JP7170485B2 - Image analysis device, image analysis method and image analysis program - Google Patents

Description

The present invention relates to an image analysis device, an image analysis method, and an image analysis program.

The water temperature distribution in coastal areas varies greatly in space and time due to natural factors such as vertical mixing due to breaking waves, the influx of river plumes, and heat exchange on the water surface, as well as anthropogenic wastewater flowing into the ocean from factories and the like. Seawater temperature is one of the most important parameters for evaluating coastal environments that form rich ecosystems. Aggregation is required.

In general, oceanographic observations, including coastal areas, have been carried out by direct measurements using ships, buoys, thermometers, and current meters, and by remote sensing using marine radars and satellites. However, it is difficult to occupy coastal areas that are highly used for fishing and shipping, and it is difficult to obtain sufficient information from ships and buoys. Further, marine radars and artificial satellites have a resolution of 1 km scale or the like. In addition, with marine radar, the measurement range is, for example, an area about 500 m or more away from the coast. Thus, it is difficult to obtain local current velocity distributions, water temperature distributions, and turbulence statistics in coastal areas due to the low resolution of ocean observations using ocean radars and satellites.

In particular, it is known that the initial mixing process, which behaves as a jet, is important for predicting the extent of oceanic diffusion of wastewater. However, spot observations using ships and buoys are difficult to capture local spatial fluctuations in water discharge flow, and as mentioned above, ocean radars and satellites have low resolution, so local observations are not possible. difficult. Therefore, it is extremely difficult to observe local phenomena immediately after water discharge.

Here, thermography is a useful tool for investigating the relationship between fluid motion and temperature fluctuations, as it can measure fluctuations in planar distribution of water temperature. Fluid measurement by thermography is mainly performed in laboratory experiments, and there are few examples of field observations in coastal areas using thermography. For example, a technique has been proposed in which thermography is installed on an observation tower installed in a coastal area and measurements are made.

Furthermore, as an ocean observation technology, there is a conventional technology for detecting water currents in a predetermined water area from a water surface temperature distribution image obtained by a thermocamera mounted on an aircraft. Also, there is a conventional technique in which buoys are installed at predetermined intervals and the flow velocity is calculated from the positions of the buoys photographed by a camera mounted on an unmanned air vehicle. In addition, there is a conventional technique for measuring the speed and direction of water flow from the movement amount and movement direction of the undulating pattern of the water surface captured by a video camera at regular intervals.

JP 2015-127669 A JP 2018-91725 A Japanese Patent Application Laid-Open No. 2009-74968

However, when a thermographic camera is installed on an observation tower installed in a coastal area to take measurements, the camera captures the sea surface from an oblique angle, which may increase image errors. In addition, the method of installing a thermography on a structure such as an observation tower is limited to the observation at the place where the structure is constructed, and the local flow velocity distribution, water temperature distribution, and turbulence statistics of arbitrary coastal areas are difficult. Getting enough is difficult.

Conventional technology, which detects water currents in a given water area from a water surface temperature distribution image obtained by a thermal camera mounted on an aircraft, can obtain rough information about water currents, but it cannot detect current velocity distributions and water temperature distributions in coastal areas. Obtaining spatio-temporal local variations and turbulence statistics is difficult. In addition, with conventional technology that calculates current velocity from the positions of buoys captured by cameras mounted on unmanned air vehicles, it is difficult to install enough buoys in coastal areas that are heavily used for fishing and shipping. It is difficult to obtain local flow velocity distribution, water temperature distribution and turbulence statistics. Furthermore, in the conventional technology for measuring the speed and direction of water flow from the amount of movement and the direction of movement of the undulating pattern of the water surface photographed at regular intervals by a video camera, the flow velocity can be calculated from the amount of movement and the direction of movement of the undulating pattern in coastal areas where waves exist. It is difficult to measure. In addition, with this conventional technology, the number of observation points is limited due to the limited installation location of the video camera, and it is difficult to obtain sufficient information to calculate the local current velocity distribution, water temperature distribution, and turbulence statistics in coastal areas. is difficult.

The disclosed technology has been made in view of the above, and aims to provide an image analysis device, an image analysis method, and an image analysis program for simply and accurately observing ocean conditions in coastal areas.

An image analysis apparatus, an image analysis method, and an image analysis program disclosed in the present application, in one aspect, include the following units. The camera is mounted on the UAV and takes a plurality of thermographic images representing water temperature distribution on the water surface from the flying UAV. The correcting unit extracts land-side feature points from each of the thermographic images captured by the camera, performs affine transformation based on the extracted land-side feature points, and corrects the mutual positions of the thermographic images. do. The detection unit acquires the pattern of the water temperature distribution from the thermography image corrected by the correction unit , and detects the state of the water flow based on the change in the acquired pattern of the water temperature distribution.

In one aspect, the present invention can easily and accurately observe ocean conditions in coastal areas.

FIG. 1 is a block diagram of an image analysis system according to an embodiment. FIG. 2 is a perspective view of the UAV. FIG. 3 is a diagram of an example of a captured thermographic image. FIG. 4 is a diagram for explaining image correction. FIG. 5 is a diagram showing an example of a thermography image acquired by the image analysis system according to the embodiment. FIG. 6 is a diagram showing a flow velocity distribution acquired by the image analysis system according to the example. FIG. 7 is a diagram showing the spatial distribution of turbulence energy acquired by the image analysis system according to the example. FIG. 8 is a flowchart of ocean observation processing by the image analysis system according to the embodiment. FIG. 9 is a diagram showing flow velocities measured using an ultrasonic anemometer. FIG. 10 is a diagram showing horizontal attenuation of flow velocity measured by the image analysis system according to the embodiment. FIG. 11 is a diagram showing a horizontal distribution of water temperatures measured by the image analysis system according to the embodiment. FIG. 12 is a diagram showing the spatial distribution of the standard deviation of water temperature fluctuations. FIG. 13 is a hardware configuration diagram of the analysis device.

Embodiments of the image analysis apparatus, the image analysis method, and the image analysis program disclosed in the present application will be described below in detail with reference to the drawings. The image analysis apparatus, image analysis method, and image analysis program disclosed in the present application are not limited to the following embodiments.

FIG. 1 is a block diagram of an image analysis system according to an embodiment. The image analysis system 100 has an analysis device 1 such as a server and a UAV (Unmanned Aerial Vehicle) 2 such as a drone. This image analysis system 100 corresponds to an example of an image analysis apparatus.

The UAV 2 is piloted by an operator, can move in the air, and can be stationary at the same point while hovering in the air. In particular, the UAV 2 according to this embodiment has a position control function using GPS (Global Positioning System). However, the hovering accuracy using GPS has a certain width, and the UAV 2 does not stay completely at the same point, and moves according to the hovering accuracy even when stationary. For example, the UAV 2 according to this embodiment is 0.5m vertically and 2.5m horizontally. FIG. 2 is a perspective view of the UAV. The UAV 2 is equipped with a camera 21 for taking a thermographic image as shown in FIG.

The camera 21 is capable of continuously capturing thermographic images at regular intervals. In this example, the camera 21 has an image size of 720×480 pixels and a focal length of 9 mm. The camera 21 is installed so that the photographing direction faces vertically downward with respect to the UAV 2 during hovering.

In the present embodiment, the UAV 2 hovers over the ocean in the coastal area and captures images so that the image captured by the camera 21 includes the land as well as a predetermined range of the ocean. For example, UAV2 hovers at an altitude of about 148m and takes pictures. FIG. 3 is a diagram of an example of a captured thermographic image. The thermographic image of FIG. 3 is a photographed image of the vicinity of the water outlet from the power plant in the coastal area. As shown in FIG. 3 , the thermography image captured by camera 21 includes sea surface 200 and land 201 .

The camera 21 continuously acquires thermography images by fixed-point observation. In this embodiment, the camera 21 continuously acquires thermographic images at imaging intervals of 2 seconds within a 40-second imaging period.

Returning to FIG. 1, the description continues. The analysis device 1 is a device that analyzes a thermographic image captured by a camera 21 mounted on a UAV 2 to obtain temperature distribution, water flow velocity, and turbulence statistics in a measurement range. Analysis device 1 is, for example, a server. The analysis device 1 has an image acquisition unit 11 , an image correction unit 12 , a detection unit 13 and a notification unit 14 .

The image acquiring unit 11 acquires a plurality of thermography images continuously captured by the camera 21 at predetermined intervals within the capturing period. Here, in FIG. 1, image acquisition is represented by a line connecting the image acquisition unit 11 and the UAV 2. The image acquisition unit 11 analyzes the portable storage medium in which the thermography image captured by the camera 21 is captured. Images may be acquired from the portable storage medium while connected to the device 1 . The image acquisition unit 11 outputs the acquired thermography image to the image correction unit 12 .

The image correction unit 12 receives from the image acquisition unit 11 the input of the thermography image captured during the imaging period. Here, in this embodiment, the camera 21 uses a wide-angle lens, and as shown in FIG. 3, the acquired thermographic image has barrel-shaped distortion. Therefore, the image correction unit 12 has in advance camera parameters estimated from images of the checkerboard projected by the halogen light captured by the camera 21 from various angles. Then, the image correction unit 12 corrects the distortion aberration of each acquired thermography image using the camera parameters that it holds.

In addition, the position of the UAV 2 is shifted due to the influence of the wind in addition to the displacement of the stop position caused by the GPS hovering accuracy as described above. Therefore, the image correction unit 12 extracts the SURF (Speeded Up Robust Features) feature amount of the land portion in each thermography image. Then, the image correction unit 12 matches the SURF feature points between the images using affine transformation as the geometric correction of the images. That is, the image correction unit 12 fixes a specific point on the land and transforms the image so that the fixed specific points match. After that, the image correction unit 12 outputs the corrected thermography image to the detection unit 13 and the notification unit 14 .

FIG. 4 is a diagram for explaining image correction. The image correction unit 12 corrects the distortion aberration of the thermography image as shown in FIG. 3, and obtains the thermography images 211 and 212 shown in FIG. Here, the thermography image 211 is an image obtained by correcting the distortion aberration of the thermography image at the start of photographing, and the thermography image 212 is an image obtained by correcting the distortion aberration of the image 40 seconds after the start of photographing. Both of the thermographic images 211 and 212 show land 201 and sea surface 200 .

The image correction unit 12 extracts points indicated by circles on the land 201 of the thermography image 211 as SURF feature points. The image correction unit 12 also extracts points indicated by crosses on the land 201 of the thermography image 211 as SURF feature points. Then, the image correcting unit 12 corrects the image so that the point indicated by the circle on the thermography image 211 and the point indicated by the cross on the thermography image 211 match each other.

Here, in this embodiment, points on the land 201 are extracted as SURF feature points, but other points may be extracted as long as they can be used as fixed points for matching images. For example, if there are rocks above the ocean, the image correction unit 12 may extract SURF feature points from the rocks above the sea surface. In addition, if the buoy moves within a possible range of error with respect to the GPS hovering accuracy, the image correction unit 12 can also correct the image using a buoy on the sea as a SURF feature point. is.

The detection unit 13 has a flow velocity distribution detection unit 131 and a turbulence statistic calculation unit 132 . The flow velocity distribution detector 131 acquires the thermography image corrected by the image corrector 12 . Then, the flow velocity distribution detection unit 131 calculates the flow velocity distribution by applying the direct cross-correlation method to each acquired thermography image. In this embodiment, the detection unit 13 has each inspection area of 33×33 pixels in the direct cross-correlation method. Further, the detection unit 13 calculated the flow velocity distribution by setting the threshold value for preventing an erroneous vector to 0.7 for the minimum correlation coefficient and 10 m/s for the maximum flow velocity.

Also, the flow velocity distribution detection unit 131 obtains the flow direction, which is the direction in which water flows in each inspection area. After that, the flow velocity distribution detection unit 131 outputs information on the flow velocity and flow direction in each inspection area to the notification unit 14 and the turbulence statistics calculation unit 132 .

The turbulence statistic calculation unit 132 receives input of flow velocity information in each inspection region from the flow velocity distribution detection unit 131 . Next, the turbulence statistic calculation unit 132 obtains the average velocity of the flow velocity in each inspection area, sets the deviation from the average velocity as the fluctuation velocity, adds the square mean of the fluctuation velocity, divides by 2, and Calculate the turbulence energy in the region. That is, the turbulence statistic calculation unit 132 calculates the turbulence energy k by the calculation represented by the following formula (1). This turbulence energy k corresponds to an example of turbulence statistics.

where u represents the lateral velocity in the inspection area. Also, v represents the vertical velocity in the inspection area. Then, u' is the variation velocity representing the deviation of u from the average velocity, and v' is the variation velocity representing the deviation of v from the average velocity. A horizontal bar attached to the top of each letter represents the average value.

After that, the turbulence statistic calculation unit 132 outputs information on the calculated turbulence energy in each inspection region to the notification unit 14 .

The notification unit 14 receives from the image correction unit 12 an input of a corrected thermography image which is an image taken during the shooting period. In addition, the notification unit 14 receives from the flow velocity distribution detection unit 131 an input of flow velocity information in each inspection region at each imaging timing within the imaging period. Further, the notification unit 14 receives input of turbulence energy information in each inspection area from the turbulence statistic calculation unit 132 . Then, the notification unit 14 notifies the user of the analysis device 1 of the water temperature at each imaging timing, the flow velocity in each inspection area, and the turbulence statistic in each inspection area. Various methods of notifying this information are conceivable, but as an example, notification of each piece of information using an image will be described below.

First, the notification unit 14 notifies the user of the water temperature in the measurement area using the image by transmitting the acquired thermography image to the user. For example, the notification unit 14 notifies the user of the water temperature by transmitting the thermography image shown in FIG.

FIG. 5 is a diagram showing an example of a thermography image acquired by the image analysis system according to the embodiment. FIG. 5 shows the water temperature distribution around the water outlet when the image analysis system 100 measures the thermal waste water discharged into the ocean from the power generation facility. Here, the horizontal and vertical axes in the upper drawing of FIG. 5 represent two-dimensional coordinates with the position of the water outlet as the origin. Here, each coordinate axis is defined as the X axis and the Y axis, the horizontal axis direction is called the X direction, and the vertical axis direction is called the Y direction. Furthermore, the lower strip in FIG. 5 represents the temperature on the horizontal axis, and the pattern on the strip represents the pattern corresponding to the temperature. The user can grasp the state of the water temperature in a predetermined range by confirming the pattern corresponding to the lower band on the upper diagram.

Here, in this embodiment, the difference in temperature is represented by patterns, but the display method is not limited to this, and the notification unit 14 may represent the difference in temperature by, for example, different colors. For example, the notification unit 14 may generate an image of the measurement range by coloring each inspection area so that the lower the temperature, the darker the blue, and the higher the temperature, the brighter the yellow.

For example, the user can understand the following from FIG. The warm waste water discharged as a density jet having positive buoyancy from the water outlet under the water surface immediately rises and raises the seawater temperature in the vicinity of the water outlet within a range of less than 50 m in the X direction from the water outlet. The water temperature in the range of less than 50 m in the X direction from the water discharge port is about 22° C., which is approximately the same as the water discharge temperature, and is higher than the surroundings. The water temperature decreases as the distance in the X direction from the water outlet increases. The water temperature outside the jet is about 19.6° C., but the vicinity of the quay within a range of less than 30 m in the X direction and 50 m to -60 m in the Y direction from the outlet is a high temperature region.

In addition, the notification unit 14 generates an image of the measurement range in which patterns corresponding to each inspection region are arranged according to the flow velocity. Furthermore, the notification unit 14 adds a vector representing the flow direction in each inspection area to the generated image of the measurement range. Then, the notification unit 14 transmits to the user an image of the measurement range in which the pattern representing the flow velocity is arranged and the vector representing the flow direction is added. Thereby, the notification unit 14 can notify the user of the flow velocity and flow direction in each inspection area at each imaging timing.

For example, the notification unit 14 notifies the user of the flow velocity and flow direction by transmitting an image representing the water flow distribution shown in FIG. FIG. 6 is a diagram showing a flow velocity distribution acquired by the image analysis system according to the example. FIG. 6 shows the spatial distribution of the instantaneous flow velocity at the same timing as in FIG. The horizontal axis and the vertical axis in the drawing on the upper side of FIG. 6 represent the X coordinate and the Y coordinate with the position of the water outlet as the origin. Furthermore, the lower band toward the drawing in FIG. 6 represents the flow velocity on the horizontal axis, and the pattern on the band represents the pattern corresponding to the flow velocity.

Here, in the case of FIG. 6, the flow velocity is not detected in the range where the X coordinate is less than 0, and it can be confirmed that the affine transformation that matches the SURF feature points on the land between the images is performed appropriately. . In addition, in FIG. 6, since the water outlet is not perpendicular to the quay, the flow axis of the warm wastewater is deflected in the positive direction of the Y-axis. In addition, since the vertical cross-section of the flow velocity in the density jet discharged underwater consists of paired vortices, there are multiple flow axes that appear on the sea surface.

The user can grasp the flow velocity and flow direction in a predetermined range by confirming the color scheme corresponding to the lower band on the upper diagram. For example, the user can understand the following from FIG. Compared to the flow speed inside the jet, the flow speed of the surrounding sea surface is smaller, and the flow speed is almost 0 in the high temperature area near the quay, which is less than 30m in the X direction from the outlet and 50m to -60m in the Y direction. , is distinguishable from the jet portion. This flow velocity difference inside and outside the jet causes liquid phase turbulence, entraining the surrounding fluid. By entraining the surrounding fluid, the flow rate of the jet increases, so the width of the jet increases and the jet is diluted as the distance in the X direction from the water outlet increases.

Here, in this embodiment, the difference in flow velocity is represented by patterns, but the display method is not limited to this, and the notification unit 14 may represent the difference in flow velocity by, for example, different colors. For example, the notification unit 14 may generate an image of the measurement range by assigning colors to each inspection region so that the lower the flow velocity, the darker the blue color, and the faster the flow velocity, the brighter the yellow color.

In addition, the notification unit 14 generates an image of the measurement range in which patterns are arranged in each inspection area according to the disturbance energy. Then, the notification unit 14 transmits to the user an image of the measurement range in which the pattern representing the turbulence energy is arranged in each inspection area. Thereby, the user can be notified of the turbulence statistics in each inspection area.

For example, the notification unit 14 transmits an image representing the spatial distribution of turbulence energy shown in FIG. 7 to the user to notify the turbulence statistics in a predetermined range. FIG. 7 is a diagram showing the spatial distribution of turbulence energy acquired by the image analysis system according to the example. Here, the horizontal axis and the vertical axis in the upper drawing of FIG. 7 represent the X coordinate and the Y coordinate with the position of the water outlet as the origin. Furthermore, the lower band toward the drawing in FIG. 7 represents the turbulence energy on the horizontal axis, and the pattern on the band represents the pattern corresponding to the turbulence energy. The user can grasp the turbulence statistic in each predetermined range by confirming the color scheme corresponding to the lower band on the upper diagram.

For example, the user can understand the following from FIG. As shown in Fig. 7, the shear flow due to the flow velocity difference between the jet and the surrounding fluid shown in Fig. 6 induces liquid phase turbulence, and in the region where there is a strong flow velocity distribution near the outlet, a high flow velocity along the flow axis Turbulent energy is produced. This strong turbulence near the outlet causes entrainment of the surrounding fluid, which greatly affects the initial dilution of the wastewater.

Here, in this embodiment, the difference in turbulence energy is represented by patterns, but the display method is not limited to this, and the notification unit 14 may represent the difference in turbulence energy by, for example, different colors. For example, the notification unit 14 may generate an image of the measurement range by coloring each inspection area so that the lower the turbulence energy, the darker the blue, and the higher the turbulence energy, the brighter the yellow.

Next, the flow of ocean observation processing by the image analysis system 100 according to the present embodiment will be described with reference to FIG. FIG. 8 is a flowchart of ocean observation processing by the image analysis system according to the embodiment.

The camera 21 mounted on the UAV 2 hovering over the ocean captures thermographic images of a predetermined range including land at predetermined intervals during a capturing period (step S1).

The image acquisition unit 11 collects thermographic images captured at predetermined intervals during the image capturing period of the camera 21 (step S2). The image acquisition unit 11 then outputs the collected thermography image to the image correction unit 12 .

The image correction unit 12 receives the input of the thermography image from the image acquisition unit 11 . Then, the image correcting unit 12 corrects the distortion aberration of each thermographic image using the stored camera parameters. Next, the image correction unit 12 extracts SURF feature points from the land portion in each thermography image. Then, the image correction unit 12 corrects each thermography image using affine transformation so that the SURF feature points match between images (step S3). After that, the image correction unit 12 outputs the corrected thermography image to the flow velocity distribution detection unit 131 and the notification unit 14 .

The flow velocity distribution detection unit 131 receives an input of the corrected thermography image from the image correction unit 12 . Next, the flow velocity distribution detector 131 divides each thermographic image into inspection areas of a predetermined size, and applies the direct cross-correlation method to calculate the flow velocity in each inspection area. Also, the flow velocity distribution detection unit 131 obtains the flow direction in each inspection area. Thereby, the flow velocity distribution detector 131 detects the flow velocity distribution in a predetermined range (step S4). After that, the flow velocity distribution detection unit 131 outputs information on the calculated flow velocity distribution to the turbulence statistic calculation unit 132 and the notification unit 14 .

The turbulence statistic calculation unit 132 receives input of information on the flow velocity distribution in a predetermined range from the flow velocity distribution detection unit 131 . Next, the turbulence statistic calculation unit 132 calculates the average velocity in each inspection region during the imaging period, obtains the fluctuation velocity representing the deviation from the calculated average velocity, and calculates the turbulence energy using Equation (1). Then, the turbulence energy in each inspection area within a predetermined range is obtained (step S5). After that, the turbulence statistic calculation unit 132 outputs the obtained information of the turbulence energy in each inspection region to the notification unit 14 .

The notification unit 14 receives from the image correction unit 12 an input of the thermography image of the predetermined range to which the correction is applied. The notification unit 14 also receives input of information on the flow velocity distribution in a predetermined range from the flow velocity distribution detection unit 131 . Further, the notification unit 14 receives input of information on turbulence energy in each inspection region within a predetermined range from the turbulence statistic calculation unit 132 . Then, the notification unit 14 generates an image representing the flow velocity distribution in the predetermined range and an image representing the turbulence statistic in the predetermined range. After that, the notification unit 14 transmits to the user a thermographic image of a predetermined range, an image representing the flow velocity distribution in the predetermined range, and an image representing the turbulence statistics in the predetermined range, so that the temperature distribution of the ocean in the coastal area, the flow velocity The distribution and turbulence statistics are reported (step S6).

Next, the accuracy of the flow velocity measured by the image analysis system 100 according to this embodiment will be described. Here, using the flow velocity distribution shown in FIG. 6, comparison with the flow velocity measured using an ADCP (Acoustic Doppler Current Profiler) will be described.

FIG. 9 is a diagram showing flow velocities measured using an ultrasonic anemometer. Specifically, FIG. 9 shows the instantaneous flow velocity distribution obtained by towing the ultrasonic current meter parallel to the quay at the position where the X axis is 75 m indicated by the dotted line in FIGS. 6 and 7 . This measurement was performed 30 minutes after the image shown in FIG. 5 was obtained by the image analysis system 100 according to the present embodiment. Also, the measurement by the ultrasonic anemometer is the measurement of the flow velocity at 0.5 m below the water surface, which is slightly different from the measurement of the water surface flow velocity by the image analysis system 100 . However, the maximum value of the flow velocity at the position of 75 m on the X axis obtained by the ultrasonic anemometer is about 0.7 m/s, and the width of the jet is about 50 m. Match. 6 and 9, it can be seen that the flow velocity distribution measurement accuracy by the image analysis system according to the present embodiment is high.

Next, horizontal attenuation of the flow velocity and water temperature measured by the image analysis system 100 according to the present embodiment will be described. Here, description will be made using the thermography image shown in FIG. 5 and the flow velocity distribution shown in FIG. However, in the density jet, the flow axis diverges, and there are two peak values of the flow velocity in the cross section in the Y-axis direction. do.

FIG. 10 is a diagram showing horizontal attenuation of flow velocity measured by the image analysis system according to the embodiment. Specifically, FIG. 10 represents the horizontal distribution of the average flow velocity during the shooting period in the flow axis direction at the position of 75 m on the X axis in FIG. Here, _yu is the horizontal distance from the point where the peak value of the flow velocity is obtained, and _bu is the width at which half the value of the peak value of the flow velocity is obtained, that is, the half width at half maximum. y _u /b _u represented by the horizontal axis in FIG. 10 is a value obtained by normalizing y _u with b _u . The vertical axis in FIG. 10 represents the dimensionless value of the flow _velocity U with the cross-sectional maximum flow velocity Um. Furthermore, the graph 301 in FIG. 10 is a Gaussian function represented by the following formula (2) centered on the cross-sectional maximum value.

FIG. 11 is a diagram showing a horizontal distribution of water temperatures measured by the image analysis system according to the embodiment. FIG. 11 shows the horizontal distribution of the average temperature during the imaging period in the flow axis direction at the position of 75 m on the X axis in FIG. Here, _yT is the horizontal distance from the point where the cross-sectional maximum value of the water temperature is obtained, and _bT is the width at which half the value of the cross-sectional maximum value of the water temperature is obtained, that is, the half width at half maximum. y _τ /b _τ represented by the horizontal axis in FIG. 11 is a value obtained by normalizing y _τ with b _τ . The vertical axis in FIG. 11 represents the dimensionless value of the water temperature difference ΔT with the cross-sectional maximum water temperature difference _ΔTm . Furthermore, the graph 302 in FIG. 11 is a Gaussian function represented by the following formula (3) centered on the cross-sectional maximum value.

The flow velocity and water temperature obtained by the image analysis system 100 according to this embodiment substantially match graphs 301 and 302 as shown in FIGS. Here, it is known from laboratory experiments that the horizontal damping of both the flow velocity value and the water temperature value are described by Gaussian distribution as a property of the jet flow. That is, since the flow velocity and the water temperature obtained by the image analysis system 100 according to the present embodiment approximate Gaussian distribution, it can be said that the image analysis system 100 according to the present embodiment can accurately measure the flow velocity and the water temperature. .

Next, turbulence statistics measured by the image analysis system 100 according to this embodiment will be described. Here, the spatial distribution of turbulence energy shown in FIG. 7 will be used for explanation.

FIG. 12 is a diagram showing the spatial distribution of the standard deviation of water temperature fluctuations. Here, the deviation θ from the average water temperature T was calculated to obtain the spatial distribution of the standard deviation of the water temperature fluctuation. Here, the horizontal axis and the vertical axis in the upper drawing of FIG. 12 represent the X coordinate and the Y coordinate with the water outlet position as the origin. Furthermore, the lower band toward the drawing in FIG. Represents the pattern corresponding to each value. The square root of the symbol with a horizontal bar above θθ in FIG. 12 corresponds to the standard deviation of the water temperature fluctuation. Also, here, the surface water temperature of the environmental field was set to 19.6°C.

Here, since the turbulence entrains the surrounding fluid into the jet and causes fluctuations in the water temperature, the position of high standard deviation of the water temperature fluctuation in Fig. 12 and the position of high turbulence energy in Fig. 7 induced by the turbulent shear stress. are almost the same. That is, it can be said that the state of the ocean obtained from the water temperature fluctuation obtained from the water temperature matches the state of the ocean obtained from the turbulence energy obtained by the image analysis system 100 according to the present embodiment. From this, it can be said that the image analysis system 100 according to the present embodiment can grasp the relationship between the fluid motion and the water temperature fluctuation.

As described above, the image analysis system according to the present embodiment can acquire the spatial distribution of water temperature in a short time, and obtain the water temperature distribution, the water flow distribution, and the turbulence statistics with high resolution with high accuracy. It is possible to easily and accurately observe the ocean conditions in coastal areas. For example, the image analysis system according to the present embodiment can accurately capture the diffusion phenomenon of local density jets in coastal areas.

(Hardware configuration)
FIG. 13 is a hardware configuration diagram of the analysis device. The analysis device 1 has a CPU (Central Processing Unit) 91, a memory 92, a network interface 93 and a hard disk 94, as shown in FIG. 13, for example. CPU 91 is connected to memory 92, network interface 93 and hard disk 94 via a bus.

The hard disk 94 stores various programs including programs for implementing the functions of the image acquisition unit 11, the image correction unit 12, the detection unit 13, and the notification unit 14 illustrated in FIG.

The CPU 91 reads various programs from the hard disk 94, develops them on the memory 92, and executes them. Thereby, the CPU 91 implements the functions of the image acquisition unit 11, the image correction unit 12, the detection unit 13, and the notification unit 14 illustrated in FIG.

A network interface 93 is an interface for communicating with an external device. Notification of the ocean temperature distribution, flow velocity distribution and turbulence statistics in the coastal zone to the external device by the notification unit 14 is performed via the network interface 93 .

1 analysis device 2 UAV
REFERENCE SIGNS LIST 11 image acquisition unit 12 image correction unit 13 detection unit 14 notification unit 21 camera 100 image analysis system 131 flow velocity distribution detection unit 132 turbulence statistics calculation unit

Claims (7)

A camera that is mounted on a UAV (Unmanned Aerial Vehicle) and captures a plurality of thermographic images representing water temperature distribution on the water surface from the flying UAV;
a correction unit that extracts land-side feature points from each of the thermographic images captured by the camera, performs affine transformation based on the extracted land-side feature points, and corrects mutual positions of the thermographic images; ,
a detection unit that acquires the water temperature distribution pattern from the thermography image corrected by the correction unit, and detects the state of water flow based on a change in the acquired water temperature distribution pattern. Device. 2. The image analysis apparatus according to claim 1, wherein the detection unit acquires water flow velocity and turbulence statistics as the state of the water flow. 2. The image analysis apparatus according to claim 1, wherein the detection unit acquires the state of the water flow using a direct cross-correlation method for the acquired water temperature distribution pattern. The camera captures the thermographic image of the water surface including land;
The image analysis apparatus according to claim 1 , wherein the correction unit extracts the land side feature point from the land on the thermography image. 5. The image analysis apparatus according to any one of claims 1 to 4 , further comprising a notification unit that notifies the water temperature distribution and the state of the water flow detected by the detection unit. Let the camera mounted on the flying UAV take multiple thermography images of the water surface,
extracting land-side feature points from each of the thermographic images captured by the camera , performing affine transformation based on the extracted land-side feature points, and correcting the mutual positions of the thermographic images;
Obtaining a water temperature distribution pattern from the corrected thermography image,
An image analysis method, characterized by detecting a state of a water flow based on a change in the obtained pattern of water temperature distribution. Land-side feature points are extracted from each of a plurality of thermographic images of a water surface photographed by a camera mounted on a flying UAV, and affine transformation is performed based on the extracted land-side feature points, so that the thermographic images are mutually correct the position of
Obtaining a water temperature distribution pattern from the corrected thermography image ,
An image analysis program for causing a computer to execute a process of detecting a state of a water flow based on a change in the acquired water temperature distribution pattern.

Images