KR101671223B1 - Real-time noise analyzing system and a method for analyzing a real-time noise using the same - Google Patents

Real-time noise analyzing system and a method for analyzing a real-time noise using the same Download PDF

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KR101671223B1
KR101671223B1 KR1020150141324A KR20150141324A KR101671223B1 KR 101671223 B1 KR101671223 B1 KR 101671223B1 KR 1020150141324 A KR1020150141324 A KR 1020150141324A KR 20150141324 A KR20150141324 A KR 20150141324A KR 101671223 B1 KR101671223 B1 KR 101671223B1
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noise
information
unit
real
analyzing
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Korean (ko)
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노희민
정호성
윤혁진
이희업
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한국철도기술연구원
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
    • G01H9/00—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves by using radiation-sensitive means, e.g. optical means
    • G01H9/002—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves by using radiation-sensitive means, e.g. optical means for representing acoustic field distribution

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Abstract

In the real-time noise analysis system using image information and the noise analysis method using the same, the real-time noise analysis system includes a photographing unit, an image information analyzing unit, a noise prediction modeling unit, a synchronization unit, and a noise map generating unit. The photographing unit photographs a road or a railroad track. The image information analyzing unit analyzes the information of the noise caused by the noise from the image photographed by the photographing unit. The noise prediction modeling unit models the noise prediction based on the information of the object. The synchronization unit synchronizes the noise prediction modeling result with real time spatial information. The noise map generator generates the synchronized result as a noise map.

Figure R1020150141324

Description

TECHNICAL FIELD [0001] The present invention relates to a real-time noise analysis system using image information and a noise analysis method using the real-

The present invention relates to a real-time noise analysis system and a noise analysis method using the same, and more particularly, to a real-time noise analysis system that analyzes noises of a road or a railway by utilizing image information of a traveling vehicle or a railway vehicle, And a noise analysis method using the same.

Generally, urban noise map is mainly used to predict or analyze the influence of noise on various public institutions such as local governments, and to design urban planning based on this. In other words, it has been developed on the basis of acoustical analysis theory based on geometrical acoustics in order to analyze the influence of noise generated in railroads, roads, factories, etc.

FIG. 1 is an example of an urban noise map according to the prior art, which is an image showing a noise map of Cheongju city. As shown in Fig. 1, it can be seen that the noise level of the urban noise map is increased around the road where the vehicle is frequently driven.

In connection with the generation of such an urban noise map, Korean Patent Laid-Open Publication No. 10-2012-0014508 discloses a technique of sensing noise from a noise sensor and converting it into a DB and generating a noise map by visualizing the noise in 2D / 3D And Korean Patent Laid-Open Publication No. 10-2012-0055783 also discloses a technique of collecting noise data and visualizing the noise map.

However, in the case of the prior art, there is a disadvantage that it is inconvenient to directly install the noise sensor in a region where measurement is required, and time and cost are increased due to a direct measurement method using a noise sensor for measuring actual noise.

Further, in most of the urban noise maps developed up to now, there is a limitation in accurately predicting the noise since it does not reflect the speed or flow of the vehicle or the railway vehicle in the road or the railroad which varies in real time.

Korean Patent Publication No. 10-2012-0014508 Korean Patent Publication No. 10-2012-0055783

SUMMARY OF THE INVENTION Accordingly, the present invention has been made keeping in mind the above problems occurring in the prior art, and an object of the present invention is to provide a real-time noise analysis system with improved accuracy by predicting noise of a vehicle or a railway vehicle.

Another object of the present invention is to provide a noise analysis method using the real time noise analysis system.

According to an embodiment of the present invention, a real-time noise analysis system includes a photographing unit, an image information analyzing unit, a noise prediction modeling unit, a synchronization unit, and a noise map generating unit. The photographing unit photographs a road or a railroad track. The image information analyzing unit analyzes the information of the noise caused by the noise from the image photographed by the photographing unit. The noise prediction modeling unit models the noise prediction based on the information of the object. The synchronization unit synchronizes the noise prediction modeling result with real time spatial information. The noise map generator generates the synchronized result as a noise map.

In one embodiment, the photographing unit may be a CCTV that photographs only a predetermined region with respect to a predetermined region.

In one embodiment, the photographing unit may be a drone, which is an unmanned air vehicle equipped with a photographing unit.

In one embodiment, the noise-causing object is a railway vehicle that runs on a road or a railroad, and each noise-causing object may be assigned to a point sound source.

In one embodiment, the apparatus may further include a database for storing information on noise of the object, information on the position of the photographing unit, and the real-time spatial information.

In one embodiment, the image information analyzing unit may include an object identifying unit for identifying the type of the object, a speed analyzing unit for analyzing the speed of the object, and a path analyzing unit for analyzing the path of the object.

In one embodiment, the noise prediction modeling unit may classify information on noise, which is matched with the type, speed, and path of the object, to the database, based on information on the type of the object, the speed of the object, The noise prediction can be modeled.

In one embodiment, the synchronization unit may receive the information on the position of the photographing unit and the real-time spatial information from the database based on the noise prediction modeling result, and output the noise prediction modeling result according to the position of the photographing unit, Real-time spatial information.

In one embodiment, the noise map generator may generate a 3D noise map of the result synchronized with the real time spatial information to display the degree of noise.

According to another embodiment of the present invention, there is provided a method for real-time noise analysis, comprising the steps of: capturing an image of a road or a railroad track; analyzing information of a noise-causing object from the photographed image; Modeling the noise prediction based on the information, synchronizing the noise prediction modeling result with real-time spatial information, and generating the synchronized result as a noise map.

In one embodiment, the step of analyzing the information on the noise source object may include the steps of identifying the type of the object from the photographed image, analyzing the speed of the object from the photographed image, And analyzing the path of the object.

In one embodiment, in modeling the noise prediction, noise prediction may be modeled by receiving information on noise that matches the type, speed, and path of the object from the database.

In one embodiment, in synchronizing with the real-time spatial information, the noise prediction modeling result may be synthesized and synchronized with information on the position of the photographed image provided from the database and the real-time spatial information.

According to the embodiments of the present invention, since the noise map is generated through noise prediction based on the photographed image, the noise sensor for measuring the noise can be omitted, the existing CCTV image information can be utilized as it is, The generation of noise maps is easier. In particular, real-time noise map can be generated more easily because the information of the vehicle or the railway vehicle changing in real time through the CCTV image information is used as it is.

In contrast to this, instead of utilizing the existing CCTV image information, image information can be acquired through a drone, which is an unmanned airplane equipped with a video shooting unit. Since the drones acquire image information by flying at an arbitrary position, It is possible to obtain more accurate real-time noise map.

In this case, if the information of the target object is analyzed from the photographed image, and the information of the previously stored database is matched based on the data of the type, speed, and route of the target object, noise prediction modeling for the target object is instantaneously completed. Predictive modeling can be performed very efficiently and accurately.

In this case, since the traffic flow of the actual vehicle or the railway vehicle is immediately reflected, real-time noise prediction is possible.

Since the information about the position of the photographing unit and the real-time spatial information are stored in the database, the noise prediction modeling result in the photographing unit is synthesized and synchronized with the spatial information to generate a three-dimensional noise map synchronized with the real- So that the accuracy of the noise prediction and the real-time variability can be more accurately predicted.

1 is an image showing an example of an urban noise map according to the prior art.
2 is a block diagram illustrating a real time noise analysis system according to an embodiment of the present invention.
3 is a block diagram illustrating an image information analyzing unit, a noise prediction modeling unit, and a database in the real-time noise analysis system of FIG.
4 is a flowchart illustrating a real-time noise analysis method using the real-time noise analysis system of FIG.
5 is an image showing an example of a step of analyzing a flow of a target object based on the image information of FIG.
FIG. 6 is an image showing an example of a step of generating the noise prediction model of FIG.

While the present invention has been described in connection with what is presently considered to be the most practical and preferred embodiment, it is to be understood that the invention is not limited to the disclosed embodiments. It is to be understood, however, that the invention is not intended to be limited to the particular forms disclosed, but on the contrary, is intended to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the invention. Like reference numerals are used for like elements in describing each drawing. The terms first, second, etc. may be used to describe various components, but the components should not be limited by the terms.

The terms are used only for the purpose of distinguishing one component from another. The terminology used in this application is used only to describe a specific embodiment and is not intended to limit the invention. The singular expressions include plural expressions unless the context clearly dictates otherwise.

In the present application, the term "comprises" or "comprising ", etc. is intended to specify that there is a stated feature, figure, step, operation, component, But do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, parts, or combinations thereof.

Unless defined otherwise, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Terms such as those defined in commonly used dictionaries are to be interpreted as having a meaning consistent with the contextual meaning of the related art and are to be interpreted as either ideal or overly formal in the sense of the present application Do not.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings.

2 is a block diagram illustrating a real time noise analysis system according to an embodiment of the present invention. 3 is a block diagram illustrating an image information analyzing unit, a noise prediction modeling unit, and a database in the real-time noise analysis system of FIG. 4 is a flowchart illustrating a real-time noise analysis method using the real-time noise analysis system of FIG. 5 is an image showing an example of a step of analyzing a flow of a target object based on the image information of FIG. FIG. 6 is an image showing an example of a step of generating the noise prediction model of FIG.

Hereinafter, a real-time noise analysis system 10 according to the present embodiment will be described with reference to FIGS. 2 to 3. At the same time, a real-time noise analysis method using the real-time noise analysis system 10 will be described with reference to FIGS. .

The real-time noise analysis system 10 according to the present embodiment includes a noise source selection unit 100, a photographing unit 200, a database 300, an image information analysis unit 400, a noise prediction modeling unit 500, 600 and a noise map generator 700.

The noise source selection unit 100 selects a target object corresponding to a noise source. Generally, in the case of a noise map, the noise source selection unit 100 is implemented centering on a city center. Therefore, the noise source selection unit 100 can be applied to a vehicle, a bus, a truck, Railroad cars such as trams, high-speed trains, and general trains are selected as objects.

Thus, when the vehicle or the railway vehicle is selected as the object corresponding to the noise source in the noise source selection unit 100, the selected vehicle or railway vehicle is assumed to be a point sound source, .

When the noise source selecting unit 100 selects the object corresponding to the noise source, the photographing unit 200 photographs a road or a railroad running by the vehicle or the railway vehicle as the object (step S10).

The photographing unit is a video photographing device such as a CCTV installed on the road or a railway. The photographing unit is not required to be separately provided for the real-time noise analysis system according to the present embodiment, CCTV can be used as it detects the driving condition.

Of course, if CCTV is not installed in the area or section where shooting is required and it is difficult to shoot the entire area or all sections, additional CCTV equipment such as CCTV may be installed in the area or section where CCTV is not installed , The real-time noise analysis system according to the present embodiment can photograph all the areas or sections requiring noise analysis.

Alternatively, the photographing unit 200 may be a drone, which is an unmanned aerial vehicle equipped with an image photographing unit, such as a camera. In the case of the drone, since the photographing can be performed by flying to an arbitrary position, it is possible to arrange the drone in the section or section in which photographing is required, so that the image photographing can be performed at an arbitrary position in the section or section. Furthermore, by moving the drones to another zone or section and photographing the zone or section, it is possible to capture various images in various zones or sections.

Thus, a variety of image information can be obtained.

The photographing unit 200 is installed on a road or a railroad, and photographs the corresponding region or a corresponding region to acquire image information. The image information photographed by the photographing unit 200 includes a vehicle Or the image relating to the railway car is intact.

Thus, the image information analyzing unit 400 analyzes the information of a target object, for example, a vehicle or a railway vehicle selected as a noise source from the noise source selecting unit 100, from the image photographed by the photographing unit 200 S20).

Meanwhile, the photographing unit 200 is a plurality of photographs of the corresponding zone or the corresponding zone, and the image information analyzing unit 400 analyzes the photographing information of the plurality of zones or the zone, Information is analyzed.

More specifically, the image information analyzing unit 400 includes an object identifying unit 410, a speed analyzing unit 420, and a path analyzing unit 430.

The object identifying unit 410 identifies a vehicle or a railway vehicle as a target selected by the noise source in each zone or section photographed by the photographing unit 200, as shown in FIG.

That is, the object identifying unit 410 determines that the first object 401 is a general passenger car and the second and third objects 402 and 403 are general passenger cars in the photographed area or section, .

In addition, the speed analyzer 420 analyzes the speed of the vehicle or the railway vehicle, which is the object selected as the noise source, in each zone or section photographed by the photographing unit 200. For this, the speed analyzer 420 may utilize image information, that is, moving picture information, continuously photographed by the photographing unit 200 in the corresponding region or section.

That is, the speed analyzer 420 determines that the speed of the first object 401 is 70 km / h and the speed of the second object 402 is 60 km / h, for example, It is possible to analyze that the speed of the third object 403 is 80 km / h.

Further, the path analyzing unit 430 analyzes paths of vehicles or railway vehicles that are selected as the noise sources in each zone or section photographed by the photographing unit 200.

That is, the path analyzing unit 430 determines that the first and second objects 401 and 402 move in the south direction and the third object 403 moves in the north direction in the captured region or section Can be analyzed.

As described above, the image information analyzing unit 400 analyzes various information such as identifying the type of the object, analyzing the speed, or analyzing the path from the image photographed by the photographing unit 200 .

In this way, the information derived from the image information analyzing unit 400 is provided to the noise prediction modeling unit 500.

Thus, the noise prediction modeling unit 500 models the noise prediction based on the information of the object (step S30).

In this case, the noise prediction modeling unit 500 may utilize the information stored in the database 300 for the noise prediction modeling.

The database 300 stores or provides various information necessary for the operation of the real-time noise analysis system 10 according to the present embodiment. For example, the database 300 includes an object database 310 for storing information on noise of a target object, A photographing part DB 320 for storing information on the position of the photographing part 200, and a spatial information DB 330 for storing real time spatial information.

More specifically, the object DB 310 stores information on noise that varies depending on the type of vehicle or railway vehicle, the speed, and the travel path of the object.

For example, when the object is a truck, the noise is larger than other automobiles or motorcycles, so information about the noise is stored. Even if the same truck is used, the noise increases as the speed increases. The information about the size is stored, and the size of the noise also changes according to the direction in which the truck is traveling, so that information on the size of the noise can be stored.

The photographing unit DB 320 and the spatial information DB 330 will be described later.

As described above, the object database 310 stores information on noise in consideration of various situations for various objects, and the information thus stored is provided to the noise prediction modeling unit 500, .

That is, since the noise prediction modeling unit 500 receives the information on the type, speed, route, and the like of the object through the image information analyzing unit 400, Speed, and the magnitude of the noise matched to the path, and predicts and models the noise that is expected to occur from the object.

For example, as shown in FIG. 6, since the first object 401 is a passenger car traveling in a south direction at a speed of 70 km / h, information on the size of the corresponding noise is stored in the object DB 310, And predicts the first noise region 501 by the first object 401.

Likewise, since the second object 402 is a passenger car traveling in a south direction at a speed of 60 km / h, information on the corresponding noise level is received from the object DB 310, and the second object 402 ) Of the second noise region (502).

Also, since the third object 403 is a passenger car traveling in a north direction at a speed of 80 km / h, information on the corresponding noise level is received from the object database 310, and the third object 403 In the third noise region 503.

As described above, the noise prediction modeling unit 500 generates the noise prediction modeling unit 500 based on the analysis result of the image provided from the image information analysis unit 400 and the information about the object provided from the database 300, Or noise prediction in the corresponding section, and provides the noise prediction modeling result in the predicted region or the corresponding region to the synchronization unit 600.

The synchronization unit 600 synchronizes the noise prediction modeling result with real-time spatial information (step S40).

As described above, the database 300 further includes a photographing part DB 320 and a space information DB 330. The photographing part DB 320 includes a location where the photographing part 200 is located, And information on the direction in which the camera 200 captures images.

In addition, the spatial information DB 330 stores three-dimensional spatial information of a target region or a target region of a noise map to be generated, for example, information on a three-dimensional city map.

That is, the synchronization unit 600 receives information on the position and the photographing direction of the photographing unit 200 from the photographing unit DB 320, and acquires information on the three-dimensional space of the noise map area from the space information DB 330 Information is provided. Then, the information about the position and the direction of the photographing unit is input to the three-dimensional spatial information, and the result of modeling the noise prediction from the image photographed by the photographing unit is synchronized with the position and direction of the photographing unit And synthesizes them into the three-dimensional spatial information.

As a result, the modeling result of the noise prediction is synchronized with the spatial information. In this case, the spatial information includes real-time spatial information, so that the noise prediction modeling result is synchronized with the real-time spatial information.

Thereafter, the noise map generator 700 generates a real-time noise map based on the result of the synchronization through the synchronization unit 600 (step S50).

That is, the noise map generating unit 700 generates the noise prediction modeling result synchronized with the real-time spatial information through the synchronization unit 600 as a three-dimensional noise map, and displays the degree of noise to the outside .

Through this, the user can confirm the predicted noise map in real time.

Meanwhile, since the real-time noise analysis system 10 according to the present embodiment can update the information in real time to derive the result, the noise map generator 700 can also display a noise map updated in real time , The user can acquire information on the noise map predicted in real time as well as acquire a more accurate noise map.

According to the embodiments of the present invention, since the noise map is generated through noise prediction based on the photographed image, the noise sensor for measuring the noise can be omitted, the existing CCTV image information can be utilized as it is, The generation of noise maps is easier. In particular, real-time noise map can be generated more easily because the information of the vehicle or the railway vehicle changing in real time through the CCTV image information is used as it is.

In contrast to this, instead of utilizing the existing CCTV image information, image information can be acquired through a drone, which is an unmanned airplane equipped with a video shooting unit. Since the drones acquire image information by flying at an arbitrary position, It is possible to obtain more accurate real-time noise map.

In this case, if the information of the target object is analyzed from the photographed image, and the information of the previously stored database is matched based on the data of the type, speed, and route of the target object, noise prediction modeling for the target object is instantaneously completed. Predictive modeling can be performed very efficiently and accurately.

In this case, since the traffic flow of the actual vehicle or the railway vehicle is immediately reflected, real-time noise prediction is possible.

Since the information about the position of the photographing unit and the real-time spatial information are stored in the database, the noise prediction modeling result in the photographing unit is synthesized and synchronized with the spatial information to generate a three-dimensional noise map synchronized with the real- So that the accuracy of the noise prediction and the real-time variability can be more accurately predicted.

It will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the spirit or scope of the present invention as defined by the following claims. It can be understood that it is possible.

The real-time noise analysis system and the noise analysis method using the same according to the present invention have industrial applicability that can be used to generate urban noise maps.

10: Real-time noise analysis system
100: Noise source selection unit 200:
300: database 400: image information analysis unit
401, 402, 403: object 410: object identification unit
420: speed analyzer 430: path analyzer
500: Noise prediction modeling unit 600: Synchronization unit
501, 502, 503: predicted noise region
700: Noise map generator

Claims (13)

A photographing section for photographing a road or a railroad track;
An image information analyzing unit for analyzing information of a noise cause object from the image photographed by the photographing unit;
A noise prediction modeling unit for modeling noise prediction based on the information of the object;
A synchronization unit for synchronizing the noise prediction modeling result with real time spatial information; And
And a noise map generation unit for generating the synchronized result by a noise map. The method according to claim 1,
Wherein the photographing unit is a CCTV that photographs only a video image in a predetermined area. The method according to claim 1,
Wherein the photographing unit is a drone, which is an unmanned air vehicle equipped with a video photographing unit. The method according to claim 1,
The noise-causing object is a railway vehicle that runs on a road or a railway,
Wherein each of the noise cause objects is assigned to one point sound source. The method according to claim 1,
And a database for storing information about noise of the object, information about a position of the photographing unit, and the real-time spatial information. 6. The image processing apparatus according to claim 5,
An object identifying unit for identifying the type of the object;
A speed analyzer for analyzing the speed of the object; And
And a path analyzer for analyzing a path of the target object. 7. The apparatus of claim 6, wherein the noise prediction modeling unit comprises:
And noise prediction is provided from the database on the basis of information on the type of the object, the speed of the object, and the path of the object, Real-time noise analysis system. 6. The apparatus of claim 5,
Wherein the information about the position of the photographing unit and the real time spatial information are received from the database and the noise prediction modeling result according to the position of the photographing unit is synchronized with the real time spatial information based on the noise prediction modeling result Real-time noise analysis system. 9. The apparatus of claim 8, wherein the noise map generator comprises:
Wherein the 3D noise map is generated by synchronizing the real time spatial information with the real time spatial information, and the degree of noise is displayed. A step of photographing an image of a road or a railway;
Analyzing information of a noise cause object from the photographed image;
Modeling noise prediction based on the information of the object;
Synchronizing the noise prediction modeling result with real time spatial information; And
And generating the synchronized result as a noise map. The method of claim 10, wherein analyzing the information of the noise-
Identifying a type of the object from the photographed image;
Analyzing the velocity of the object from the photographed image; And
And analyzing the path of the object from the photographed image. 12. The method of claim 11, wherein in modeling the noise prediction,
And noise prediction is modeled by receiving information on noise that matches the type, speed, and path of the object from a database. 12. The method of claim 11, wherein in synchronizing to the real-time spatial information,
Wherein the noise prediction modeling result is synthesized with the real-time spatial information and information on the position of the photographed image provided from the database and is synchronized.
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Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108320510A (en) * 2018-04-03 2018-07-24 深圳市智绘科技有限公司 One kind being based on unmanned plane video traffic information statistical method and system
CN108694829A (en) * 2018-03-27 2018-10-23 西安科技大学 Magnitude of traffic flow identification monitoring network based on unmanned aerial vehicle group mobile platform and method
KR20190030275A (en) * 2017-09-14 2019-03-22 동아대학교 산학협력단 System for Providing Noise Map Based on Big Data Using Sound Collection Device Looked Like Earphone
KR102713408B1 (en) * 2024-01-11 2024-10-07 주식회사 씨엔에스환경기술 Automatic noise measurement system and method according to train operation considering public data portal and external shape of each train

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR100690279B1 (en) * 2005-09-12 2007-03-09 주식회사 리트코 Multipurpose video image detection system
KR20120014508A (en) 2010-08-09 2012-02-17 서울시립대학교 산학협력단 Apparatus and method for online visualization of noise using ubiquitous-city middleware
KR20120055783A (en) 2010-11-23 2012-06-01 서울시립대학교 산학협력단 Apparatus and method of cloud computing for online visualization of noise map
CN102654940A (en) * 2012-05-23 2012-09-05 上海交通大学 Traffic information acquisition system based on unmanned aerial vehicle and processing method of traffic information acquisition system
WO2014051199A1 (en) * 2012-09-28 2014-04-03 한국건설기술연구원 Method and device for reducing traffic noise using vehicle sensor

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR100690279B1 (en) * 2005-09-12 2007-03-09 주식회사 리트코 Multipurpose video image detection system
KR20120014508A (en) 2010-08-09 2012-02-17 서울시립대학교 산학협력단 Apparatus and method for online visualization of noise using ubiquitous-city middleware
KR20120055783A (en) 2010-11-23 2012-06-01 서울시립대학교 산학협력단 Apparatus and method of cloud computing for online visualization of noise map
CN102654940A (en) * 2012-05-23 2012-09-05 上海交通大学 Traffic information acquisition system based on unmanned aerial vehicle and processing method of traffic information acquisition system
WO2014051199A1 (en) * 2012-09-28 2014-04-03 한국건설기술연구원 Method and device for reducing traffic noise using vehicle sensor

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20190030275A (en) * 2017-09-14 2019-03-22 동아대학교 산학협력단 System for Providing Noise Map Based on Big Data Using Sound Collection Device Looked Like Earphone
KR101965313B1 (en) 2017-09-14 2019-04-03 동아대학교 산학협력단 System for Providing Noise Map Based on Big Data Using Sound Collection Device Looked Like Earphone
CN108694829A (en) * 2018-03-27 2018-10-23 西安科技大学 Magnitude of traffic flow identification monitoring network based on unmanned aerial vehicle group mobile platform and method
CN108694829B (en) * 2018-03-27 2021-08-13 西安科技大学 Traffic flow identification monitoring network and method based on unmanned aerial vehicle group mobile platform
CN108320510A (en) * 2018-04-03 2018-07-24 深圳市智绘科技有限公司 One kind being based on unmanned plane video traffic information statistical method and system
CN108320510B (en) * 2018-04-03 2020-12-04 深圳市智绘科技有限公司 Traffic information statistical method and system based on aerial video shot by unmanned aerial vehicle
KR102713408B1 (en) * 2024-01-11 2024-10-07 주식회사 씨엔에스환경기술 Automatic noise measurement system and method according to train operation considering public data portal and external shape of each train

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