIN code character position, which comprises the following steps:
Archive VIN code image in S1, acquisition vehicle VIN code image to be detected and server；
S2, using the target detection model inspection VIN code image based on deep learning, judge that VIN code region whether there is, if depositing
It is 0 then recording this mark, extracts VIN code region；Then recording this mark if it does not exist is 1, and saves picture concerned, into
Enter to statistically analyze process；
S3, using the Character segmentation model inspection VIN code region based on deep learning, VIN code is divided into single character, judge
Whether character records this if setting up and indicates to be 0, and extract each character picture with answer matches；It is recorded if invalid
This mark is 1, and saves picture concerned, into statistical analysis process；
S4, by image to be detected and archival image head and the tail two characters placement configurations homography matrix, by image to be detected sky
Between be mapped to archival image space, judge the position (character between left and right away from and upper and lower position) of transformed image to be detected character
Whether the position consistency with server archival image character, it is 0 that this mark is recorded if consistent；This is recorded if inconsistent
Mark is 1, and saves picture concerned, into statistical analysis process；
It is S5, for statistical analysis to the result of the action of whole process, flag bit all 0 is recorded, then VIN code tampering detection leads to
It crosses, if it exists mark 1, then VIN code tampering detection does not pass through；Meanwhile it is unacceptable according to the position acquisition verification that mark 1 occurs
Reason and problem picture.
2. a kind of altering detecting method based on vehicle VIN code character position as described in claim 1, which is characterized in that institute
It states and judges that VIN code region whether there is using following method in step S2:
The classification information of vehicle VIN code image object to be detected is obtained using Softmax, uses bounding box
Regression obtains the location information of vehicle VIN code image object to be detected.
3. a kind of altering detecting method based on vehicle VIN code character position as described in claim 1, which is characterized in that institute
The target detection model obtaining step stated in step S2 based on deep learning is as follows:
S21, training data prepare: obtaining the vehicle VIN code image of different shooting conditions (such as illumination, angle)；
S22, data mark: vehicle VIN code region is marked in the picture using rectangle frame, rectangle frame region domestic demand completely includes
Vehicle VIN code；
S23, model training: utilizing and used the trained VGG basic model of ImageNet, the vehicle VIN code that will have been marked
Image inputs in SSD frame, is finely adjusted on basic model, to preferably train vehicle VIN code target detection model.
4. a kind of altering detecting method based on vehicle VIN code character position as claimed in claim 3, which is characterized in that institute
It states to be finely adjusted on basic model and include the following steps:
S231. the mean value file of vehicle VIN code data set is calculated；
S232. the last output of modification SSD frame；
Basic studies rate is adjusted to 0.0001, weight_decay and is adjusted to 0.0005 by S233. regularized learning algorithm rate, learning rate
Strategy is set as " multistep ", and gamma is set as 0.1, momentum and is set as 0.9；
S234. load grounding model is finely adjusted.
5. a kind of altering detecting method based on vehicle VIN code character position as claimed in claim 1 or 2 or 3 or 4, special
Sign is that the Character segmentation model obtaining step in the step 3 based on deep learning is as follows:
S31, training data prepare: obtaining the start-stop symbol image of different shooting conditions (such as illumination, angle)；
S32, data mark: start-stop symbol region is marked in the picture using rectangle frame；
S33, model training: utilizing and used the trained VGG basic model of ImageNet, the vehicle VIN code that will have been marked
Image inputs in SSD frame, is finely adjusted on basic model, to preferably start-stop be trained to accord with target detection model.
6. a kind of altering detecting method based on vehicle VIN code character position as claimed in claim 5, which is characterized in that institute
It states to be finely adjusted on basic model and include the following steps:
S231. the mean value file of vehicle VIN code data set is calculated；
S232. the last output of modification SSD frame；
Basic studies rate is adjusted to 0.0001, weight_decay and is adjusted to 0.0005 by S233. regularized learning algorithm rate, learning rate
Strategy is set as " multistep ", and gamma is set as 0.1, momentum and is set as 0.9；
S234. load grounding model is finely adjusted.
7. a kind of altering detecting method based on vehicle VIN code character position as described in claims 1 or 2 or 3 or 4 or 6,
It is characterized in that, steps are as follows for character position comparison in the step S4:
S41, pass through the placement configurations homography matrix of two characters of VIN code head and the tail in image to be detected and archival image；
S42, by image to be detected space reflection to archival image space；
S43, calculate the variance of character height in image to be detected and archival image and between left and right away from variance, then calculate two
The distance of a variance is judged to tampering detection and passes through if distance is less than certain threshold value；It is on the contrary then be judged to tampering detection and do not pass through.
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| CN110245583A (en) * | 2019-05-27 | 2019-09-17 | 上海眼控科技股份有限公司 | An intelligent identification method for vehicle exhaust inspection report |
| CN110276295A (en) * | 2019-06-20 | 2019-09-24 | 上海眼控科技股份有限公司 | Vehicle identification number detection recognition method and equipment |
| CN110503102A (en) * | 2019-08-27 | 2019-11-26 | 上海眼控科技股份有限公司 | Vehicle identification code detection method, device, computer equipment and storage medium |
| CN110619330A (en) * | 2019-09-18 | 2019-12-27 | 上海眼控科技股份有限公司 | Recognition model training method and device, computer equipment and recognition method |
| CN110807396A (en) * | 2019-10-28 | 2020-02-18 | 华南理工大学 | Face-changing video tampering detection method and system based on consistency of illumination direction |
| CN110826551A (en) * | 2019-11-04 | 2020-02-21 | 大连交通大学 | Intelligent discrimination method for VIN code rubbing die image of motor vehicle |
| CN111062931A (en) * | 2019-12-20 | 2020-04-24 | 河北工业大学 | Detection method of spliced and tampered image |
| CN111144475A (en) * | 2019-12-22 | 2020-05-12 | 上海眼控科技股份有限公司 | Method and device for determining car seat, electronic equipment and readable storage medium |
| CN112418214A (en) * | 2020-11-06 | 2021-02-26 | 北京五八信息技术有限公司 | Vehicle identification code identification method and device, electronic equipment and storage medium |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107818322A (en) * | 2017-10-13 | 2018-03-20 | 上海眼控科技股份有限公司 | A kind of vehicle VIN code tampering detection system and methods for vehicle annual test |
| CN107886047A (en) * | 2017-10-13 | 2018-04-06 | 上海眼控科技股份有限公司 | A kind of detecting system and method for vehicle annual test survey report |
| CN107967445A (en) * | 2017-10-13 | 2018-04-27 | 上海眼控科技股份有限公司 | A kind of car installs the intelligent checking system and method for skylight additional |
-
2018
- 2018-09-20 CN CN201811105370.6A patent/CN109344835A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107818322A (en) * | 2017-10-13 | 2018-03-20 | 上海眼控科技股份有限公司 | A kind of vehicle VIN code tampering detection system and methods for vehicle annual test |
| CN107886047A (en) * | 2017-10-13 | 2018-04-06 | 上海眼控科技股份有限公司 | A kind of detecting system and method for vehicle annual test survey report |
| CN107967445A (en) * | 2017-10-13 | 2018-04-27 | 上海眼控科技股份有限公司 | A kind of car installs the intelligent checking system and method for skylight additional |
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| CN110503102A (en) * | 2019-08-27 | 2019-11-26 | 上海眼控科技股份有限公司 | Vehicle identification code detection method, device, computer equipment and storage medium |
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| CN110807396B (en) * | 2019-10-28 | 2022-04-22 | 华南理工大学 | Face changing video tampering detection method and system based on illumination direction consistency |
| CN110807396A (en) * | 2019-10-28 | 2020-02-18 | 华南理工大学 | Face-changing video tampering detection method and system based on consistency of illumination direction |
| CN110826551A (en) * | 2019-11-04 | 2020-02-21 | 大连交通大学 | Intelligent discrimination method for VIN code rubbing die image of motor vehicle |
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| CN111062931A (en) * | 2019-12-20 | 2020-04-24 | 河北工业大学 | Detection method of spliced and tampered image |
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| CN111144475A (en) * | 2019-12-22 | 2020-05-12 | 上海眼控科技股份有限公司 | Method and device for determining car seat, electronic equipment and readable storage medium |
| CN112418214A (en) * | 2020-11-06 | 2021-02-26 | 北京五八信息技术有限公司 | Vehicle identification code identification method and device, electronic equipment and storage medium |
| CN112418214B (en) * | 2020-11-06 | 2024-07-23 | 北京爱上车科技有限公司 | Vehicle identification code identification method and device, electronic equipment and storage medium |
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