CN110826456A - Countdown board fault detection method and system - Google Patents
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Abstract
The embodiment provides a countdown board fault detection method and system, which relate to the technical field of image recognition and comprise the steps of acquiring a monitoring video of a countdown board from an electronic monitoring platform in real time according to identification information of the countdown board; extracting a frame of monitoring image from the monitoring video every interval of first preset time, and identifying the characteristic information of the countdown board in the monitoring image through a preset image identification model; and determining the fault type of the countdown board according to the characteristic information. The countdown board fault detection method and system provided by the embodiment can detect the lighting fault of the countdown board, can also detect the offset fault and the shielding fault, and have a large coverage range of fault detection.
Description
Technical Field
The application relates to the technical field of image recognition, in particular to a countdown board fault detection method and system.
Background
In road traffic, the countdown board of traffic signal lamp can show the time that the signal that current signal lamp instructed can also last, helps maintaining traffic order, guarantees vehicle and pedestrian's trip safety. Therefore, failure detection of the countdown board is necessary.
At present, a commonly used method for detecting a failure of a countdown board is to determine whether the failure exists according to a working voltage value or a current value of the countdown board. Specifically, if the voltage value or the current value is not within the preset voltage value or current value range, the countdown board is considered to be extinguished. However, the above method cannot detect other faults of the countdown board, such as blocking by foreign objects, abnormal digital display, and deviation, and the coverage of fault detection is small.
Disclosure of Invention
The application provides a countdown board fault detection method and system, which are used for solving the problem that the countdown board fault detection coverage range is small in the prior art.
In a first aspect, the present embodiment provides a countdown board fault detection method, which is applied to a countdown board fault detection system, and the method includes:
acquiring a monitoring video of the countdown board from an electronic monitoring platform in real time according to the identification information of the countdown board;
extracting a frame of monitoring image from the monitoring video every interval of first preset time, and identifying the characteristic information of the countdown board in the monitoring image through a preset image identification model;
and determining the fault type of the countdown board according to the characteristic information.
In a first implementation manner of the first aspect, identifying, by a preset image recognition model, feature information of a countdown board in the monitoring image includes:
determining an identification area of the monitoring image according to a pre-calibrated countdown board area in a reference image of the countdown board; and determining the characteristic information of the countdown board according to the image in the identification area.
In a second implementation manner of the first aspect, the determining the fault type of the countdown board according to the characteristic information includes: determining a lighting fault according to the color of the lamp holder or display information; or, determining the shielding fault according to the characteristic information; alternatively, an offset fault is determined based on the location information.
In a third implementation manner of the first aspect, the determining a lighting fault according to a color of a lamp holder or display information specifically includes:
if the characteristic information in the second preset time is not empty and the color of the lamp holder is not included in the characteristic information, determining that the countdown board has a lamp-on fault; or,
and if the characteristic information in the second preset time is not empty and the display information in the characteristic information in the second preset time is different from the preset display information, determining that the countdown board has a lighting fault.
In a fourth implementation manner of the first aspect, determining the failure type of the countdown board according to the feature information includes:
and if the characteristic information in the second preset time is empty, determining that the countdown board has a shielding fault.
In a fifth implementation manner of the first aspect, the determining, according to the location information, an offset fault of the countdown board specifically includes:
and comparing the position information with a reference position in a reference image of the countdown board, and if the difference value between the position information and the reference position exceeds a preset range, determining that the countdown board has an offset fault.
In a sixth implementation manner of the first aspect, the acquiring, in real time, the monitoring video of the countdown board from the electronic monitoring platform according to the identification information of the countdown board includes:
and if the countdown board has preset working time and the current time is within the working time range, acquiring the monitoring video of the countdown board from the electronic monitoring platform in real time according to the identification information of the countdown board.
In a seventh implementation manner of the first aspect, the method further includes: and sending fault information to an operation and maintenance management platform, wherein the operation and maintenance management platform is used for managing and maintaining the countdown board, and the fault information comprises the fault type and fault related information.
In an eighth implementation manner of the first aspect, the image recognition model is determined by:
acquiring a plurality of sample images of different environmental parameters from the electronic monitoring platform, and labeling the characteristic information of the countdown board in each sample image, wherein each sample image comprises an image of the countdown board;
forming a training sample library by the labeled sample images;
and generating an image recognition model according to a deep learning algorithm, and training the image recognition model to recognize the characteristic information of the countdown board according to the training sample library.
In a second aspect, the present embodiment provides a countdown board fault detection system, including a fault detection server and a fault determination server, wherein,
the fault detection server is used for acquiring a monitoring video of the countdown board from the electronic monitoring platform in real time according to the identification information of the countdown board;
extracting a frame of monitoring image from the monitoring video every interval of first preset time, and identifying the characteristic information of the countdown board in the monitoring image through a preset image identification model;
the failure determination server is configured to,
and determining the fault type of the countdown board according to the characteristic information.
According to the method and the system for detecting the failure of the countdown board, provided by the embodiment of the application, the monitoring video collected by the electronic monitoring platform is further processed by utilizing an image recognition technology, the characteristic information of the countdown board is determined, and the failure type of the countdown board is judged according to the characteristic information. The method can detect the lighting fault of the countdown board, can also detect the offset fault and the shielding fault, and has a large coverage range of fault detection.
Drawings
In order to more clearly explain the technical solution of the present application, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious to those skilled in the art that other drawings can be obtained according to the drawings without any creative effort.
Fig. 1 is a flowchart illustrating a method for determining an image recognition model according to the present embodiment;
FIG. 2 is a flowchart illustrating a countdown board fault detection method according to the present embodiment;
fig. 3 is a frame of monitoring image shown in the present embodiment;
fig. 4 is a reference image shown in the present embodiment;
fig. 5 is a schematic structural diagram of a countdown board fault detection system shown in this embodiment;
fig. 6 is a schematic view of an application scenario of the countdown board fault detection system shown in this embodiment.
Detailed Description
To make the objects, technical solutions and advantages of the exemplary embodiments of the present application clearer, the technical solutions in the exemplary embodiments of the present application will be clearly and completely described below with reference to the drawings in the exemplary embodiments of the present application, and it is obvious that the described exemplary embodiments are only a part of the embodiments of the present application, but not all the embodiments.
At present, in order to facilitate traffic management and maintain traffic order, electronic monitoring devices are installed on each branch road of most of the intersections (such as three-branch intersections, crossroads and five-branch intersections). The electronic monitoring device is used for collecting monitoring videos and sending the monitoring videos to the electronic monitoring platform for unified management so as to monitor the motion conditions of vehicles and pedestrians at each fork road under the indication of the corresponding signal lamp. Because the monitoring videos collected by the electronic monitoring device usually include images of traffic lights, and most of the traffic lights are usually configured with countdown boards, each monitoring video usually includes an image of the countdown board.
Based on this, the embodiment of the application provides a countdown board fault detection method and system, which are used for detecting the working condition of the countdown board at a road intersection in real time according to the image of the countdown board in a monitoring video.
The operation of the countdown board fault detection method and system provided by the embodiment depends on an image recognition model to recognize the characteristic information of the countdown board in the monitoring image, such as shape information, position information, lamp head color, display information and the like. Therefore, the embodiment of the present application first describes the image recognition model in detail.
Referring to fig. 1, an image recognition model provided in the embodiment of the present application is determined through the following steps S101 to S103.
Step S101, obtaining a plurality of sample images of different environmental parameters from an electronic monitoring platform, and labeling characteristic information of a countdown board in each sample image, wherein each sample image comprises an image of the countdown board.
For example, a plurality of monitoring videos with different environmental parameters (such as sunny days, rainy days, foggy days, morning, evening, and the like) can be obtained from the electronic monitoring platform, and are subjected to preprocessing such as video coding and decoding, image denoising, enhancement, and the like, and some monitoring images are extracted from the videos to be used as sample images.
After the sample images are determined, the feature information in the sample images may be labeled manually. The characteristic information may include: position information (such as the position of a pixel point of the countdown board in the whole monitoring image), shape information (such as a circle and a square), information such as lamp cap colors (such as red, green and yellow), display information (such as 1, 2 and 3) and the like.
And S102, forming a training sample library by the labeled sample images.
And S103, generating an image recognition model according to a deep learning algorithm, and training the image recognition model to recognize the characteristic information of the countdown board according to a training sample library.
For example, a Spatial Regularization Network (SRN) may be employed to learn the relationships between multiple labels of a sample image. Because the shape of the countdown board is specified in the national standard, the SRN only utilizes image-level supervision information to learn the semantic and spatial relationship among the labels, and achieves the aim of identifying the characteristic information of the countdown board.
An image recognition model for recognizing the feature information of the countdown boards in the image can be obtained through the above steps S101 to S103.
Referring to fig. 2, an embodiment of the present application provides a countdown board fault detection method applied to a countdown board fault detection system, which includes the following steps S201 to S205.
Step S201, the fault detection system receives a fault detection instruction sent by the user equipment, where the fault detection instruction carries identification information of the countdown board.
The fault detection system provided by the embodiment can be accessed to the user equipment and executes corresponding operation according to the instruction of the user equipment. For example, a Personal Computer (PC) client may be disposed on the user equipment, and the PC client is used as a tool for human-computer interaction, and is capable of controlling the PC to generate a fault detection instruction according to a user instruction and sending the fault detection instruction to the fault detection system.
The fault detection instruction carries identification information of the countdown board and can also carry a fault type to be detected, and the instruction is used for indicating a fault detection system to execute corresponding fault detection operation according to the identification information and the fault detection type. The identification information of the countdown board is used for uniquely indicating the countdown board, and may be a number, a name, an address, and the like of the countdown board. The fault type includes at least one of a light-on fault, an offset fault, and a shading fault.
It should be noted that, in one fault detection instruction, the number of the countdown boards may be one, or may be multiple, for example, all the traffic countdown boards on a certain line, all the traffic countdown boards in a certain area, and the like.
And S202, the fault detection system acquires the monitoring video of the countdown board from the electronic monitoring platform in real time according to the fault detection instruction.
Because the electronic monitoring platform stores monitoring videos shot by different electronic monitoring devices, and each monitoring video usually comprises an image of a countdown board of a specific fork, a one-to-one correspondence relationship exists between the monitoring videos and the countdown boards. Therefore, the fault detection system can acquire the monitoring video of the countdown board from the electronic monitoring platform in real time according to the identification information of the countdown board and the corresponding relation between the monitoring video and the countdown board.
At present, most of the countdown boards work continuously for 24 hours, but a small number of the countdown boards only work in preset work time, for example, the countdown boards at the school doorway may only work in peak periods of people flow for students to learn and put, such as time periods of 7:30am-7:45am, 12:00pm-12:15pm, 13:50pm-14:00pm, 17:30pm-17:40pm, and the like. Therefore, as an optional implementation manner, when the fault detection system obtains the monitoring video of the countdown board, it may first determine whether the countdown board has preset working time, and if the current time is within the preset working time range, obtain the monitoring video corresponding to the countdown board from the electronic monitoring platform.
Step S203, extracting a frame of monitoring image from the monitoring video every interval of first preset time by the fault detection system, and identifying the characteristic information of the countdown board in the monitoring image through a preset image identification model.
The first preset time may be 0.5S, 1S, 2S, and the like, and is determined according to a preset configuration, which is not limited in this embodiment.
Through a preset image recognition model, the fault detection system can perform pixel-level segmentation on a monitored image, accurately determine the position of the countdown board, perform feature extraction on the positioned position of the countdown board, and confirm whether the monitored image is the same target or not through comparison of partial features of the target to finish feature information recognition.
Taking the monitoring image exemplarily shown in fig. 3 as an example, the countdown board in the image lights up green, and the display information is 15. The position information of the countdown board in the monitoring image can be determined to be (x, y) through an image recognition model, wherein x belongs to [ x ∈ [ [ x ]1，x2]，y∈[y1，y2]. The countdown board is square, green in color and 15 in display information.
As an alternative embodiment, each countdown board has a preset reference image, and each reference image is labeled with a countdown board area in advance. For example, in the reference image shown in fig. 4, the black frame area is the pre-labeled countdown board area. These reference images are pre-stored in a reference image library of the fault detection system to be called by the fault detection system during image recognition.
When the image recognition model recognizes the feature information of the countdown board in the monitored image, the image recognition model can determine the area of the countdown board, which is marked in advance in the reference image of the countdown board, as the recognition area of the monitored image, recognize the image in the recognition area, and determine the feature information of the countdown board in the monitored image, so as to improve the recognition efficiency of the image. And if the characteristic information of the countdown board cannot be determined according to the identification area, identifying the whole monitoring image.
It should be noted that the reference image and the monitoring image have the same shooting conditions, including the same electronic monitoring device and the same shooting angle. The reference image is different from the monitoring image in that the monitoring image may be taken in different weather or time, such as rainy day, foggy day, night, etc., and the picture thereof may be unclear, whereas the reference image is taken in a good-sight condition and the picture thereof is clear.
And step S204, the fault detection system determines the fault type of the countdown board according to the characteristic information.
The countdown board fault detection method provided by the embodiment can determine the lighting fault, the offset fault and the shielding fault of the countdown board according to the characteristic information. The characteristic information used for determining different faults may be different, and a specific process thereof is described below.
(1) Determination of lighting failure
The fault detection system determines the lighting fault of the countdown board according to the color or display information of the lamp holder in the characteristic information of the countdown board, and the fault detection system is specifically as follows:
if the characteristic information of the countdown board is not empty within a second preset time (for example, 5S, 10S and the like), and the characteristic information does not include the color of the lamp holder, the fault detection system identifies the countdown board, but does not identify the color of the countdown board, so that the countdown board is determined to have the lighting fault.
And if the characteristic information of the countdown board is not empty within the second preset time and the display information in the characteristic information is different from the preset display information, determining that the lighting fault exists in the countdown board.
In one example, according to a preset setting, the countdown board should display the whole number of 50-1, for example, 10, 9, 8, 7, 6, but display 10, 8, 7, 6, or 60, 59, 58, 57, 56 within a second preset time, and at this time, it is determined that the countdown board has a lighting fault.
In another example, the display information of the countdown board is not a numerical value, for example, "H" is displayed, and at this time, it is determined that there is a lighting failure of the countdown board.
(2) Determination of occlusion failure
If all the characteristic information in the second preset time is null, the fact that the countdown board is not recognized in the monitoring video in the second preset time is indicated, and therefore the fact that the countdown board is shielded is determined.
(3) Determination of offset faults
And the fault detection system determines the offset fault of the countdown board according to the position information in the characteristic information of the countdown board.
Specifically, the position information is compared with a reference position in a reference image of the countdown board, and if the difference between the position information and the reference position exceeds a preset range, the countdown board is determined to have an offset fault.
In one example, the position information (x, y) of the countdown board in the monitored image is compared to a reference position (m, n) of the countdown board in a reference image, where m ∈ [ m ] m1，m2]，n∈[n1，n2]. If the difference between the position information and the reference position exceeds a predetermined range, e.g. | x1-m1| is ≧ Δ h, or | y1-n1And if the value is more than or equal to delta h, determining that the countdown board has an offset fault.
Step S205, the fault detection system sends the fault information of the countdown board to the user equipment, wherein the fault information comprises the fault type and the fault related information.
In the present embodiment, the failure information includes a failure type and failure-related information. The relevant information of the fault can comprise detection task description, fault pictures, fault codes, a fault intersection, an electronic monitoring device corresponding to a fault countdown board, shooting time of a monitoring video corresponding to the fault and the like.
After determining the failure information, the failure detection system sends it to the user equipment, and stores it in a local database, for example, a local FTP (File Transfer Protocol) server.
After receiving the fault information, the user device may display the fault information in a GIS (geographic information System) map, and/or a fault list for the user to view. Meanwhile, after the user examines the fault information, the user can send a detection stopping instruction of the fault countdown board to the fault detection system through the PC client of the user equipment.
As an optional implementation manner, the fault detection system can also send the fault information to an operation and maintenance management platform, and the operation and maintenance management platform is used for managing and maintaining the countdown boards, so that the operation and maintenance management personnel can maintain the fault countdown boards in time.
In addition, the user equipment can also send a fault misinformation message of the countdown board to the operation and maintenance management platform through the PC client, or carry out one-key guarantee operation and send the fault information to the operation and maintenance management platform.
The method for detecting the failure of the countdown board provided by the embodiment of the application utilizes an image recognition technology to further process a monitoring video acquired by an electronic monitoring platform, determines the characteristic information of the countdown board and judges the failure type of the countdown board according to the characteristic information. The method can detect the lighting fault of the countdown board, can also detect the offset fault and the shielding fault, and has a large coverage range of fault detection.
In addition, the fault detection method provided by the embodiment can detect the fault of the countdown board in real time, and timely discover and report the fault, so that maintenance personnel can timely maintain the fault countdown board, and traffic confusion and even traffic accidents caused by untimely maintenance are avoided.
Referring to fig. 5, a schematic structural diagram of a countdown board fault detection system exemplarily shown in this embodiment; and fig. 6 is a schematic view of an application scenario of the fault detection system.
The system for detecting a failure of a countdown board provided by the embodiment is used for executing the method for detecting the failure of the countdown board provided by the embodiment, and comprises a failure detection server and a failure determination server. The failure detection server is configured to perform the above steps S201 to S203, and the failure determination server is configured to perform the above steps S204 to S205.
The countdown board fault detection system provided by the embodiment of the application can detect the lighting fault of the countdown board, can also detect the offset fault and the shielding fault, and has the advantages of large coverage range of fault detection and high accuracy. Moreover, the system can detect the fault of the countdown board in real time, and timely discover and report the fault, so that maintenance personnel can maintain the fault countdown board in time, and avoid traffic confusion and even traffic accidents caused by untimely maintenance.
All other embodiments, which can be derived by a person skilled in the art from the exemplary embodiments shown in the present application without inventive effort, shall fall within the scope of protection of the present application. Moreover, while the disclosure herein has been presented in terms of exemplary one or more examples, it is to be understood that each aspect of the disclosure can be utilized independently and separately from other aspects of the disclosure to provide a complete disclosure.
It should be understood that the terms "first," "second," "third," and the like in the description and in the claims of the present application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used are interchangeable under appropriate circumstances and can be implemented in sequences other than those illustrated or otherwise described herein with respect to the embodiments of the application, for example.
Furthermore, the terms "comprises" and "comprising," as well as any variations thereof, are intended to cover a non-exclusive inclusion, such that a product or device that comprises a list of elements is not necessarily limited to those elements explicitly listed, but may include other elements not expressly listed or inherent to such product or device.
Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present application.
Claims (10)
1. A countdown board fault detection method is characterized by being applied to a countdown board fault detection system, and comprises the following steps:
acquiring a monitoring video of the countdown board from an electronic monitoring platform in real time according to the identification information of the countdown board;
extracting a frame of monitoring image from the monitoring video every interval of first preset time, and identifying the characteristic information of the countdown board in the monitoring image through a preset image identification model;
and determining the fault type of the countdown board according to the characteristic information.
2. The method according to claim 1, wherein the identifying the feature information of the countdown board in the monitoring image through a preset image recognition model comprises:
determining an identification area of the monitoring image according to a pre-calibrated countdown board area in a reference image of the countdown board; and determining the characteristic information of the countdown board according to the image in the identification area.
3. The method of claim 1 or 2, wherein the characteristic information comprises a lamp head color, display information and position information, and the determining the fault type of the countdown board according to the characteristic information comprises:
determining a lighting fault according to the color of the lamp holder or display information; or,
determining a shielding fault according to the characteristic information; or,
and determining the offset fault according to the position information.
4. The method of claim 3, wherein determining a light-up fault based on a color of a light head or display information comprises:
if the characteristic information in the second preset time is not empty and the color of the lamp holder is not included in the characteristic information, determining that the countdown board has a lamp-on fault; or,
and if the characteristic information in the second preset time is not empty and the display information in the characteristic information in the second preset time is different from the preset display information, determining that the countdown board has a lighting fault.
5. The method of claim 3, wherein determining an occlusion fault from the characterization information comprises:
and if the characteristic information in the second preset time is empty, determining that the countdown board has a shielding fault.
6. The method of claim 3, wherein determining an offset fault based on the location information comprises:
and comparing the position information with a reference position in a reference image of the countdown board, and if the difference value between the position information and the reference position exceeds a preset range, determining that the countdown board has an offset fault.
7. The method of claim 1, wherein the obtaining of the monitoring video of the countdown board from the electronic monitoring platform in real time according to the identification information of the countdown board comprises:
and if the countdown board has preset working time and the current time is within the working time range, acquiring the monitoring video of the countdown board from the electronic monitoring platform in real time according to the identification information of the countdown board.
8. The method of claim 1, further comprising:
and sending fault information to an operation and maintenance management platform, wherein the operation and maintenance management platform is used for managing and maintaining the countdown board, and the fault information comprises the fault type and fault related information.
9. The method of claim 1, wherein the image recognition model is determined by:
acquiring a plurality of sample images of different environmental parameters from the electronic monitoring platform, and labeling the characteristic information of the countdown board in each sample image, wherein each sample image comprises an image of the countdown board;
forming a training sample library by the labeled sample images;
and generating an image recognition model according to a deep learning algorithm, and training the image recognition model to recognize the characteristic information of the countdown board according to the training sample library.
10. A failure detection system for a countdown board is characterized by comprising a failure detection server and a failure judgment server, wherein,
the failure detection server is configured to,
acquiring a monitoring video of the countdown board from an electronic monitoring platform in real time according to the identification information of the countdown board;
extracting a frame of monitoring image from the monitoring video every interval of first preset time, and identifying the characteristic information of the countdown board in the monitoring image through a preset image identification model;
the failure determination server is configured to,
and determining the fault type of the countdown board according to the characteristic information.
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| CN112735159A (en) * | 2020-12-29 | 2021-04-30 | 武汉微创光电股份有限公司 | Fault monitoring method and device for signal lamp countdown timer |
| CN114422248A (en) * | 2022-01-20 | 2022-04-29 | 深信服科技股份有限公司 | Attack processing method, system, network security device and storage medium |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106650641A (en) * | 2016-12-05 | 2017-05-10 | 北京文安智能技术股份有限公司 | Traffic light positioning and identification method, device and system |
| CN107689158A (en) * | 2017-08-10 | 2018-02-13 | 五邑大学 | A kind of intellectual traffic control method based on image procossing |
| CN107993470A (en) * | 2016-10-26 | 2018-05-04 | 天津职业技术师范大学 | Count down traffic signal lamp condition detection method and the monitoring system based on the method |
| CN108556875A (en) * | 2018-06-27 | 2018-09-21 | 苏州立创致恒电子科技有限公司 | Train DAS (Driver Assistant System) and its application method |
| CN109064768A (en) * | 2018-09-28 | 2018-12-21 | 通号（长沙）轨道交通控制技术有限公司 | One kind being used for traffic signals facility staff realtime monitoring system and method |
| CN109410583A (en) * | 2018-11-29 | 2019-03-01 | 上海市城市建设设计研究总院（集团）有限公司 | Traffic information collection and analysis system and operation method based on automobile data recorder |
| CN109508580A (en) * | 2017-09-15 | 2019-03-22 | 百度在线网络技术（北京）有限公司 | Traffic lights recognition methods and device |
| CN109636777A (en) * | 2018-11-20 | 2019-04-16 | 广州方纬智慧大脑研究开发有限公司 | A kind of fault detection method of traffic lights, system and storage medium |
| CN109684900A (en) * | 2017-10-18 | 2019-04-26 | 百度在线网络技术（北京）有限公司 | Method and apparatus for exporting colouring information |
| CN109740412A (en) * | 2018-11-09 | 2019-05-10 | 浙江浩腾电子科技股份有限公司 | A kind of signal lamp failure detection method based on computer vision |
| CN109830114A (en) * | 2019-02-20 | 2019-05-31 | 华为技术有限公司 | Traffic lights based reminding method and device |
-
2019
- 2019-10-31 CN CN201911049994.5A patent/CN110826456A/en active Pending
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107993470A (en) * | 2016-10-26 | 2018-05-04 | 天津职业技术师范大学 | Count down traffic signal lamp condition detection method and the monitoring system based on the method |
| CN106650641A (en) * | 2016-12-05 | 2017-05-10 | 北京文安智能技术股份有限公司 | Traffic light positioning and identification method, device and system |
| CN107689158A (en) * | 2017-08-10 | 2018-02-13 | 五邑大学 | A kind of intellectual traffic control method based on image procossing |
| CN109508580A (en) * | 2017-09-15 | 2019-03-22 | 百度在线网络技术（北京）有限公司 | Traffic lights recognition methods and device |
| CN109684900A (en) * | 2017-10-18 | 2019-04-26 | 百度在线网络技术（北京）有限公司 | Method and apparatus for exporting colouring information |
| CN108556875A (en) * | 2018-06-27 | 2018-09-21 | 苏州立创致恒电子科技有限公司 | Train DAS (Driver Assistant System) and its application method |
| CN109064768A (en) * | 2018-09-28 | 2018-12-21 | 通号（长沙）轨道交通控制技术有限公司 | One kind being used for traffic signals facility staff realtime monitoring system and method |
| CN109740412A (en) * | 2018-11-09 | 2019-05-10 | 浙江浩腾电子科技股份有限公司 | A kind of signal lamp failure detection method based on computer vision |
| CN109636777A (en) * | 2018-11-20 | 2019-04-16 | 广州方纬智慧大脑研究开发有限公司 | A kind of fault detection method of traffic lights, system and storage medium |
| CN109410583A (en) * | 2018-11-29 | 2019-03-01 | 上海市城市建设设计研究总院（集团）有限公司 | Traffic information collection and analysis system and operation method based on automobile data recorder |
| CN109830114A (en) * | 2019-02-20 | 2019-05-31 | 华为技术有限公司 | Traffic lights based reminding method and device |
Non-Patent Citations (3)
| Title |
|---|
| 刘明峰;刘孙俊;郭顺森;李祥新;吴;: "基于DBN图像识别的机房巡检系统设计研究", no. 11, pages 51 - 55 * |
| 李聪;: "地铁信号系统智能运维方案设计", no. 02, pages 91 - 95 * |
| 邓超 等: "《数字图像处理与模式识别研究》", 北京 地质出版社, pages: 202 - 207 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112735159A (en) * | 2020-12-29 | 2021-04-30 | 武汉微创光电股份有限公司 | Fault monitoring method and device for signal lamp countdown timer |
| CN114422248A (en) * | 2022-01-20 | 2022-04-29 | 深信服科技股份有限公司 | Attack processing method, system, network security device and storage medium |
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