CN110930724A - Traffic off-site illegal record screening and auditing method and system based on deep learning - Google Patents
Traffic off-site illegal record screening and auditing method and system based on deep learning Download PDFInfo
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- 238000012216 screening Methods 0.000 title claims abstract description 37
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- 230000037430 deletion Effects 0.000 claims abstract description 25
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- 238000005516 engineering process Methods 0.000 claims description 6
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Abstract
The invention relates to the technical field of intelligent traffic management, in particular to a method and a system for screening and auditing traffic off-site illegal records based on deep learning. The method comprises the following steps: acquiring off-site illegal record information uploaded by a front end; determining corresponding illegal action item information and illegal evidence image information according to the off-site illegal record information; acquiring at least one pre-stored image data of a scrap deletion scene according to the illegal activity item information; comparing the illegal evidence image information corresponding to the illegal action item information with the image data of the waste film deletion scene one by one; and if the illegal evidence image information is consistent with at least one piece of image data of the scrap deletion scene, determining that the illegal evidence image is a scrap. Wherein the system is adapted to perform the above method. The invention can identify and automatically delete the invalid illegal evidence photos and retrieve the off-site illegal records deleted by mistake and by mistake.
Description
Technical Field
The invention relates to the technical field of intelligent traffic management, in particular to a method and a system for screening and auditing traffic off-site illegal records based on deep learning.
Background
In recent years, more and more electronic police equipment is used by traffic control departments to check motor vehicle law violation, law enforcement and evidence collection equipment (electronic monitoring equipment such as overspeed snapshot, red light running snapshot, retrograde snapshot and the like) is built to present a rapidly increasing situation, and motor vehicle law violation collected by the law enforcement and evidence collection equipment is that the motor vehicle law violation is off-site law. According to statistics, the quantity of the traffic illegal behaviors in China is hundreds of millions, and more than 80 percent of the traffic illegal behaviors are non-field illegal.
At present, the screening and auditing method of off-site illegal activities is to carry out manual screening and auditing by policemen. The method has obvious disadvantages, mainly including the following three points. Firstly, the amount of off-site illegal data is huge, and a large amount of policemen are required to carry out daily screening and auditing work. Secondly, the law enforcement and evidence obtaining equipment has a large number of waste films in the non-field illegal snapshot due to factors such as non-uniform accuracy, uneven imaging effect of the equipment, difficulty in obtaining evidence under the influence of light rays in the tunnel and at night, inaccurate illegal recognition algorithm and the like, and the data lead to generally low screening and auditing efficiency of policemen. Thirdly, in the screening and auditing process, the result is greatly influenced by human factors, and the condition that effective off-site illegal records are deleted as waste films exists.
Disclosure of Invention
In order to solve the defects in the prior art, the invention provides a deep learning-based screening and auditing method and system for traffic off-site illegal records, which can identify and automatically delete invalid illegal evidence photos and retrieve the wrongly deleted off-site illegal records.
According to the technical scheme provided by the invention, as a first aspect of the invention, a method for screening and auditing traffic off-site illegal records based on deep learning is provided, which comprises the following steps:
acquiring off-site illegal record information uploaded by a front end;
determining corresponding illegal action item information and illegal evidence image information according to the off-site illegal record information;
acquiring at least one pre-stored image data of a scrap deletion scene according to the illegal activity item information;
comparing the illegal evidence image information corresponding to the illegal action item information with the image data of the waste film deletion scene one by one;
and if the illegal evidence image information is consistent with at least one piece of image data of the scrap deletion scene, determining that the illegal evidence image is a scrap.
Further, one piece of illegal activity item information corresponds to at least one piece of scene image data deleted by the scrap.
Further, the comparing the illegal evidence image information corresponding to the illegal activity item information with the scrap deletion scene image data one by one includes:
and comparing the illegal evidence image information corresponding to the illegal action item information with the image data of the scrap deletion scene one by utilizing an AI image recognition technology.
Further, after determining that the evidence of violation image is a scrap, the following steps are also performed:
and determining that the off-site illegal recording information is in a deletable state, and transferring the off-site illegal recording information to a historical record database.
Further, after determining that the evidence of violation image is a scrap, the following steps are also performed:
acquiring off-site illegal recording information corresponding to the illegal evidence image determined as the waste film;
acquiring at least one pre-stored scrap finding scene image data according to the illegal action item information;
comparing the illegal evidence image information determined as the waste film with the scene image data retrieved by the waste film one by one;
if the illegal evidence image information is consistent with at least one piece of scrap finding scene image data, changing the state of the off-site illegal record information into a state to be repeatedly checked;
and rolling back the off-site illegal record information from the historical record database, and transferring the rolled back off-site illegal record information to a database to be repeatedly checked.
Further, the comparing the illegal evidence image information determined as the waste film with the scene image data retrieved by the waste film one by one includes:
and comparing the illegal evidence image information determined as the waste film with the scene image data retrieved by the waste film one by utilizing an AI image recognition technology.
Further, after the illegal evidence image information corresponding to the illegal action item information is compared with the image data of the waste film deleting scene one by one, if the illegal evidence image information is inconsistent with the image data of the waste film deleting scene, the offsite illegal record information is determined to be in a to-be-repeatedly-checked state, and the offsite illegal record information is transferred to a to-be-repeatedly-checked database.
And further, manually screening and checking the off-site illegal record information in the database to be repeatedly checked.
As a second aspect of the present invention, a deep learning-based screening and auditing system for traffic offsite illegal records is provided, which is characterized in that the deep learning-based screening and auditing system for traffic offsite illegal records is used for executing the deep learning-based screening and auditing method for traffic offsite illegal records according to the first aspect of the present invention.
Compared with the prior art, the method and the system for screening and auditing the traffic off-site illegal records based on deep learning have the following advantages:
and comparing the pre-stored scene image data deleted by the waste film with the illegal evidence image information to determine whether the illegal evidence image information is the waste film. And then can filter the off-site illegal record that does not conform to law enforcement evidence collection standard automatically, the off-site illegal record number that the policeman actually need artifical screening to examine and verify will significantly reduce, policeman's work efficiency is showing and is promoting.
Drawings
Fig. 1 is a flow chart of example 1 of the first aspect of the present invention.
Fig. 2 is a flow chart of embodiment 2 of the first aspect of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to specific embodiments and the accompanying drawings. In which like parts are designated by like reference numerals. It should be noted that the words "front", "rear", "left", "right", "upper" and "lower" used in the following description refer to directions in the drawings. The terms "inner" and "outer" are used to refer to directions toward and away from, respectively, the geometric center of a particular component.
Example 1 as the first aspect of the present invention
A deep learning-based screening and auditing method for traffic off-site illegal records is provided, as shown in FIG. 1, and comprises the following steps
S1: acquiring off-site illegal record information uploaded by a front end; the off-site illegal recording information comprises corresponding illegal activity item information and illegal evidence image information, the illegal activity item information is obtained, further text information of illegal activities can be obtained, and the illegal evidence image information is obtained, further, evidence image information used for proving specific illegal activities can be obtained;
s2: determining corresponding illegal action item information and illegal evidence image information according to the off-site illegal record information;
s3: acquiring at least one pre-stored image data of a scrap deletion scene according to the illegal activity item information;
s4: comparing the illegal evidence image information corresponding to the illegal action item information with the image data of the waste film deletion scene one by one; the scene image data deleted by the waste film is off-site illegal recording image recording data which does not accord with law enforcement evidence collection standards.
S5: and if the illegal evidence image information is consistent with at least one piece of image data of the scrap deletion scene, determining that the illegal evidence image is a scrap.
An example is given below to illustrate that the scrap deletion scene image data includes scenes:
not illegal; the card is not identified; the number plate hanging area is incomplete; no vehicle was tracked; road occupation construction, traffic police command and unclear marking; a single non-composite picture; a temporary license plate, the vehicle is not found; the number plate is stained or not hung; a special vehicle; the user-friendly use is stopped; leaf shielding; darkness at night; a picture video problem; taking a non-motor vehicle without pictures or videos; the picture equipment position information is wrong; overlaying illegal information errors and snapshotting that the vehicles are inconsistent; fake cards and fake cards; a vehicle is scrapped; the illegal vehicle is blocked; testing data and repeating data; non-yellow-mark vehicles; has been punished on site; no video path; test data are shot in the absence; data is expired; the illegal time is before the vehicle inspection; incomplete front features; the rear features of the vehicle are incomplete; the front features of the car are unclear; the rear part features are unclear; manually cancelling; intentionally shielding the license plate; a failure of the device; the unlawful act occurs before the vehicle transfer registration; and others.
And (3) red light running deleting scene: no red light running; the second three pictures have no obvious displacement; the driving direction is not a red light; signal lamp blur (chromatic aberration); the stop line is unclear; at least one other than red light; the signal lamp is shielded; the first graph shows a stop line, and the third graph does not cross the stop line; the third picture does not track the target vehicle; finally, the terminal does not pass through the road; a large-sized vehicle; seconds without red lights or 0; the straight green light enters a left-turning waiting area; the vehicle turns right without a right turn red light and faces to the coming vehicle.
And comparing the illegal evidence images with the scene image data one by one to determine that the illegal evidence image information is consistent with at least one piece of the scrap deleted scene image data, and determining that the illegal evidence images are scrap.
It can be understood that, by comparing the pre-stored image data of the scene deleted by the waste film with the image information of the evidence of violation, it is determined whether the image information of the evidence of violation is a waste film. And then can filter the off-site illegal record that does not conform to law enforcement evidence collection standard automatically, the off-site illegal record number that the policeman actually need artifical screening to examine and verify will significantly reduce, policeman's work efficiency is showing and is promoting.
And deleting scene image data corresponding to at least one piece of the scrap according to the illegal activity item information. See above for an example of a scene comprised by the scrap deleted scene image data.
For S4: and comparing the illegal evidence image information corresponding to the illegal activity item information with the scrap deletion scene image data one by one, and comparing the illegal evidence image information corresponding to the illegal activity item information with the scrap deletion scene image data one by adopting an AI image recognition technology.
At S5: if the illegal evidence image information is consistent with at least one piece of the image data of the scrap deletion scene, determining that the illegal evidence image is a scrap, and then performing the following steps:
s6: and determining that the off-site illegal recording information is in a deletable state, and transferring the off-site illegal recording information to a historical record database.
Example 2 as the first aspect of the present invention
The method for screening and auditing the traffic off-site illegal records based on deep learning is provided, and S1-S6 are the same as those in embodiment 1.
In order to improve the accuracy of screening and confirming that the illicit evidence image is a waste film, after determining that the illicit evidence image is a waste film, as shown in fig. 2, embodiment 2 further includes the following steps:
s7: acquiring off-site illegal recording information corresponding to the illegal evidence image determined as the waste film;
s8: acquiring at least one pre-stored scrap finding scene image data according to the illegal action item information;
s9: comparing the illegal evidence image information determined as the waste film with the scene image data retrieved by the waste film one by one;
s10: if the illegal evidence image information is consistent with at least one piece of scrap finding scene image data, changing the state of the off-site illegal record information into a state to be repeatedly checked;
s11: and rolling back the off-site illegal record information from the historical record database, and transferring the rolled back off-site illegal record information to a database to be repeatedly checked.
For S8: comparing the illegal evidence image information determined as the waste film with the scene image data retrieved by the waste film one by one, comprising: and comparing the illegal evidence image information determined as the waste film with the scene image data retrieved by the waste film one by utilizing an AI image recognition technology.
And S7 to S11 are in a mode of rolling back the on-site illegal recording AI waste films, namely when the illegal deletion and the illegal deletion of the off-site illegal recording information are carried out, the waste films can be rolled back from the historical record database through AI, the rolled-back off-site illegal recording information is transferred to a database to be repeatedly checked, and manual re-screening and checking are waited.
As a second aspect of the present invention, a traffic off-site illegal record screening and auditing system based on deep learning is provided, which is used for executing the traffic off-site illegal record screening and auditing method based on deep learning of the first aspect of the present invention.
The traffic off-site illegal record screening and auditing system based on deep learning compares the image data of the scene deleted by the pre-stored waste film with the image information of the illegal evidence, so as to determine whether the image information of the illegal evidence is the waste film. And then can filter the off-site illegal record that does not conform to law enforcement evidence collection standard automatically, the off-site illegal record number that the policeman actually need artifical screening to examine and verify will significantly reduce, policeman's work efficiency is showing and is promoting.
Those of ordinary skill in the art will understand that: the above description is only exemplary of the present invention and should not be construed as limiting the present invention, and any modifications, equivalents, improvements and the like made within the spirit of the present invention should be included in the scope of the present invention.
Claims (9)
1. A traffic off-site illegal record screening and auditing method based on deep learning is characterized by comprising the following steps:
acquiring off-site illegal record information uploaded by a front end;
determining corresponding illegal action item information and illegal evidence image information according to the off-site illegal record information;
acquiring at least one pre-stored image data of a scrap deletion scene according to the illegal activity item information;
comparing the illegal evidence image information corresponding to the illegal action item information with the image data of the waste film deletion scene one by one;
and if the illegal evidence image information is consistent with at least one piece of image data of the scrap deletion scene, determining that the illegal evidence image is a scrap.
2. The method for screening and auditing traffic off-site illegal records based on deep learning of claim 1, characterized in that one item of illegal activity item information corresponds to at least one piece of scene image data of the junk deletion.
3. The deep learning-based screening and auditing method for traffic off-site illegal records according to claim 1, characterized in that comparing the illegal evidence image information corresponding to the illegal activity item information with the image data of the waste film deletion scene one by one comprises:
and comparing the illegal evidence image information corresponding to the illegal action item information with the image data of the scrap deletion scene one by utilizing an AI image recognition technology.
4. The method for screening and auditing traffic off-site illegal records based on deep learning of claim 1, characterized by further performing the following steps after determining that the illegal evidence image is a scrap:
and determining that the off-site illegal recording information is in a deletable state, and transferring the off-site illegal recording information to a historical record database.
5. The deep learning-based screening and auditing method for traffic off-site illegal records according to claim 4, characterized in that after determining that the illegal evidence image is a scrap, the following steps are further performed:
acquiring off-site illegal recording information corresponding to the illegal evidence image determined as the waste film;
acquiring at least one pre-stored scrap finding scene image data according to the illegal action item information;
comparing the illegal evidence image information determined as the waste film with the scene image data retrieved by the waste film one by one;
if the illegal evidence image information is consistent with at least one piece of scrap finding scene image data, changing the state of the off-site illegal record information into a state to be repeatedly checked;
and rolling back the off-site illegal record information from the historical record database, and transferring the rolled back off-site illegal record information to a database to be repeatedly checked.
6. The method for screening and auditing traffic off-site illegal records based on deep learning of claim 5, wherein the step of comparing the illegal evidence image information determined as a waste with the scene image data retrieved from the waste one by one comprises the following steps:
and comparing the illegal evidence image information determined as the waste film with the scene image data retrieved by the waste film one by utilizing an AI image recognition technology.
7. The method for screening and auditing traffic off-site illegal records based on deep learning of claim 1, characterized in that after the illegal evidence image information corresponding to the illegal activity item information is compared one by one with the image data of the scene of the deletion of the waste film, if the illegal evidence image information is not consistent with the image data of the scene of the deletion of the waste film, the off-site illegal record information is determined to be in a state of being repeatedly audited, and the off-site illegal record information is transferred to a database to be repeatedly audited.
8. The method for screening and auditing traffic off-site illegal records based on deep learning of claim 5 or 7, characterized in that the off-site illegal record information in the database to be audited is manually screened and audited.
9. A traffic off-site illegal record screening and auditing system based on deep learning is characterized in that the traffic off-site illegal record screening and auditing system based on deep learning is used for executing the traffic off-site illegal record screening and auditing method based on deep learning according to any one of claims 1-7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112509325A (en) * | 2020-12-04 | 2021-03-16 | 公安部交通管理科学研究所 | Video deep learning-based off-site illegal automatic discrimination method |
| CN113627414A (en) * | 2021-08-13 | 2021-11-09 | 黄丽雅 | Evidence collection and recording method and device |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP3426123B2 (en) * | 1997-11-07 | 2003-07-14 | 小糸工業株式会社 | Violating vehicle analyzer |
| US6754663B1 (en) * | 1998-11-23 | 2004-06-22 | Nestor, Inc. | Video-file based citation generation system for traffic light violations |
| CN1529232A (en) * | 2003-10-13 | 2004-09-15 | 南京三宝科技股份有限公司 | Red-light-running photo auto-screening method |
| WO2016107474A1 (en) * | 2014-12-30 | 2016-07-07 | 清华大学 | Vehicle checking method and system |
| CN109344805A (en) * | 2018-10-29 | 2019-02-15 | 北京深瞐科技有限公司 | Traffic offence picture intelligence preliminary hearing method and device |
| CN109637151A (en) * | 2018-12-31 | 2019-04-16 | 上海眼控科技股份有限公司 | A kind of recognition methods that highway Emergency Vehicle Lane is driven against traffic regulations |
| CN109831699A (en) * | 2018-12-28 | 2019-05-31 | 广州华多网络科技有限公司 | Image audit processing method, device, electronic equipment and storage medium |
| CN110009913A (en) * | 2019-03-27 | 2019-07-12 | 江苏智通交通科技有限公司 | A kind of non-at-scene law enforcement picture intelligent checks method and system of vehicles running red light |
-
2019
- 2019-12-09 CN CN201911247694.8A patent/CN110930724A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP3426123B2 (en) * | 1997-11-07 | 2003-07-14 | 小糸工業株式会社 | Violating vehicle analyzer |
| US6754663B1 (en) * | 1998-11-23 | 2004-06-22 | Nestor, Inc. | Video-file based citation generation system for traffic light violations |
| CN1529232A (en) * | 2003-10-13 | 2004-09-15 | 南京三宝科技股份有限公司 | Red-light-running photo auto-screening method |
| WO2016107474A1 (en) * | 2014-12-30 | 2016-07-07 | 清华大学 | Vehicle checking method and system |
| CN109344805A (en) * | 2018-10-29 | 2019-02-15 | 北京深瞐科技有限公司 | Traffic offence picture intelligence preliminary hearing method and device |
| CN109831699A (en) * | 2018-12-28 | 2019-05-31 | 广州华多网络科技有限公司 | Image audit processing method, device, electronic equipment and storage medium |
| CN109637151A (en) * | 2018-12-31 | 2019-04-16 | 上海眼控科技股份有限公司 | A kind of recognition methods that highway Emergency Vehicle Lane is driven against traffic regulations |
| CN110009913A (en) * | 2019-03-27 | 2019-07-12 | 江苏智通交通科技有限公司 | A kind of non-at-scene law enforcement picture intelligent checks method and system of vehicles running red light |
Non-Patent Citations (1)
| Title |
|---|
| 何瑞华等: "非现场违法记录智能筛选审核的应用研究", 《科学与信息化》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112509325A (en) * | 2020-12-04 | 2021-03-16 | 公安部交通管理科学研究所 | Video deep learning-based off-site illegal automatic discrimination method |
| CN113627414A (en) * | 2021-08-13 | 2021-11-09 | 黄丽雅 | Evidence collection and recording method and device |
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Application publication date: 20200327 |