JP2010014491A - Offshore monitoring system method - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide an offshore monitoring system capable of heightening monitoring accuracy by an inexpensive and lightweight constitution. <P>SOLUTION: In this offshore monitoring system is equipped with an imaging means 20 for imaging a measuring object M on the ocean, and generating time-series image data; and an analyzer 30 for analyzing the image data, and generating position information of the measuring object M. The analyzer 30 calculates each coordinate value of the measuring object M at each time, and determines the position information of the measuring object M from a median or a mean value of coordinate values within a prescribed time. <P>COPYRIGHT: (C)2010,JPO&INPIT

Description

  The present invention relates to an offshore monitoring system and method for monitoring offshore floating objects.

  In recent years, the need for observation of floating substances on the ocean, such as melting of drift ice due to global warming, large-scale occurrence of harmful plankton, oil spills and marine litter associated with ship accidents, has increased in recent years. It is required to measure with accuracy. As an apparatus for measuring the position of an object, for example, configurations disclosed in Patent Document 1 and Patent Document 2 are known.

  The device of Patent Document 1 projects a composite continuous mosaic image from a plurality of predetermined line image data including positioning data and tilt data of an imaging device at an altitude of 0 m, and calculates a buoy trajectory by matching calculation.

The apparatus of Patent Literature 2 captures a target object in time series using an imaging apparatus that moves relative to the target object, and extracts feature points from the obtained series of captured images. Next, tracking processing of each feature point is performed, a stereo image is selected from a series of captured images to which the corresponding feature point is attached, and orientation and three-dimensional measurement are performed. Then, the suitability of each corresponding feature point is determined based on the error range of the corresponding feature point calculated by the three-dimensional measurement, the corresponding feature point determined to be inappropriate is excluded, and the orientation and the three-dimensional measurement are performed again. By doing so, the measurement accuracy is improved.
JP-A-8-211088 JP 2007-327938 A

  However, since the apparatus disclosed in Patent Document 1 needs to accurately grasp the position and shooting angle of the imaging device with respect to the object at the moment of shooting in order to perform the matching calculation, vibration caused by wind or the like. In the shooting flight that is easily affected by this, there is a great restriction on the improvement of the measurement accuracy of the position and angle of the imaging device. For example, when using a high-precision measuring instrument having a vibration-proof function, not only is the equipment expensive, but the weight is excessive. For this reason, it is extremely difficult to capture images from ship-towed balloons that have a limited weight, and quantitative monitoring technology from ships close to the observation site has not been put into practical use despite increasing demand. .

  Further, the apparatus disclosed in Patent Document 2 requires stable feature points for correction using a stereo image. However, when the shooting location is offshore, most of the feature points are affected by uncertain factors such as wind, waves, and tide flow, and move greatly in a short time, making it difficult to obtain highly accurate feature points. Therefore, there is a limit to the improvement of measurement accuracy.

  Therefore, an object of the present invention is to provide an offshore monitoring system and method capable of increasing the monitoring accuracy with an inexpensive and lightweight configuration.

  The object of the present invention includes an imaging device that images a measurement object on the ocean and generates time-series image data, and an analysis device that analyzes the image data and generates position information of the measurement object. The analysis device is achieved by an offshore monitoring system that calculates coordinate values of a measurement object at each time and obtains position information of the measurement object from a median value or an average value of the coordinate values within a predetermined time.

  In addition, the object of the present invention is to provide an imaging step of imaging a measurement object on the ocean to generate time-series image data, and an analysis step of analyzing the image data to generate position information of the measurement object. The analysis step is achieved by an offshore monitoring method that calculates a coordinate value of the measurement object at each time and obtains position information of the measurement object from a median value or an average value of the coordinate values within a predetermined time. .

  According to the present invention, it is possible to provide an offshore monitoring system and method capable of improving ship monitoring accuracy and enabling ship observation with an inexpensive and lightweight configuration.

Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. FIG. 1 is a schematic configuration diagram of an offshore monitoring system according to an embodiment of the present invention. As shown in FIG. 1, the offshore monitoring system 1 includes a plurality of buoys 10, an imaging device 20, and an analysis device 30.

The buoyant body 10 is made of a buoy that can float on the ocean, and a GPS logger is installed at the center of the upper surface. The GPS logger includes a receiving unit 12 that receives GPS signals from GPS satellites, and a recording unit 14 that records time-series GPS signals. The GPS signal can be measured with relatively high accuracy even in a low-cost and lightweight non-differential GPS using, for example, a satellite navigation augmentation system (MSAS) for a transport multipurpose satellite.
The surface of each buoy 10 is given a visually distinguishable color while floating on the ocean S. The greater the number of buoys 10, the higher the measurement accuracy. In the present embodiment, five types of buoyant bodies 10 having peach, green, red, yellow, and orange colors having a large color difference from the sea surface color are prepared. Each buoy 10 is dropped in the vicinity of the measurement object M, as will be described later. In this case, it is preferable to use a buoyant of a color having a large color difference from the color of the measurement object. A part of the offshore ship 28 equipped with GPS may be used as one of the buoys. The buoy does not necessarily have to be a single color. However, in that case, it is necessary to add adjacent information to the identification condition of the buoyant body.

  The imaging device 20 includes an imaging unit 22 that acquires image data by imaging, and an image recording unit 24 that records the acquired image data. The imaging unit 22 and the image recording unit 24 are mounted on the balloon ship 21. Thus, the camera can be imaged from the air. The balloon ship 21 can be towed by an offshore ship 28 via a mooring line 23, and the horizontal photographing position of the imaging unit 22 can be controlled by the movement of the offshore ship 28, and the vertical photographing position. Can be adjusted by winding or unwinding the mooring line 23. In addition, the offshore ship 28 has a wireless transmission unit 27 for remotely operating the imaging unit 22, and the imaging direction can be adjusted via a wireless reception unit 25 installed on the balloon ship 21.

  The analysis device 30 includes an input unit 32 for inputting time-series GPS signals and image data recorded in the buoy 10 and the imaging device 20, an analysis processing unit 34 for analyzing the input data, and an analysis result. And an output unit 36 for outputting by screen display or the like, and can be constituted by, for example, a general personal computer. The analysis device 30 can be analyzed at an observation place if it is mounted on the offshore ship 28, but it can also be analyzed at other places after photographing.

  Next, a method for performing offshore monitoring using the offshore monitoring system 1 having the above-described configuration will be described. First, a plurality of buoys 10 having different colors are moved to the vicinity of a measurement object floating on the sea surface, for example, by being mounted on an offshore ship, and dropped on the ocean. The measurement object is not particularly limited, and examples thereof include offshore drifting garbage, spilled oil, plankton, and drift ice. Each buoy 10 floating on the ocean receives a GPS signal from a GPS satellite and records latitude information and longitude information at each time in the recording unit 14. Thus, the position of each buoyant body 10 serving as a reference point can be grasped. The buoy body 10 may include a wireless transmission unit for transmitting a received GPS signal to the analysis device 30, whereby the analysis device 30 can grasp the position of each buoy body 10 in real time.

  Next, the imaging device 20 is manually operated remotely, and imaging is performed so that each buoy 10 and the measurement object always exist in the imaging region. The obtained time-series image data is stored in the recording unit 24. The image data can also be transmitted to the analysis device 30 in real time. Further, only the low-resolution image data may be wirelessly transmitted to the offshore ship 28 to control the tilt angle of the imaging unit 22, and the high-resolution image data may be recorded in the recording unit 24.

  The analysis device 30 obtains the coordinate value of the measurement object based on the GPS signal and the image data respectively input from the buoyant body 10 and the imaging device 20 within a predetermined time. This procedure will be described with reference to the flowchart shown in FIG.

  First, a group of abnormal color regions are detected from image data (step S1). For example, in a predetermined color space, after extracting a sea area in the image data from the median value of color coordinates in small area units, an abnormal color area included in the sea area is detected. The color space to be used is preferably a uniform color space such as CIELV or CIELAB in which the color difference perceived by humans matches the Euclidean distance in the color space. However, when it is desired to detect a buoyant and a measurement object having a minute color difference that are difficult for humans to perceive, an uneven color space suitable for the buoyant may be used.

  It is preferable that unnecessary abnormal colors other than the measurement object such as a land area or a ship are not included in the image data in order to shorten the analysis time. If included, for example, a region that is clearly not a sea region from the median value of the color space of the small region, and a coastline neighborhood with a large variance value of the boundary region between the sea and land that are in the vicinity are extracted as a land region. Thus, land green, quay, clouds, sky, observation ship, land objects reflected on the water surface, etc. can be excluded from the abnormal color area. The boundary may be detected by edge detection, or a simpler method such as excluding pixels in a color gamut that is clearly not a sea color empirically may be used. A group of abnormal color areas having a large color difference from the sea is detected from the sea areas thus extracted. The detection can be performed, for example, by extracting pixels whose Mahalanobis distance between the luminance L on the CIELV and the color UV is a predetermined value or more.

A group of abnormal color regions is recognized as a plurality of pixel clusters, and the representative values of the color and pixel coordinates on the image of each cluster are calculated. For calculating the representative value, for example, a well-known Mean Shift method can be preferably used as a clustering method such as a color space. For the Mean Shift method, for example, the following document is helpful. Dorin Comaniciu and Peter Meer.Mean shift: A robust approach toward feature space analysis.IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.24, No.5, pp.603-619, 2002
The Mean Shift method is a method in which classification target points dispersed in a plurality of normal distributions in a certain coordinate system are sequentially moved to local average coordinates and converged to the cluster center. This is a simple method in which it is not necessary to set the number of classifications in advance simply by giving a window parameter that controls the criterion for determining how close the neighborhood is to be similar. For example, in the present embodiment, the Mean Shift method is applied to the four-dimensional coordinates that collectively process the two coordinate systems of the color coordinates (U, V) and the pixel coordinates (x, y), and each determined block is determined. The color, position, and size on the image of any number of offshore drifting objects that have any color different from the ocean can be calculated at once by the center of gravity of the color coordinates, the center of gravity of the pixel coordinates, and the number of classified pixels. . The window parameter is set for each of color coordinates and pixel coordinates. It is preferable to set according to the color variation of each buoyant or measurement object and the size on the image. Here, the luminance L is not used as a criterion for judgment because it does not always follow the normal distribution from experience, but the luminance of the pixels classified based on the color coordinates (U, V) and the pixel coordinates (x, y) is not used. The average luminance is calculated for each pixel block. The group of abnormal color areas detected in this way corresponds to some offshore drifting objects, and each buoyant body exists in these abnormal color areas.

  Next, a plurality of buoys are determined from a group of abnormal color regions (step S2). Since the detected abnormal color area may contain noise other than the buoyant body due to white waves or sea surface reflection, it is necessary to extract only the buoyant body. Therefore, for example, when there are five buoys, five color centroids are detected by combining five abnormal color regions in a predetermined color space, and the color difference is obtained by comparing with color information stored in advance for each buoy. A combination of abnormal color regions that minimizes the sum of the color differences is determined. In this way, five reference points in the image data can be specified. The color centroid and pixel centroid extracted in this way indicate the color coordinates C (L, U, V) and pixel coordinates (xi, yi) of each buoyant in the captured image (i = 1 to L). However, in this embodiment, L = 5).

  In the absolute coordinate system (for example, WGS-84 coordinate system), the coordinate value (Xi, Yi) of the buoyant and the coordinate value P (XP, YP) of the object to be measured are different from those of land objects. It moves with the passage of time under the influence of the flow of However, the coordinate values (Xi, Yi) of the buoy can be grasped in a time series from the latitude and longitude of GPS signals of a plurality of buoys. In this embodiment, since the buoy and the measurement object are photographed from various positions and angles, even in the imaging system, the pixel coordinates (xi, yi) of the buoy and the pixel coordinates P (xp of the measurement object) , yp) is very discontinuous in time series. Therefore, the arbitrary measurement object extracted in step S1 by the projective transformation described below with reference to the relationship between the coordinate value (Xi, Yi) obtained from the GPS signal and the pixel coordinate (xi, yi) obtained in step S2. The coordinate value G (XG, YG) of the object is calculated (step S3).

  Here, the projective transformation method will be described. The following formulas 1 and 2 are established in the pixel coordinate system and the absolute coordinate system.

ここでｂjとｃj(j=1〜5)は、ＧＰＳ信号の受信により絶対座標が既知であるn個の基準点から、次の数式３〜６で示される最小二乗法により算出される。

Here, bj and cj (j = 1 to 5) are calculated from the n reference points whose absolute coordinates are known by receiving the GPS signal by the least square method expressed by the following equations 3 to 6.

すなわち、画像データに含まれるi=1〜n（実施例ではn=5である）の各浮標体の画素座標(xi,yi)と座標値(Xi,Yi)との関係を基準としてｂjとｃj(j=1〜5)を算出し、数式１及び２を用いて測定対象物の画素座標(xp,yp)から座標値 (XG,YG)を算出することができる。

That is, with reference to the relationship between pixel coordinates (xi, yi) and coordinate values (Xi, Yi) of each buoyant body of i = 1 to n (n = 5 in the embodiment) included in the image data, bj and cj (j = 1 to 5) is calculated, and using Equations 1 and 2, the coordinate value (XG, YG) can be calculated from the pixel coordinates (xp, yp) of the measurement object.

With the above method, the coordinate value of the measurement object at each time obtained from each image data is extracted within a predetermined time, and the coordinate value of the measurement object is corrected by calculating the time average value (step) S4). In actual measurement, particularly when the position and orientation of the imaging device 20 vary significantly over time, the measurement error of the coordinate value of the obtained measurement object is large. The present inventors pay attention to the fact that the main factor causing such a measurement error in offshore monitoring is a vibration system such as a wave or wind, and from the median value within a predetermined time of the coordinate value of the measurement object. It was found that by performing projective transformation, the absolute position, size, shape, etc. of the measurement object can be obtained with high accuracy. Such a method is effective, for example, when detection or identification of some buoys within a predetermined time has failed, or when GPS signals are temporarily lost. Further, when an average value is used instead of the median value, an effect equivalent to that can be expected, and the analysis time can be shortened.
The predetermined time and the number of image data for calculating the time average value are not particularly limited. For example, 50 to 100 (or more) image data are captured in 10 to 30 minutes. Is preferred. In addition, it is possible to grasp the movement trajectory of the measurement object by increasing the predetermined time or increasing the number of shots per predetermined time. If the movement trajectory can be grasped, for example, it is possible to identify a white wave that frequently disappears within a predetermined time and a white drifting object that does not frequently occur.

  Thus, according to the offshore monitoring system of the present embodiment, it is possible to measure the position of the measurement object with high accuracy by an inexpensive configuration even on the sea where there is no clear position reference, Drift ice that melts due to warming, patchiness of jellyfish that appears in large numbers, tidal flats and seaweed beds that decrease due to landfill, spilled oil of different sea color, red tide, distribution of river plume after flood, navigation safety measures And, it can be expected to contribute to environmental policy by applying to various offshore monitoring such as maritime search. Further, by providing a resistor below the surface of the buoyant body 10 or by introducing a tracer provided with a resistor around the buoyant body 10, a Lagrangian flow state investigation can be performed from the movement trajectory.

    As mentioned above, although one Embodiment of this invention was explained in full detail, this invention is not limited to said embodiment. For example, in this embodiment, the GPS signal receiver 12 is mounted on the buoy 10, but the GPS signal receiver is provided on the imaging device 20, and the position information of the imaging device 20 is used instead of the buoy. You may comprise so that it may identify. In this case, since the imaging apparatus 20 includes a position recording unit or a position transmission unit that records the position, inclination, and focal length of the imaging apparatus 20, the position information of the measurement object can be obtained as in the present embodiment. In addition, it is possible to eliminate the operation of loading and collecting the buoyant body 10. Further, the mounting location of the imaging device is not limited to the ship-towed balloon. For example, it may be mounted on a remote control helicopter, or may be mounted on the top of a ship's pole, etc., if sufficient altitude is secured to widen the field of view. The calculation method will be described below.

  FIG. 5 is a diagram for explaining the geometric relationship among the camera, the image plane, and the measurement object. This is expressed by the following mathematical expressions 7 and 8. Here, P (Xp, Yp, Zp) is an arbitrary coordinate value on the image plane, and P (xp, yp) is a coordinate in the pixel coordinate system corresponding thereto. O (Xo, Yo, Zo) is the coordinate value of the imaging unit (camera), G (XG, YG, ZG) is the coordinate value of the object to be measured, ω, φ, κ are the images for the X, Y, and Z axes Is the rotation angle of the unit (camera), and f is the focal length of the imaging unit (camera). In the first term on the right side of Equation 7, the pixel coordinates are rotated by -ω with respect to the X axis, -φ with respect to the Y axis, and further with -κ rotation with respect to the Z axis. The following Expression 8 is a condition indicating that the three points O, G, and P in the absolute coordinate system are on the same straight line.

撮像装置２０の位置及び傾斜を記録または送信可能な構成においては、Xo、Yo、Zo、ω、φ、κ、ｆがいずれも既知であるから、数式７及び８により、測定対象物の画素座標P（xp,yp）から、絶対座標系の座標値 G(XG,YG,ZG)を求めることができる。さらに、ここで算出した座標値を、ＧＰＳを搭載した洋上船２８などを基準点とすることで、補正しても良い。こうして求めた座標値の、所定時間内の中央値または平均値を用いることで、測定精度が低い軽量の傾斜計を用いた場合であっても、測定対象物の位置情報を有用な精度で得ることができる。

Since Xo, Yo, Zo, ω, φ, κ, and f are all known in the configuration capable of recording or transmitting the position and inclination of the imaging device 20, the pixel coordinates of the measurement object are expressed by Equations 7 and 8. From P (xp, yp), the coordinate value G (XG, YG, ZG) of the absolute coordinate system can be obtained. Furthermore, the coordinate value calculated here may be corrected by using the offshore ship 28 equipped with GPS as a reference point. By using the median value or the average value of the coordinate values thus obtained within a predetermined time, the position information of the measurement object can be obtained with useful accuracy even when a lightweight inclinometer with low measurement accuracy is used. be able to.

  Based on the Example of this invention, this invention is demonstrated still in detail. However, the present invention is not limited to the following examples.

  A five-color drifting buoy (buoyant body) placed on the ocean was photographed from a small balloon (imaging device) equipped with a consumer-use compact camera (RiCOH GR Digital). Each drifting buoy is equipped with a consumer-use GPS logger (Wintec DLG-201), and by performing projective transformation on all 74 images acquired within 23 minutes, the position information of each drifting buoy Acquired. Each drifting buoy uses a GPS logger that uses an MSAS signal and is guaranteed to be accurate to less than ± 3m. The movement trajectory of each drifting buoy based on the GPS signal is shown in FIG. 3 (a), and the median value is shown in FIG. 3 (b).

  FIG. 4A shows the difference between the position recorded by the GPS logger and the position specified by projective conversion for each image data for one of the drifting buoys (G: green). As shown in FIG. 4A, when projective transformation is performed for each image data, it can be seen that there is variation between the actual position within a range of ± 10 m.

  On the other hand, when the median of time-series coordinate values obtained from each image data is obtained, as shown by a diamond (G: green) in FIG. It can be seen that is significantly improved. Similar results were obtained for the other four buoys (Y: yellow, P: peach, O: orange, R: red), and as shown in FIG. The range is 1 m.

  In the present embodiment, projective transformation is performed including image data that failed to be detected or identified, but measurement accuracy can be further improved by determining and removing the failed image from the acquired image data. The failure image can be determined, for example, based on whether or not the error of the projective transformation exceeds a predetermined distance.

1 is a schematic configuration diagram of an offshore monitoring system according to an embodiment of the present invention. It is a flowchart for demonstrating the principal procedure of the offshore monitoring method which concerns on one Embodiment of this invention. It is a figure which shows the measurement result of the Example of this invention. It is a figure which shows the measurement precision of the Example of this invention. It is a figure for demonstrating an example of a measurement principle.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Offshore monitoring system 10 Floating body 12 Reception part 14 Recording part 20 Imaging device 22 Imaging part 24 Recording part 30 Analysis apparatus 32 Input part 34 Analysis processing part 36 Output part M Measurement object

Claims (4)

An imaging device that images a measurement object offshore and generates time-series image data;
An analysis device that analyzes the image data and generates position information of the measurement object;
The offshore monitoring system in which the analysis device calculates coordinate values of the measurement object at each time and obtains position information of the measurement object from a median value or an average value of the coordinate values within a predetermined time. It further comprises a plurality of buoys that can float on the ocean,
The buoy or the imaging device includes a GPS receiver that receives GPS signals from GPS satellites,
The offshore monitoring according to claim 1, wherein the analysis device calculates a coordinate value of a measurement object at each time, based on the image data and the GPS signal, with each buoyant body included in the image data as a reference point. system. Each buoy is different in color on the outer surface,
The analysis apparatus detects a group of abnormal color areas from the image data, and extracts a combination of the abnormal color areas that gives a group of color centroids in which the sum of color differences from the color information of each buoyant body is minimized. The offshore monitoring system according to claim 2, wherein a reference point of the image data is specified. An imaging step of imaging a measurement object offshore and generating time-series image data;
Analyzing the image data to generate position information of the measurement object, and
The analysis step is an offshore monitoring method that calculates the coordinate value of the measurement object at each time and obtains the position information of the measurement object from the median value or average value of the coordinate values within a predetermined time.

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