KR102357256B1 - Two dimensional aerosol extinction coefficient and directional visibility measurement methods, using arbitrary landscape image photography - Google Patents

Abstract

The present invention relates to a method for calculating an average dissipation coefficient of fine dust in each direction in a space in sight of a camera based on the amount of fine dust in the atmosphere to obtain a dissipation coefficient of each voxel based on the calculated average dissipation coefficient of fine dust. An objective of the present invention is to provide a method of measuring direction dependent visibility range and two dimensional space fine dust distribution using an arbitrary landscape image of a camera, which calculates contribution of a dissipation coefficient of a plurality of voxels in a given space from an average value of fine dust in a plurality of directions obtained from one camera and an average dissipation coefficient obtained from another camera located at another direction through a solution of a system of equations, to obtain a dissipation coefficient of fine dust in each voxel. Since a dissipation coefficient of each direction is obtained using the above result and a dissipation coefficient in each voxel is obtained, dissipation coefficients of each location and each direction in a given space may be obtained by applying the obtained dissipation coefficients to a Koschmieder visibility distance definition.

Description

Two dimensional aerosol extinction coefficient and directional visibility measurement methods, using arbitrary landscape image photography

The present invention relates to a method for measuring direction-dependent visibility distance and two-dimensional spatial fine dust distribution using an arbitrary landscape image of a camera. The fact that the average dissipation coefficient of fine dust in the direction of the subject can be obtained using the principle that the visibility distance deteriorates due to the change of contrast It relates to a method of measuring the direction-dependent visibility distance and two-dimensional spatial fine dust distribution using an arbitrary landscape image of a camera using the principle that visibility of and the average dissipation coefficient of fine dust in all directions can be obtained.

을 측정의 표준으로 삼고 있어 이를 직접 비교하는 오류에서 일어난 일이다.According to a paper presented at the Korean Association of Atmospheric Environment in 2018, there is a report that the current fine dust concentration in Beijing, China is similar to the fine dust concentration in Korea in the 1990s. Mass per unit volume of dust (

This was caused by an error in direct comparison with the standard of measurement.

에 기반을 두는 물리량이며 이 값은 일반인들이 느끼는 미세먼지에 대한 정서와 부합한다. 또한, 실제 미세먼지에 묻어있는 오염원의 화학적 물리적 활성화는 그 면적에 비례기 때문에 환경/보건학 적으로도 단위 부피에 존재하는 미세먼지의 질량보다는 단위 부피에 존재하는 미세먼지의 총 면적이(

더 중요한 물리량이다.The perceived fine dust that the general public is interested in is through the visible distance, and this physical quantity is the dissipation coefficient (

It is a physical quantity based on In addition, since chemical and physical activation of pollutants embedded in actual fine dust is proportional to the area, from an environmental/health perspective, the total area of fine dust present in a unit volume (

It is a more important physical quantity.

This unreasonable measurement is also due to the practice of using the value (TSP) of the existing total amount of fine dust measurement equipment as it is. In addition, the importance of ultrafine dust has been emphasized by simultaneously measuring PM2.5 and PM10, which are ultrafine particles, for several years, but efforts are still being made to inform the physical quantity such as the size distribution of fine dust.

단위의 물리량을 측정하기 때문에 일반인이 느끼는 정서에 맞는 그리고 실제 보건 환경학적으로 중요한 물리량을 측정한다고 할 수 있다.The optical method is directly related to the conventional method of measuring mass.

Because it measures the physical quantity of a unit, it can be said that it measures a physical quantity that fits the emotions of the general public and is important in terms of actual health and environment.

However, the measurement of ultrafine dust is not only dichotomized into PM2.5 and PM10, but it is also measured in a local area, so it is still insufficient to represent a neighborhood or a large area. In this respect, a device that can diversify the measurement range to a relatively wide area and can be easily obtained is required.

Currently, cameras are installed everywhere in Korea for various purposes (crime prevention, visibility measurement, traffic condition monitoring, cloud distribution measurement), so it can be said that H/W device construction already exists. Therefore, it is very important to develop an algorithm or method that can utilize it, and its value can be doubled.

Devices for measuring visibility include a scattering type, a transmission type, and a method using an image. However, since visibility depends on the scattering coefficient of fine dust and the direction and position of the sun, visibility is different even with the same amount of fine dust. Because the existing scattering and transmission equations are related only to the amount of fine dust, it is difficult to apply them in various environments (position of the sun). In the method of using the image, visibility is inferred only from the sharpness of the landscape viewed from the point where the image was acquired, and it is difficult to obtain fine dust information at each street or visibility information with distance resolution. Existing patents or methods have a limitation in providing visibility information only at one point rather than providing visibility distance information at each location.

Since this patent obtains the dissipation coefficient of fine dust from a two-dimensional pixel, visibility information can be provided for each pixel in a two-dimensional plane as well as visibility information dependent on the direction. For example, it provides different visibility distances to a driver who drives with his back to the sun and a driving position with the sun in the same fine dust condition, and also provides information on how visibility changes when the driver is in different positions. In other words, it can provide visibility of pixels in two-dimensional space or dissipation coefficient information of fine dust.

Republic of Korea Patent Registration No. 10-1892999 (2018.08.23.)

An object of the present invention is to obtain the average dissipation coefficient of fine dust in each direction in the space in the field of view of the camera for the amount of fine dust in the atmosphere, and from this, the direction in which the dissipation coefficient of each voxel in the two-dimensional space in that direction can be obtained It aims to provide a method for measuring dependent visibility distance and two-dimensional spatial fine dust distribution.

Another object of the present invention is to calculate the contribution of the dissipation coefficient of each voxel in a given space from the average value of fine dust in several directions obtained with one camera and the average dissipation coefficient obtained through another camera located in another direction. The purpose of this study is to provide a method for measuring the direction-dependent visibility distance and two-dimensional spatial fine dust distribution that can be calculated through Jung Singh's solution to obtain the fine dust dissipation coefficient in each voxel.

In order to achieve the above object, the present invention relates to a method for measuring a direction-dependent visibility distance and a two-dimensional spatial fine dust distribution using an arbitrary landscape image of a camera. partitioning the two-dimensional planar space into n×m grid spaces (n and m are natural numbers greater than or equal to 1); At a specific location where a digital camera capable of obtaining a color image is installed, in 1 to n×m directions toward the grid-type space, the distance from the end of the grid-type space to the end of the grid-type space through the RGB wavelengths obtained from the camera calculating an average dissipation coefficient; generating an average dissipation coefficient of fine dust in each direction as an n×m-order simultaneous equation regarding a fine dust dissipation coefficient in each grid space in the corresponding direction and a distance passing through the grid space; and calculating the simultaneous equation to calculate a fine dust dissipation coefficient for each grid-type space.

And in the step of calculating the average dissipation coefficient of the fine dust, two or more cameras are arranged at positions spaced apart from each other in the grid space, and a total of 1 to n × m cameras are placed toward the grid space from the position where each camera is installed. The average dissipation coefficient of fine dust is calculated for the distance to the end of the grid space in the direction.

In this case, when two cameras are used, it is preferable to be spaced apart from each other at an angle perpendicular to each other in the grid-type space.

In addition, when three cameras are used, it is preferable to be spaced apart from each other at an angle of 60° in the grid space.

In the step of calculating the average dissipation coefficient of the fine dust, the dissipation coefficient may be calculated through the Green wavelength among the RGB wavelengths obtained by the camera.

According to the method for measuring the direction-dependent visibility distance and two-dimensional spatial fine dust distribution using an arbitrary landscape image of a camera according to the present invention, when the space in the camera's field of view is divided into a plurality of pixels on a two-dimensional plane, the fineness in each pixel Since the dust dissipation coefficient can be calculated, there is an advantage in that it is possible to calculate and provide the fine dust value for each specific point in the region, rather than the average value of the fine dust in the region.

And, according to the present invention, there is an advantage in that it is possible to more accurately and easily provide a fine dust value for a specific point in real time by using an image signal of a digital camera that is photographed in various directions.

1 is a view for explaining how light scattered by sunlight is incident on the eye;
2 is a view showing the scattering characteristics according to the type and angle of suspended matter in the atmosphere;
3 is a photograph showing a difference in the sharpness of the foreground shown when shooting a 360° panoramic image in a specific area;
4 is a view showing subjects sequentially arranged in one direction and the other with respect to the camera;
FIG. 5 is a view for explaining a method of calculating a fine dust dissipation coefficient for each voxel in which a two-dimensional planar space is partitioned into a grid using two cameras.

Hereinafter, a method for measuring a direction-dependent visibility distance and a two-dimensional spatial fine dust distribution using an arbitrary landscape image of a camera according to the present invention will be described in detail together with the accompanying drawings.

1 is a view for explaining how light scattered by sunlight is incident on the eye, FIG. 2 is a view showing scattering characteristics according to the type and angle of air suspended matter, and FIG. 3 is a 360° panoramic image in a specific area. It is a photograph showing the difference in the sharpness of the foreground shown when shooting with It is a diagram explaining a method of calculating the fine dust dissipation coefficient for each voxel partitioned in a grid type.

The present invention obtains an image at a point, obtains a visibility distance from that point in a direction within the camera's field of view (FOV), and obtains an image by changing the direction of the camera again through the process of obtaining a visibility distance in another direction in the same way Visibility distances in various directions are obtained, and visibility distances in various directions can be provided. In addition, the visibility distance in each direction is related to the fine dust in that direction, according to the definition of the cosimider, and provides an average value of the fine dust in each direction at the same time.

The average dissipation coefficient in a specific direction is an average of the dissipation coefficients of each spatial pixel with the total length of light passing through the spatial pixels in that direction as a weight.

Since the average dissipation coefficient obtained in one direction is a weighted average of several spatial pixels, multiple average dissipation coefficients can be obtained by calculating the average dissipation coefficient in various directions. It is possible to obtain and use the solution of the system of equations with the dissipation coefficient in the pixel.

The dissipation coefficient at each spatial pixel obtained in this way can extract the visibility distance at that point, and eventually, the visibility distance according to the location in the two-dimensional space can be obtained. At the same time, by using the average dissipation coefficient obtained from each point in several directions, the visibility distance according to the direction can be converted.

A more detailed description of this is as follows.

Visibility is a meteorological factor that indicates the degree of atmospheric turbidity, and the maximum distance that a person with normal vision from the ground can identify a target (angle of 0.5° to 5°) is displayed in km unit, and is displayed on the Korea Meteorological Administration website (www. You can check the number updated every hour at .weather.go.kr). Visibility is also affected by suspended substances (air, fine dust, fog, water vapor, etc.) in the atmosphere and, in the case of objects illuminated by the sun, the illuminance and direction of the sun. it is dust

Visibility is the most easily perceived indicator of air pollution because fine dust in the air mainly scatters light and weakens the intensity of light reaching our eyes, thereby worsening visibility. As an example, visibility on days with a lot of fine dust, such as yellow dust and smog, is very short. Short visibility means that the image of the subject (object) is blurred on the retina by the suspended matter in the atmosphere. This is because the signal scattered by fine dust and water droplets (fog)) is reduced, and at the same time, the light scattered by suspended matter enters the retina through the eye at the same time.

The degree of blur is determined by the amount of suspended matter in the atmosphere, the direction of the light (sun), and the intensity of the light. In other words, even if the amount of suspended matter in the atmosphere is the same, it may be different depending on the location and intensity of sunlight. For example, if a white object is observed on a black background or a white object is observed on a black background, at a short distance, the scattered signal by suspended matter is less incident to the eye, and the scattered light from the object is large. The greater the distance between the object and the camera, the greater the signal scattering by airborne matter. Conceptually, light is incident on the eye in the form shown in FIG. 1 .

In Fig. 1, the round black object is suspended matter, which represents all particles between the eye and the object. In FIG. 1, an object and a background without an object are shown separately, and '1' indicates that light scattered by suspended matter is incident on the eye, and '2' indicates that the light is incident from the object and is incident on the eye. In this case, '3' corresponds to the case of a space without an object (the sky). In addition, '4' indicates light that is scattered by suspended particles, but does not enter the eye and spreads into space. Finally, 'θ' refers to the angle made by sunlight, suspended particles, and snow.

The target described in the present invention means the brightness distribution of an object such as '3' in FIG. 1 when an image is formed on the CCD by a lens, and the brightness distribution is a signal from an object at a certain distance as described above and It will be the sum of the scattered by all particles in the region midway between the object and the eye. The intensity of the scattered light from the object will gradually decrease according to the Beer Lambert law, and the background signal (blurry), that is, the scattered signal of particles suspended in the air, is transmitted at all distances between the object and the eye (CCD). It is the integration of the scattering effect of the particles present. This effect is expressed mathematically as [Equation 1].

[Equation 1] describes at what intensity (I) the image of the object at the distance R is formed on the retina (CCD). In the equation, 'α' represents the light dissipation coefficient, and 'C ₁ ' is due to suspended particles in the air, which depends on the angle made by the sun, suspended particles, and snow. 'C ₂ ' is a quantity determined by the direction, color, and reflectivity of an object (sun). Overall, it can be said that the sharpness of an image is determined by all of these variables.

)과 부유물질의 특성(

)에 의하여 결정된다. 즉 선명도는 물체의 종류와 부유물질에 의하여 결정된다. 다르게 표현하면 물체의 반사도와 태양의 반사각에 의하여 'C₁'이 결정되며, 마찬가지로 태양과 카메라 그리고 대기 부유물질의 종류와 각도에 의하여 'C₂' 값이 결정된다. 'C₁'값은 산란면의 특성이 주어져 있을 경우(물체가 나무, 바위 등) 오직 태양과 이루는 각도에 의한 Rambertian 반사 특성을 따르고, 'C₁'은 부유 입자의 카메라 방향으로의 반사도를 의미하며 Mie 산란 특성을 따른다. 두 산란 특성은 아래 도 2와 같은데, 도면에서 알 수 있듯이 부유입자의 경우 태양광과 물체가 같은 방향에 존재하여 그 각도(θ)가 '0'에 가까운 경우 급격히 산란의 세기가 커진다. 반면에 물체의 Rambertian은 코사인 함수의 꼴로 나타나며, 물체의 종류에 따른 변화가 그리 크기 않다.In defining visibility, sharpness is a characteristic of an object (

) and properties of suspended solids (

) is determined by That is, the sharpness is determined by the type of object and suspended matter. In other words, 'C ₁ ' is determined by the reflectivity of the object and the angle of reflection of the sun, and similarly, the 'C ₂ ' value is determined by the type and angle of the sun, camera, and air suspended matter. The 'C ₁ ' value follows the Lambertian reflection characteristic by the angle formed with the sun only when the scattering surface characteristics are given (objects such as trees, rocks, etc.), and 'C ₁ ' means the reflectivity of suspended particles in the camera direction. and follows the Mie scattering characteristics. The two scattering characteristics are the same as in Fig. 2 below. As can be seen from the figure, in the case of suspended particles, sunlight and the object exist in the same direction, and when the angle (θ) is close to '0', the intensity of scattering increases rapidly. On the other hand, the Rambertian of an object appears in the form of a cosine function, and the change according to the type of object is not so great.

On the other hand, in the case of suspended particles, forward scattering occurs strongly depending on the size of the particles. In other words, the scattering signal of suspended particles corresponding to the background signal is very sensitive to the angle, so when considering the definition of visibility, the visibility is very sensitive to the angle of the sun even if the same material is floating in the atmosphere. When viewed, the scattering by the suspended particles becomes very large and the visibility deteriorates. Consequently, this sharpness depends only on the intensity of the scattering signal of suspended matter at a given distance. This fact can be fully felt in our daily life. Therefore, visibility information must be given in location, direction, and time. Even if the suspended particles are the same, the magnitude of the background signal changes depending on the direction and the visibility distance varies. 3 shows an example of a 360° panoramic image showing different sharpness in the same foreground. It can be seen that in the vicinity of forward scattering including the sun, the background signal becomes larger and the sharpness deteriorates, and consequently the visibility deteriorates. In this regard, in implementing the present invention, it is necessary to properly and selectively install the camera so that target objects of various distances exist in the angle of view of the camera, and there is a need to obtain visibility information from various angles.

)과 하늘(

:배경)의 밝기 차이가 산의 유무를 결정할 수 있으며, 이는 선명도로 인식되기 때문에 대비(혹은 선명도)는 다음과 같이 정의된다. For visibility mainly used in airports, recognition of the closest and highest mountain-like object viewed from a high altitude will be important. That is, from the pilot's point of view, it may be appropriate to define the brightness of an object with the sky as a background through the brightness of the sky background. That is, the definition of visibility is determined by whether black and white objects can be distinguished from each other by placing them at a specific distance. For example, a shadowed mountain (

) and the sky (

: The difference in brightness of the background) can determine the presence or absence of acid, which is recognized as sharpness, so contrast (or sharpness) is defined as follows.

'는 산을 의미하며, '

'는 배경을 의미하므로 대비는 배경신소에 대한 물체(

)와 배경신호(

)의 차이를 말하므로, [수학식 1]을 배경신호와 물체에 적용하면, 즉 아래의 [수학식 3]과 같이 표현할 수 있다.In [Equation 2] above, '

' means mountain, '

' means the background, so the contrast is the object (

) and the background signal (

), if [Equation 1] is applied to the background signal and the object, that is, it can be expressed as [Equation 3] below.

'는 검은 물체의 망막(CCD)에서의 밝기, '

'는 하늘과 같은 배경신호의 망막(CCD)에서의 밝기를 나타낸다. 그러므로 코시미더 선명도는 오직 소산계수와 거리의 변수만 된다. 코시미더의 정의에 따르면 위의 [수학 2]에서 대비(contrast=0.02)가 0.02(2%)가 되는 거리(R)를 시정으로 정의한다. 그러므로 코시미더 시정거리는 550nm 파장에서의 소산계수는 시정 거리(V)와 다음의 관계가 있다.(왜냐하면 log(0.02)=-3.9) In [Equation 3], '

' is the brightness in the retina (CCD) of a black object, '

' indicates the brightness in the retina (CCD) of a background signal such as the sky. Therefore, cosimidor sharpness is only a variable of dissipation coefficient and distance. According to the definition of cosimider, the distance (R) at which the contrast (contrast=0.02) becomes 0.02 (2%) in [Mathematics 2] above is defined as visibility. Therefore, the cosimidor visibility is related to the dissipation coefficient at the wavelength of 550nm as follows (because log(0.02)=-3.9)

The wavelength of 550 nm is the most sensitive among the three cone cells in the human retina, and when observing visibility by eye observation, the dissipation coefficient at this wavelength has the most influence. In [Equation 3], if the dissipation coefficient is known at 550 nm, the visibility distance can be known, and conversely, if the visibility is obtained, it means that the dissipation coefficient can be obtained at 550 nm. If there is only air in the atmosphere, the visibility due to Rayleigh scattering is about 296 km. In conclusion, the correction can be obtained from the dissipation coefficient through the above equation, and vice versa. While the existing device obtains the visibility from the dissipation coefficient due to fine dust, the present invention directly specifies the visibility and also obtains the dissipation coefficient of the fine dust from the visibility. When the dissipation coefficient is obtained using a camera, the use of the dissipation coefficient at the green wavelength among RGB wavelengths has the least error in the calculation of the visibility distance.

A method of obtaining the average dissipation coefficient of the three points from three or more objects is known when the image obtained from a fixed camera and the known distance to the target object are known. As an example, the method of obtaining the dissipation coefficient of fine dust at 3 wavelengths in that direction in a given image is described in Patent Application No. 10-2019-0003495 of the present inventor (A system for extracting fine dust dissipation coefficient and size information in the air using an arbitrary landscape) The same method can be used.

To explain this in more detail, Patent Application No. 10-2019-0003495 discloses a lighting constant, a diffuse reflection constant, a camera constant, etc. under the condition that the brightness of each pixel obtained by the camera and the distance (d) of the landscape corresponding to the pixel are known. We present a system that can obtain fine dust dissipation coefficient information from complex information.

In other words, it is a method to find a special pixel point in an arbitrary landscape with a special approach and obtain the dissipation coefficient of fine dust in the space composing the camera and the landscape at each wavelength. of the size information of , but from the light intensity distribution of a pixel corresponding to a specific object obtained from the camera's three RGB wavelengths and the distance information from the camera to the landscape, obtain information on the dissipation coefficient of fine dust between the landscape and the camera, and from this size is calculated.

That is, the distance between the camera and the photographed object is calculated through the distance measurement unit, and the calculation unit obtains the dissipation coefficient at each wavelength using the pixel information of each object obtained from the camera, and then calculates the theoretical dissipation coefficient of air molecules. By removing it, the dissipation coefficient of fine dust present in the air is calculated.

The particle size information is extracted from the dissipation coefficient of the fine dust through the Ahu extraction unit, and for this purpose, a DB in which the size of the particles compared to the dissipation coefficient is prepared in advance through experiments or calculations can be used.

In other words, if the same type of object is selected from a given image, for example, a telephone pole in the same direction at a different distance, a tree in a mountain in the same direction, or a window of a building existing in the same direction, using various targets such as The dissipation coefficient of can be obtained at 3 wavelengths of RGB. At this time, since [Equation 1] is used, at least three objects are required.

As an example, FIG. 4 shows subjects in two directions different from that of the camera installed in the right direction as an example. In the example, the objects of 1, 2, 3,.. exist in different directions from 1', 2', 3',.., etc. For example, using the targets of 1, 2, 3,.. It is the average dissipation coefficient value in that direction when obtaining If you turn the camera completely in the other direction, for example in the opposite direction, you get the dissipation coefficient in the opposite direction too.

까지의 평균 소산계수를 얻는 것이고, 10, 11, 12의 피사체로 평균 소산계수를 얻으면

거리에 있는 미세먼지의 평균 소산계수를 구한 것이 된다. In Fig. 4, the dissipation coefficient obtained using only the subjects 1, 2, 3 is to obtain the average dissipation coefficient of the distance, and when the dissipation coefficient is obtained with 1, 2, 3,...

It is to obtain the average dissipation coefficient of up to

This is the average dissipation coefficient of fine dust in the street.

Next, it is possible to obtain the dissipation coefficient (or visibility distance, if [Equation 4] is applied) of fine dust in the space of each position of the two-dimensional surface. This will be described in detail as follows.

As an example, consider a given two-dimensional space (we live on a two-dimensional surface) divided into 16 4x4 pixels. And let's say the camera is to the left of pixel 31 as an example. Figure 5 realistically shows this space and the position of the camera. Each pixel corresponds to a point on a given two-dimensional surface. For example, the average dissipation coefficient of fine dust obtained in the k direction can be written as [Equation 5].

화소만 기여하고 각 화소 중의 경우도 다른 가중치로 k방향의 시정에 기여한다. 즉

화소는 도 5와 같이 각각의 화소에

경로만큼 기여하기 때문에 이런 가중치의 합으로 표시한다. In [Equation 5], the poem in the k direction is

Only the pixels are contributed, and each pixel also contributes to the visibility in the k direction with a different weight. In other words

As shown in FIG. 5, each pixel is

Since it contributes as much as the path, it is expressed as the sum of these weights.

As another example, the visibility (average dissipation coefficient of fine dust) in the N direction in FIG. 5 is contributed by other spatial pixels.

값은 간단한 기하학으로 얻을 수 있으며, 미지수인 각 공간화소에서의 미세먼지 소산계수 16 개는 16방향으로의 평균 소산계수

즉 [수학식 5]와 같은 16개의 연립방정식을 통하여 얻을 수 있다.When a two-dimensional space is given in [Equation 5] and the position of the camera is determined,

The value can be obtained with simple geometry, and the 16 fine dust dissipation coefficients in each spatial pixel that are unknown are the average dissipation coefficients in 16 directions.

That is, it can be obtained through 16 simultaneous equations as in [Equation 5].

If the 16 dissipation coefficients obtained from the green wavelength of the camera are applied to [Equation 4], the dissipation coefficient and the visibility distance at each point can be obtained. And from the average dissipation coefficient in each direction, the visibility distance in each direction can also be obtained from [Equation 4].

And when two cameras are arranged in a two-dimensional grid space, in order to generate a total of n×m (n, m is a natural number greater than or equal to 1) different directions, that is, a plurality of directions passing through a specific voxel In order to generate a total of n×m different directions without being arranged in parallel close to each other, it is preferable that the two cameras are spaced apart from each other at an angle perpendicular to each other in the grid space (refer to FIG. 5 ).

In addition, when three cameras are used, it is preferable to be spaced apart from each other at an angle of 60° in the grid space.

Although more than three cameras may be used, the mutual direction tends to overlap, so the utility does not increase significantly.

As described above, the rights of the present invention are not limited to the embodiments described above, but are defined by what is described in the claims, and those of ordinary skill in the art can make various modifications and adaptations within the scope of the claims. It is self-evident that you can

Claims (5)

dividing the two-dimensional planar space into n×m grid-type spaces (n, m is a natural number greater than or equal to 1) by using an area for measuring the distribution of fine dust as a two-dimensional planar space;
At a specific location where a digital camera capable of obtaining a color image is installed, in 1 to n×m directions toward the grid-type space, the distance from the end of the grid-type space to the end of the grid-type space through the RGB wavelengths obtained from the camera calculating an average dissipation coefficient;
generating an average dissipation coefficient of fine dust in each direction as an n×m-order simultaneous equation for a fine dust dissipation coefficient in each grid-type space in the corresponding direction and a distance passing through the grid-type space; and
Calculating the simultaneous equation to calculate the fine dust dissipation coefficient for each grid-type space;
In the step of calculating the average dissipation coefficient of the fine dust, two or more cameras are arranged at positions spaced apart from each other in the grid space, and a total of 1 to n × m directions from the position where each camera is installed to the grid space A method for measuring a direction-dependent visibility distance and a two-dimensional spatial fine dust distribution using an arbitrary landscape image of a camera, characterized in that the average dissipation coefficient of fine dust is calculated with respect to the distance to the end of the grid-type space.
delete According to claim 1,
When two cameras are used, a method for measuring a direction-dependent visibility distance and a two-dimensional spatial fine dust distribution using an arbitrary landscape image of a camera, characterized in that they are spaced apart from each other at an angle perpendicular to each other in the grid-type space.
According to claim 1,
When three cameras are used, direction-dependent visibility distance and two-dimensional spatial fine dust distribution measurement method using an arbitrary landscape image of a camera, characterized in that they are spaced apart at an angle of 60° from each other in the grid-type space.
According to claim 1,
In the step of calculating the average dissipation coefficient of the fine dust, direction-dependent visibility distance and two-dimensional spatial fineness using an arbitrary landscape image of a camera, characterized in that calculating the dissipation coefficient through a green wavelength among the RGB wavelengths obtained from the camera How to measure dust distribution.

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