JP2017104025A - Fishing support system using unmanned aircraft - Google Patents

Abstract

PROBLEM TO BE SOLVED: To establish and provide an optimum fishing support system using an unmanned aircraft (drone).SOLUTION: A fishing support system is mounted with a fishing boat side control device 11 and a fishing boat side wireless transmitter-receiver 12 mounted on a fishing boat. An unmanned aircraft (drone) includes an aircraft side control device 21, an aircraft side wireless transmitter-receiver 22 for performing transmission and reception of data with the fishing boat side wireless transmitter-receiver 12, an aircraft position grasping device 27 for grasping the position of the unmanned aircraft (drone) and inputting it in the aircraft side control device 21, and imaging devices 23 and 24 for inputting image data acquired by imaging the water surface in the aircraft side control device 21. The aircraft side control device 21 detects a fish school from the image data acquired from the imaging devices 23 and 24, and sends the data associating the detected fish school with the position of the unmanned aircraft (drone) when it is acquired from the aircraft position grasping device 27 to the fishing boat side control device 11 through the aircraft side wireless transmitter-receiver 22 and the fishing boat side wireless transmitter-receiver 12.SELECTED DRAWING: Figure 2

Description

  The present invention relates to a fishery support system using an unmanned aerial vehicle that supports a fishery by searching and detecting a school of fish using the unmanned aerial vehicle.

  In general, fishing by fishing boats is carried out through a procedure of “fishing ground movement” → “fish school search” → “fishing operation” → “landing”. In this procedure, “fish school search” is performed by using a fish finder to determine the existence of a school of fish, its fish species and scale at a position determined by the ruler's rule of thumb. As a result of the measurement by the fish finder, if there is no fish school to be operated, the process returns to the fishing ground movement, and the fish finder is again searched by the fish finder.

  Therefore, in order to reliably search for a school of fish in a wide range of fishing grounds, it is necessary to move and search many times, or to operate on a fleet scale with a large number of fishing boats, both in terms of cost and environment. Waste is a problem. In the future, in order to achieve an environmentally friendly blue economy, it is an urgent task to save fishery procedures.

  Especially in fisheries such as saury fisheries, the efficiency of fish discovery is greatly related to the increase in catch, and therefore, the fluctuations in the location of remote fish schools can be visualized from the point where the ship is operating. In addition, it is expected that at the time of search navigation by a fish finder, it will be possible to grasp the operation candidate sites at a plurality of points by conducting fish search in a wider area and to directly improve the operation efficiency.

  In recent years, it has been proposed that unmanned aerial vehicles equipped with cameras can be used for fish school search (for example, Patent Documents 1 to 3).

Special table 2012-524695 gazette Special table 2013-539729 gazette Japanese Patent No. 5214599

  Although these Patent Documents 1 to 3 describe a method for collecting unmanned aircraft, a line capturing method using unmanned aircraft, and a wireless control method for unmanned aircraft, how to use unmanned aircraft to support fisheries such as fish school search. There is no disclosure about the specific configuration of what to do.

  Accordingly, an object of the present invention is to establish an optimal fishery support system using unmanned aerial vehicles.

  According to the present invention, a fishing support system using an unmanned aerial vehicle includes a fishing boat side control device mounted on a fishing boat, a fishing boat side wireless transmission / reception device mounted on a fishing boat, and an aircraft side control device mounted on an unmanned aircraft. An aircraft-side radio transceiver that is installed in an unmanned aerial vehicle and transmits / receives data to / from a fishing boat-side radio transceiver, and an unmanned aerial vehicle that detects the position of this unmanned aerial vehicle An aircraft position grasping device for inputting, and an imaging device mounted on an unmanned aerial vehicle for inputting image data obtained by photographing the water surface to the aircraft-side control device are provided. The aircraft-side control device detects a fish school from the image data obtained from the imaging device, and associates the detected fish school with the position of the unmanned aircraft at that time obtained from the aircraft position determination device, the aircraft-side wireless transmission / reception device, and It is comprised so that it may send to a fishing boat side control apparatus via a fishing boat side radio | wireless transmission / reception apparatus.

  Image data is acquired by imaging the water surface with an imaging device mounted on an unmanned aerial vehicle, a fish school is detected from the acquired image data, and data relating the detected fish school and the position of the unmanned aircraft at that time is transmitted to a fishing boat. Therefore, the fishing boat can automatically find a remote school of fish with only a few instructions. In the natural world, seabirds are searching for fish schools from the sky, and in the present invention, a fishery support system is constructed using a highly mobile small unmanned aerial vehicle (drone). This can save labor and improve efficiency.

  The aircraft-side control device is configured to find a fish high probability encounter area, and is preferably configured to detect a fish within the found fish high probability encounter area.

  The aircraft-side control device is preferably configured to detect a school of fish by moving the image data obtained from the imaging device with the unmanned aircraft stationary.

  In this case, the aircraft-side control device detects the school of fish by determining whether or not the cluster of pixels in which the chromaticity has changed between one continuously shot frame and the next frame is an elliptical approximation. More preferably, it is configured.

  The aircraft-side control device may be configured to detect a school of fish by determining whether or not the length of the major axis of the elliptical approximate cluster is equal to or greater than a predetermined value and a plurality of elliptical approximate clusters are adjacent to each other. preferable.

  It is also preferable that the aircraft-side control device be configured to elliptically approximate the outer circumference of the group of elliptical approximate clusters and detect the size of the fish school by obtaining the area of the cluster group that has been elliptically approximated.

  The aircraft-side control device determines whether the major axis of the elliptical approximate cluster is within a predetermined range or whether the ratio of the minor axis to the major axis (minor axis / major axis) of the elliptical approximate cluster is equal to or less than a predetermined value. It is also preferable that the determination is made.

  It is also preferable that the moving direction of the fish school is obtained from the slope of the regression line obtained from the center point of the fish school for each image during the continuous shooting period.

  It is also preferable that the moving speed of the school of fish is obtained from the linear distance between the center point of the fish school at the start of continuous shooting, the center point of the fish school at the end of continuous shooting, and the continuous shooting period.

  The unmanned aircraft includes a temperature sensor that measures the sea surface temperature and a sea color sensor that measures the phytoplankton concentration, and the aircraft-side control device includes the sea surface temperature measured by the temperature sensor and the phytoplankton measured by the sea color sensor. It is also preferable to be configured to find a fish school high probability encounter area from the concentration.

  The aircraft-side control device shall be configured to find a fish school high probability encounter area from the sea surface temperature measured by the temperature sensor, the sea surface temperature gradient based on the sea surface temperature, and the phytoplankton concentration measured by the sea color sensor. Is also preferable.

  It is further equipped with a fishing boat position grasping device that is mounted on a fishing boat and grasps the position of the fishing boat and inputs it to the fishing boat side control device. The fishing boat side control device grasps the position of the fishing boat and the aircraft position obtained from the fishing boat position grasping device. It is also configured to calculate the distance between the fishing boat and the unmanned aircraft from the position of the unmanned aircraft obtained from the device, and to instruct the unmanned aircraft to return to the fishing boat when the calculated distance exceeds the predetermined location preferable.

  The imaging apparatus preferably includes a visible light camera used in a bright state and an infrared camera used in a dark state.

  The aircraft-side control device is also preferably configured to return the unmanned aircraft to the fishing boat when the battery charge of the unmanned aircraft decreases.

  It is also preferable that the aircraft-side control device is configured to fly an unmanned airplane along a preset transect route.

  According to the present invention, the fishing boat can automatically find a remote school of fish only by issuing several instructions. In the natural world, seabirds are searching for fish schools from the sky, and in the present invention, a fishery support system is constructed using a highly mobile small unmanned aerial vehicle (drone). This can save labor and improve efficiency.

It is a figure showing roughly the whole composition in one embodiment of a fishery support system of the present invention. It is a block diagram which shows roughly the electrical structure of the fishing boat and drone in embodiment of FIG. It is a flowchart which shows roughly the flow of control operation of the fishing boat in embodiment of FIG. It is a flowchart which shows schematically the flow of the control operation | movement of the drone in embodiment of FIG. It is a flowchart which shows schematically the flow of the control operation | movement of the drone in embodiment of FIG. It is a flowchart which shows schematically the flow of the control operation | movement of the drone in embodiment of FIG. 4 is a flowchart schematically showing a flow of fish high probability encounter area search processing shown in FIGS. 4a and 4b. It is a flowchart which shows roughly the flow of the discrimination process of the fish school and fish kind shown in FIG. 4c. It is a figure explaining the channel by a line transect method. It is a figure explaining the extraction process of a suitable water temperature range and a suitable water temperature gradient area about a sea surface temperature. It is a figure explaining the extraction process of a suitable density range about a phytoplankton density | concentration. It is a figure explaining the process which extracts a fish school high probability encounter area from a suitable water temperature range, a suitable water temperature gradient area, and a suitable concentration area. It is a figure which shows an example of the image displayed on the display of a fishing boat side control apparatus. It is a figure explaining the calculation method of the moving direction and moving speed of a school of fish in other embodiment of the fishery support system of this invention.

  FIG. 1 schematically shows the overall configuration of an embodiment of the fishery support system of the present invention, and FIG. 2 schematically shows the electrical configuration of the fishing boat side and drone side in this fishery support system.

  In FIG. 1, reference numeral 10 denotes a fishing boat for saury fishing, for example, and 20 denotes a drone that is a radio-operated unmanned aerial vehicle that starts from the fishing boat 10 as a base. The drone 20 in the present embodiment is a rotorcraft that can be stationary in the air.

  As shown in FIG. 2, the fishing boat 10 is connected to a fishing boat side control device 11 that controls the fishing boat side in this fishery support system, and the fishing boat side control device 11, and For example, a fishing boat side wireless transmission / reception device 12 for performing data communication with a wireless LAN (local area network) of, for example, IEEE 802.16-2004, and a GPS (global positioning) device 13 for acquiring the position of the fishing boat 10 are provided. Is provided. The fishing boat control apparatus 11 includes a digital computer 11a, for example, a display unit 11b and an operation unit 11c using liquid crystal or the like. The display unit 11b and the operation unit 11c may be provided independently of each other, or may be integrated when a touch panel type liquid crystal display is used. The fishing boat side wireless transmission / reception device 12 includes a wireless unit 12a and an antenna 12b. The fishing boat side control device 11 may be configured to acquire map information from a map service on the web. For example, it may be configured to connect to a smartphone via Bluetooth (registered trademark), acquire map information from a map service on the web, and display the map information on the display unit 11b. Moreover, you may comprise so that ocean information, such as seawater temperature distribution, may be acquired from the database of artificial satellite NOAA.

  The drone 20 is connected to the drone side control device 21 that controls the drone side in the fishery support system and the drone side control device 21, and is connected to the fishing boat side wireless transmission / reception device 12 by the above-described wireless LAN. A drone-side wireless transmission / reception device 22 for performing data communication, a super-wide-angle (for example, a focal length of about 0.02 m) visible light digital camera 23 with a high-performance polarizing filter for capturing moving images in a bright environment, and dark An ultra-wide-angle infrared digital camera 24 for capturing moving images in an environment, a high-resolution radiometer 25 for measuring the temperature of the sea surface, and a sea for detecting a specific spectrum of the sea surface and measuring the phytoplankton concentration A color sensor 26 and a GPS device 27 for acquiring the position of the drone 20 are provided. The drone side control device 21 includes a digital computer 21a. The drone-side radio transmission / reception device 22 includes a radio unit 22a and an antenna 22b.

  The high-resolution radiometer 25 measures the infrared component of the reflected light from the sea surface, which is determined according to the sea surface temperature, and can estimate the sea surface temperature from the measured value. The sea color sensor 26 detects the concentration of chlorophyll a, which is a photosynthetic pigment contained in the phytoplankton, by analyzing the spectrum of the reflected light from the sea surface. From the measured value, the phytoplankton on the sea surface is detected. The concentration can be estimated.

  FIG. 3 schematically shows the flow of control operation on the fishing boat side in this embodiment, and FIGS. 4a to 4c, FIG. 5 and FIG. 6 schematically show the flow of control operation on the drone side in this embodiment. Yes. Hereinafter, the control operation of the fishing boat 10 and the drone 20 in the present embodiment will be described in detail with reference to these drawings.

Initially, the operation | movement of the fishing boat side control apparatus 11 mounted in the fishing boat 10 is demonstrated using FIG. When the system is activated, first, initial setting of the operation of the drone 20 is performed (step S1). The contents of the initial setting in this case include setting of the route based on the line transect method by the drone 20 and setting of the navigation altitude of the drone 20. As is well known, the line transect method is a method of drawing a line in a survey target area and investigating organisms on the line or within a certain range from the line. Here, as shown in FIG. The position information of the transect route 70, the transect start point 71, and the position information of the transect end point 72 are respectively initialized. By setting the navigation altitude of the drone 20, the line width of the transect line is determined. That is, the line width a (mm) is determined by a = 2f / cot (α / 4). However, f is a navigation altitude (mm), α is an angle of view (°), and this angle of view α is α = 2 tan ⁻¹ (24√13 when the size of the camera sensor is 24√13 (mm). / F).

  It should be noted that the survey target area, the position information of the transect route 70, the transect start point 71, and the position information of the transect end point 72 may be determined by the operator of the system based on past results and experience, It may be determined from a suitable water temperature range based on the sea surface temperature distribution received from the artificial satellite NOAA, or both may be used in combination.

  Next, the initial setting content is transmitted to the drone 20 (step S2), and a take-off instruction is transmitted to the drone 20 by the operation of the operator (step S3).

  The fishing boat 10 and the drone 20 regularly acquire position information by the GPS devices 13 and 27, respectively. The acquired position information of the drone 20 is received, and the distance between the fishing boat 10 and the drone 20 is calculated from this and the acquired position information of the fishing boat 10 (step S4).

  Next, it is determined whether or not the calculated distance exceeds a predetermined value, for example, 2.5 km which is a communication limit between the fishing boat 10 and the drone 20 (step S5). If it is determined that it has not exceeded (NO), it is determined whether or not a compulsory homing instruction is issued to the drone 20 (step S6). When it is determined that the homing instruction has not been issued (in the case of NO), the process returns to step S4 and the processes of steps S4 to S6 are repeated. If it is determined that a homing instruction has been issued (YES), a homing instruction signal is transmitted to the drone 20 (step S7). Thereby, the drone 20 starts homing to the fishing boat 10 (step S25 of FIG. 4b).

  If it is determined in step S5 that the distance exceeds the predetermined value (in the case of YES), the process proceeds to step S7, and a homing instruction signal is transmitted to the drone 20. Thereby, the drone 20 starts homing to the fishing boat 10 (step S25 of FIG. 4b).

  Next, operation | movement of the drone side control apparatus 21 mounted in the drone 20 is demonstrated using FIG. 4a-4c, FIG.5, and FIG.6. When the system is activated, the initial settings transmitted from the fishing boat 10 are received (step S11 in FIG. 4a) and stored in the memory in the digital computer 21a.

  Next, it is determined whether or not there has been a take-off instruction from the fishing boat 10 side (step S12). Only when it is determined that there is a take-off instruction (in the case of YES), a take-off operation is performed (step S13). The drone 20 flies toward the transect starting point 71 after takeoff.

  Next, it is determined whether or not a compulsory homing instruction is issued (step S14). When it is determined that no homing instruction has been issued (in the case of NO), a search process for an area where the fish school is encountered with high probability is performed (step S15). Therefore, this fish school high probability encounter area search process is started as soon as the drone 20 starts to fly, and is continuously executed during the flight.

  If it is determined in step S14 that a homing instruction has been issued (in the case of YES), homing of the drone 20 is started (step S25 in FIG. 4b), landed on the fishing boat 10 (step S26), and this fishery Processing of the support system ends.

  The fish high probability encounter area search process in step S15 is shown in FIG.

  First, the sea surface temperature is measured by the high-resolution radiometer 25 mounted on the drone 20 (step S100), and the chromaticity of the sea surface is detected by the sea color sensor 26 also mounted on the drone 20, and the phytoplankton is detected. The concentration is measured (step S106). Specifically, the entire area having an aspect ratio (for example, 16: 9, which is just an example) according to the angle of view of the camera is divided into a plurality of grids arranged in a matrix (for example, 80 grids). Then, the sea surface temperature estimated from the measurement value of the high resolution radiometer 25 in each grid is formed into a histogram, and the mode value is calculated as the water temperature of the grid and stored in the memory. FIG. 8A shows sea surface temperature measurement data in a region divided into a plurality of grids with gradations corresponding to numerical values, and FIG. 8B shows grid water temperatures with gradations corresponding to numerical values. .

  The phytoplankton concentration measured by the sea color sensor 26 is similarly histogrammed, and the mode value is calculated as the phytoplankton concentration of the grid and stored in the memory. FIG. 9A shows phytoplankton concentration measurement data in a region divided into a plurality of grids with gradations corresponding to numerical values, and FIG. 9B shows grid concentration with gradations corresponding to numerical values. Yes.

  Next, it is determined whether or not the grid water temperature thus calculated is within a predetermined temperature range (step S101). Specifically, it is determined whether or not the grid water temperature is within a range of 14.6 to 15.4 ° C., for example.

  When it is determined that the grid water temperature is within the predetermined temperature range (in the case of YES), it is determined that the grid is in a suitable water temperature range (step S102). When it is determined that the grid water temperature is not within the predetermined temperature range (in the case of NO), the process returns to steps S100 and S106, and the above-described processing is repeated. FIG. 8C shows a grid in a suitable water temperature region where the grid water temperature is within a predetermined temperature range.

  Subsequent to the process in step S102, a water temperature difference value between adjacent grids is calculated (step S103). Next, it is determined whether or not the calculated water temperature difference value is within a predetermined temperature range (step S104). Specifically, it is determined whether or not the water temperature difference value is within a range of 0.8 to 2.0 ° C., for example.

  When it is determined that the water temperature difference value is within the predetermined temperature range (in the case of YES), it is determined that the periphery of the grid is a suitable water temperature gradient region (step S105), and the process proceeds to step S109. When it is determined that the water temperature difference value is not within the predetermined temperature range (in the case of NO), the process returns to steps S100 and S106, and the above-described processing is repeated. FIG. 8C also shows a grid of a suitable water temperature gradient region in which the water temperature difference value is within a predetermined temperature range.

On the other hand, as described above, it is determined whether or not the grid concentration that is the phytoplankton concentration of the grid calculated in step S106 is within a predetermined range (step S107). Specifically, it is determined whether or not the grid concentration is within a range of 0.23 to 3.96 mg · m ³ , for example.

  When it is determined that the grid density is within the predetermined range (in the case of YES), it is determined that the grid is in the preferred density range (step S108). When it is determined that the grid density is not within the predetermined range (in the case of NO), the process returns to steps S100 and S106, and the above-described processing is repeated. FIG. 9C shows a grid in a preferable density region where the grid density is within a predetermined range.

  Following the processing in step S105 or step S108, it is determined whether or not a search for a suitable water temperature region, a suitable water temperature gradient region, and a suitable concentration region has been performed for all grids (step S109). When it is determined that the search is not performed for all the grids (in the case of NO), the process returns to steps S100 and S106, and the above-described processing is repeated.

  When it is determined that the search has been performed for all the grids (in the case of YES), a grid with a suitable grid water temperature, a suitable inter-grid water temperature gradient, and a suitable grid concentration is searched (step S110). Specifically, the preferred water temperature region grid shown in FIG. 10A, the preferred water temperature gradient region grid shown in FIG. 10B, and the preferred concentration region grid shown in FIG. The grids are overlapped as shown in FIG. 10 (D) and displayed at a gradation corresponding to the height of the overlapping rate as shown in FIG. 10 (E) to search for a grid with a high overlapping rate.

  Next, it is determined whether or not there is a grid with a high overlap rate (step S111). Specifically, it is determined whether or not there is a grid in which all of the grids with the preferred grid water temperature, the preferred inter-grid water temperature gradient, and the preferred grid density overlap.

  When it is determined that there is a grid with a high overlap rate (in the case of YES), it is determined that a fish school high probability encounter area has been found (step S112). On the other hand, when it is determined that there is no grid with a high overlap rate (in the case of NO), the process returns to steps S100 and S106, and the above-described processing is repeated.

  Next, the process proceeds to step S16 in FIG. 4a. In step S16, it is determined whether or not a fish school high probability encounter area has been found. If it is determined that the area has been found (YES), the process proceeds to step S27 in FIG. Do. This search process will be described later.

  When it is determined that there is no area discovery (in the case of NO), it is determined whether or not the current position of the drone 20 is the transect start point 71 (FIG. 7) (step S17). When it is determined that it is not the transect start point (in the case of NO), the process returns to step S15 and the above-described processing is repeated. When it is determined that it is a transect start point (in the case of YES), flight along the transect route 70 (FIG. 7) is started (step S18).

  Next, the above-described fish high probability encounter area search process similar to step S15 is performed (step S19 in FIG. 4b). Thereafter, it is determined whether or not a fish school high probability encounter area has been found (step S20). If it is determined that the area has been found (in the case of YES), the process proceeds to step S27 in FIG. This search process will be described later.

  When it is determined that there is no area discovery (in the case of NO), it is determined whether or not a forced homing instruction is issued (step S21). When it is determined that a homing instruction has been issued (in the case of YES), homing of the drone 20 is started (step S25 in FIG. 4b), landing on the fishing boat 10 (step S26), and processing of this fishery support system Ends. If it is determined in step S21 that no homing instruction has been issued (in the case of NO), it is determined whether or not the charge amount of the battery mounted on the drone 20 has dropped below a threshold value (step S22). ).

  If it is determined in step S22 that the battery charge is low (in the case of YES), a warning “battery charge low” is displayed on the display unit 11b of the fishing boat side control device 11, and the return of the drone 20 is started. Then (step S25), landing on the fishing boat 10 (step S26), the processing of this fishery support system ends. Actually, the battery charge amount at that time is detected, and when the battery charge becomes 20% or less of the full charge, the return is made. Moreover, you may comprise so that the fishing boat 10 may be notified when the battery charge amount becomes 50% or less at the time of a full charge.

  If it is determined in step S22 that there is no decrease in the battery charge amount (in the case of NO), it is determined whether or not the current position of the drone 20 is the transect end point 72 (FIG. 7) (step S23). If it is determined that it is not the transect end point (in the case of NO), the process returns to step S19 and the above-described processing is repeated. When it is determined that it is a transect end point (in the case of YES), the transect flight is ended (step S24), the return of the drone 20 is started (step S25), and landing on the fishing boat 10 (step S26). The processing of this fishery support system ends.

  If it is determined in step S16 of FIG. 4a or step S20 of FIG. 4b that a fish school high probability encounter area has been found (in the case of YES), the process proceeds to the process of FIG. 4c. First, continuous shooting (moving image shooting) of the sea surface is started in a state where the drone 20 is stationary (hovering) in the air in the discovered fish high probability encounter area (step S27). At this time, it is determined whether or not the environment is bright (step S28). If it is determined to be in the bright state (in the case of YES), the visible light digital camera 23 with a high-performance polarizing filter is used (step S29), and it is determined that it is not in the bright state (dark state) (NO) In this case, the ultra-wide-angle infrared digital camera 24 is used (step S30). Thereby, the fall of the fish school discrimination ability by a photography environment state can be prevented.

  Next, a process for discriminating fish school and fish species is performed from the moving image data obtained by photographing (step S31).

  The fish school and fish type discrimination process in step S31 is shown in FIG.

  First, whether or not light of a wavelength of 530 nm, which is considered to be reflected from the body surface, such as saury, mackerel or anchovy, is included in one pixel (pixel) of one frame of the captured moving image data Is determined (step S200). If it is determined that it is not included (in the case of NO), the process proceeds to step S204 described later. When it is determined that light having a wavelength of 530 nm is included (in the case of YES), it is determined whether or not there is a change in light chromaticity or brightness between pixels corresponding to each other in adjacent frames (step S201). . That is, binarization processing using the background difference method is performed to recognize whether the background is present. More specifically, the chromaticity (RGB ratio) between the n-th frame and the (n + 1) -th frame taken continuously is compared for each corresponding pixel to determine whether there is a change.

  If it is determined in step S201 that there has been a change (in the case of YES), the value of the pixel is set to “1” indicating that the object is not a background but a moving object (step S202), and it is determined that there has been no change. In the case of NO (in the case of NO), the value of the pixel is set to “0” representing the background (step S203).

  Next, it is determined whether or not the above comparison has been performed for all the pixels of the frame (step S204). If it is determined that all the pixels have not been compared (NO), the process returns to step S200 and the subsequent processing is repeated.

  If it is determined in step S204 that all pixels have been compared (in the case of YES), it is determined whether or not the total number of “1” pixels that are moving objects and the like is not 3.2 million or more. (Step S205). This step S205 is provided for noise removal.

  If it is determined that the total number of “1” pixels is not equal to or greater than 3.2 million (in the case of NO), it is estimated that there is no moving object discovery, so it is determined that there is no fish school discovery (step S206). The process of step S31 for discriminating the fish species is terminated, and the process proceeds to step S32 of the next FIG. 4c. On the other hand, when it is determined that the total number of “1” pixels is 3.2 million or more (in the case of YES), it is determined that a cluster of “1” pixels constitutes a moving object, and the cluster is an ellipse approximation. It is determined whether or not it is a cluster (step S207).

  If it is determined that the cluster is not an elliptical approximate cluster (in the case of NO), it is presumed that the moving object is not a fish, so it is determined that no fish school has been found (step S206), and the process proceeds to step S32 in FIG. 4c. On the other hand, when it is determined that the cluster is an elliptical approximate cluster (in the case of YES), it is determined whether or not the major axis of the ellipse of the cluster is 17 cm or more (step S208).

  If it is determined that the distance is not 17 cm or more (in the case of NO), it is estimated that the moving object is not a fish, so it is determined that no fish school has been found (step S206), and the process proceeds to step S32 in FIG. 4c. On the other hand, when it is determined that it is 17 cm or more (in the case of YES), it can be determined that the cluster is a fish, but it is necessary to determine whether or not the clusters around the cluster constitute a fish school.

  Therefore, it is first determined whether or not the distance between adjacent clusters is less than the major axis of the ellipse of the cluster (step S209). When it is determined that the distance between clusters is not less than the major axis of the cluster or more (in the case of NO), it is estimated that the cluster group is not a fish school, and therefore it is determined that no fish school has been found (step S206). Proceed to step S32 of FIG. 4c. On the other hand, when it is determined that the distance between clusters is less than the major axis of the cluster (in the case of YES), it is determined whether or not three or more clusters are adjacent to each other (step S210). When it is determined that three or more clusters are not adjacent to each other (in the case of NO), it is estimated that the cluster group is not a fish school, so it is determined that no fish school has been found (step S206), and step S32 in FIG. Proceed to On the other hand, when it is determined that three or more clusters are adjacent to each other (in the case of YES), it is determined that these cluster groups are fish schools (step S211). That is, a cluster group that is adjacent to each other at a distance less than the ellipse major axis of a cluster that is determined to be a fish body and that has three or more clusters adjacent to each other is determined as a fish group.

  Next, the outer circumference of the cluster group is approximated to an ellipse (step S212), the area is calculated, the fish species are determined, and the fish school position is acquired (step S213), and the processing of step S31 for determining the fish school and fish species is performed. And the process proceeds to step S32 in FIG. 4c.

  The area of the school of fish (cluster group) is calculated from the formula for the approximate area of the ellipse. In other words, if the major axis of the ellipse of the cluster group is a and the minor axis is b, the area S of the fish school can be approximately obtained by S = πab.

For example, if the major axis (fish body length) of the elliptical approximate cluster for the fish is 17 to 35 cm, or the minor axis / major axis ratio of the elliptical approximate cluster for the fish is 0.1 or less, use a saury. Judge that there is. If the cluster number / cluster group area, which is the ratio of the approximate elliptical area of the cluster group representing the fish school and the number of clusters in the cluster group, is 3.5 / 100 (animals / m ² ) or more, It is determined that the school of fish is a saury school.

  In step S32 of FIG. 4c, it is determined whether or not a school of fish has been found. If it is determined that the school of fish has not been found (in the case of NO), the process proceeds to step S36, where it is determined whether or not the frame is out.

  When it is determined in step S32 that a school of fish has been found (in the case of YES), it is determined whether or not a fish type has been determined (step S33). When it is determined that the fish type has also been determined (in the case of YES), the calculated fish school area, the position of the fish school acquired by the GPS device 27, and information on the determined fish type are transmitted to the fishing boat 10 ( Step S34). When it is determined that there is no fish type determination (in the case of NO), information on only the calculated fish school area and the position of the fish school acquired by the GPS device 27 is transmitted to the fishing boat 10 (step S35).

  After that, it is determined whether or not the school of fish has come out of the frame that is continuously photographed from a fixed point (step S36). More specifically, this frame-out is defined as the moment when the center point of the school of fish that is elliptically approximated as a cluster group deviates from the frame that is the angle of view of the camera. When it is determined that the frame is not out (in the case of NO), the process returns to step S28 and the subsequent processing is repeated.

  When it is determined that the frame is out (in the case of YES), shooting at a fixed point is ended, and the movement of the drone 20 is resumed. At that time, it is first determined whether or not the transect flight can be resumed (step S37). If it is determined that the transect flight cannot be resumed (in the case of NO), the return of the drone 20 is started ( Step S25 in FIG. 4b), landing on the fishing boat 10 (step S26), and the processing of this fishery support system ends.

  When it is determined that the transect flight can be resumed (in the case of YES), it is determined whether or not the current position of the drone 20 is outside the transect range (step S38). When it is determined that the position of the drone 20 is outside the range of the transect route 70 (in the case of YES), the process proceeds to step S17 in FIG. 4a and the above-described processing is repeated to determine that it is within the range of the transect route 70. If it is (NO), the process proceeds to step S19 in FIG. 4b and the above-described processing is repeated.

  Next, the operation of the fishery support system of this embodiment will be described. When the operator starts the system, sets the route by the line transect method of the drone 20, sets the initial altitude of the drone 20, and takes off the drone 20, the drone 20 automatically takes off from the fishing boat 10. It automatically proceeds to the designated transect start point, flies through the transect route, detects the fish school and fish species, and reports to the fishing boat 10 the fish school discovery, the fish school size, the fish species and their position. Fish school and fish species detection starts immediately after takeoff. These pieces of information obtained from the drone are displayed on the display unit 11b of the fishing boat control device 11.

  FIG. 11 shows an example of an image displayed on the display unit 11b of the fishing boat side control device 11 in this case. However, here, the moving direction and moving speed of the school of fish detected in other embodiments described later are also displayed. In this figure, 110 is the position of the fishing boat 10, 111 is the position of the drone 20, 112 is the set transect channel 70, 113 is the set transect start point 71, 114 is the set transect end point 72, 115 is the artificial The sea surface temperature distribution display area received from the satellite NOAA, 116 is a discovered fish high probability encounter area, and 117 is an icon representing the discovered fish school. On this screen, the area other than the sea surface temperature distribution display area 115 by NOAA indicates an area where NOAA data could not be acquired or an area outside the survey target area. The fish school icon 117 has a size corresponding to its scale. Various information regarding the fish school, such as the fish school scale, moving direction, moving speed, and discovery time, is displayed in a pop-up as shown at 118 by selecting the fish school on the screen. In addition, drone information such as flight time, distance between the fishing boat 10 and the drone 20, battery charge amount, communication signal strength reduction and occurrence of various troubles, etc. are displayed in an area 119 below the fishing boat 10 icon 110. Is displayed.

  The drone 20 automatically returns to the fishing boat 10 when the search is completed after flying to the end point of the transect route. However, the return is made when a compulsory homing instruction is issued from the fishing boat side, when the distance from the fishing boat 10 is too wide, or when the battery charge is lower than a predetermined value.

  As described above in detail, according to the present embodiment, the seawater surface is captured by the visible light digital camera 23 or the infrared digital camera 24 mounted on the drone 20 to acquire the moving image data, and the acquired moving image Since it is configured to detect a school of fish from the data using the background subtraction method, and transmit the data in which the detected position of the school of fish and the position of the drone are linked to the fishing boat 10, the fishing boat 10 side performs initial setting and takeoff. It is possible to automatically find a remote school of fish only by issuing some instructions such as instructions. In this way, by constructing a fishery support system using the drone 20 following seabirds searching for a school of fish from above, labor saving and efficiency improvement of the fishery can be achieved. Further, in the present embodiment, a suitable water temperature region, a suitable water temperature gradient region, and a suitable concentration region of phytoplankton are discovered using the high resolution radiometer 25 and the sea color sensor 26, and the drone 20 is stationary in the fish high probability encounter area. Since the school of fish is detected by performing fixed point continuous shooting, it becomes possible to find the school of fish efficiently.

  Next, another embodiment of the fishery support system of the present invention will be described. In the present embodiment, the drone further searches for the moving direction and moving speed of the school of fish, and other configurations, operations, and effects of the present embodiment are exactly the same as those in the embodiment of FIGS. It is.

  In the present embodiment, the movement direction and movement speed of the school of fish are calculated from the movement direction and movement speed of the center point of each school of fish in the fixed point continuous shooting, and the calculated movement direction and movement speed are sent to the fishing boat side control device 11. Sending.

More specifically, in step S36 in FIG. 4c of the above-described embodiment, when it is determined that the frame is out, the fixed point continuous shooting is finished and the moving shooting is performed. However, in this embodiment, the fixed point continuous shooting is started. The moving speed of the school of fish is calculated by dividing the straight line distance between the center points of the school of fish by the continuous shooting time between the image and the image out of the frame and the fixed point continuous shooting end. That is, the movement speed V of the school of fish is
V = √ {(X− _{n + i−} X _n ) ² + (Y _{n + i} −Y _n ) ² } / (T _{n + i} −T _n )
Is calculated from Where V is the moving speed of the school of fish (m / sec), (X _n , Y _n ) is the position coordinates of the school of fish at the start of fixed point continuous shooting, and (X _{n + i} , Y _{n + i} ) is the position of the school of fish at the end of fixed point continuous shooting The coordinate, T _n is the time at the start of fixed point continuous shooting, and T _{n + i} is the time at the end of fixed point continuous shooting.

The movement direction of the school of fish is calculated by plotting the center point of the school of fish for each image during the fixed point continuous photographing period on orthogonal plane coordinates, obtaining a regression line by the least square method, and calculating the angle θ of the movement direction. That is, as shown in FIG. 12, the angle θ in the moving direction is
θ = tan ⁻¹ {(Y ₀ −Y _{9b / 16} ) / (X _a −X _b )}
Is calculated from However, as shown in FIG. 12B, θ is the moving direction represented by an angle (slope) with respect to the y axis (lower end of the angle of view) of the regression line, and P (X _a , Y ₀ ) is the regression line and the y axis. The intersection coordinate, Q (X _b , Y _{9b / 16} ) is the intersection coordinate between the regression line and the right end of the angle of view.

  According to the present embodiment, in addition to the operational effects of the above-described embodiment, the fishing boat 10 reports the movement direction and movement speed of the school of fish from the drone 20, so it is easy to determine at which position the discovered school of fish can be encountered. It is also possible to obtain an effect that it is possible to know.

  In the above-described embodiment, the drone 20 is configured to be forced to return when it is determined that the distance between the fishing boat 10 and the drone 20 exceeds a predetermined value. The drone 20 may be configured to perform forced homing when it is determined that the radio signal strength from the mobile phone is less than a predetermined value, for example, less than −50 dBm.

  In the embodiment described above, the fishing boat 10 is a fishing boat that performs saury fishing, but may be a fishing boat that fishes fish other than saury, such as skipjack, mackerel, sardines, and other fish.

  All the embodiments described above are illustrative of the present invention and are not intended to be limiting, and the present invention can be implemented in other various modifications and changes. Therefore, the scope of the present invention is defined only by the claims and their equivalents.

DESCRIPTION OF SYMBOLS 10 Fishing boat 11 Fishing boat side control apparatus 11a, 21a Digital computer 11b Display part 11c Operation part 12 Fishing boat side radio | wireless transmission / reception apparatus 12a, 22a Radio | wireless part 12b, 22b Antenna 13, 27 GPS apparatus 20 Drone 21 Drone side control apparatus 22 Drone side radio transmission / reception Equipment 23 Visible light digital camera 24 Infrared digital camera 25 High resolution radiometer 26 Sea color sensor 70 Transect route 71 Transect start point 72 Transect end point 110 Fishing boat position 111 Drone position 112 Set transect route 113 Set transect start Point 114 Set transect end point 115 Sea surface temperature distribution display area by NOAA 116 Discovered fish high probability encounter area 117 Discovered fish school 118 Information about 119 Drone information in flight

Claims (15)

  A fishing boat side control device mounted on a fishing boat, a fishing boat side wireless transmission / reception device mounted on the fishing boat, an aircraft side control device mounted on an unmanned aircraft, and the fishing boat side wireless transmission / reception device mounted on the unmanned aircraft An aircraft-side wireless transmission / reception device that transmits / receives data to / from the aircraft, an aircraft position determination device that is mounted on the unmanned aircraft and knows the position of the unmanned aircraft and inputs it to the aircraft-side control device, and the unmanned aircraft And an imaging device that inputs image data obtained by photographing the water surface to the aircraft-side control device. The aircraft-side control device detects a school of fish from the image data obtained from the imaging device. The data relating the detected fish school and the position of the unmanned aircraft at that time obtained from the aircraft position grasping device is used as the aircraft-side wireless transmission / reception device and the fishing boat. Fishery support system using unmanned aircraft, characterized in that it is configured to send through the wireless transmitter-receiver to the fishing-side control device.   The said aircraft side control apparatus is comprised so that a fish high probability encounter area may be discovered, and it is comprised so that the detection of a fish may be performed in this discovered fish high probability encounter area. A fishing support system using the unmanned aerial vehicle according to 1.   The said aircraft side control apparatus is comprised so that a school of fish may be detected by the background difference method from the moving image image data obtained from the said imaging device in the state which stopped the said unmanned aircraft. A fishery support system using the unmanned aerial vehicle described.   The aircraft-side control device is configured to detect a school of fish by determining whether or not a cluster of pixels whose chromaticity has changed between one continuously shot frame and the next frame is an elliptical approximation. The fishery support system using the unmanned aerial vehicle according to claim 3, wherein the fishery support system is used.   The aircraft-side control device is configured to detect a school of fish by determining whether the length of the major axis of the elliptical approximate cluster is equal to or greater than a predetermined value and whether or not the plurality of elliptical approximate clusters are adjacent to each other. The fishery support system using the unmanned aerial vehicle according to claim 4.   The aircraft-side control device is configured to elliptically approximate the outer periphery of the group of ellipse approximation clusters and detect the size of the fish school by obtaining an area of the cluster group that is elliptically approximated. A fishery support system using the unmanned aerial vehicle according to 4 or 5.   The aircraft-side control device determines whether the major axis of the elliptical approximate cluster is within a predetermined range or whether the ratio of the minor axis to the major axis (minor axis / major axis) of the elliptical approximate cluster is a predetermined value or less. The fishery support system using the unmanned aerial vehicle according to any one of claims 4 to 6, wherein the fish type is determined.   The said aircraft side control apparatus is comprised so that the movement direction of a fish school may be calculated | required from the inclination of the regression line calculated | required from the center point of the fish school for every image of a continuous imaging | photography period. A fishery support system using the unmanned aerial vehicle according to claim 1.   The aircraft-side control device is configured to obtain a moving speed of the school of fish from a linear distance between the center point of the school of fish at the start of continuous shooting, the center point of the school of fish at the end of continuous shooting, and the continuous shooting period. A fishery support system using an unmanned aerial vehicle according to any one of claims 3 to 8.   The unmanned aircraft includes a temperature sensor that measures sea surface temperature and a sea color sensor that measures phytoplankton concentration, and the aircraft-side control device measures the sea surface temperature measured by the temperature sensor and the sea color sensor. The fishery support system using an unmanned aerial vehicle according to any one of claims 1 to 9, wherein a fish school high probability encounter area is found from the phytoplankton concentration.   The aircraft-side control device is configured to find a fish school high probability encounter area from a sea surface temperature measured by the temperature sensor, a sea surface temperature gradient based on the sea surface temperature, and a phytoplankton concentration measured by the sea color sensor. The fishery support system using the unmanned aerial vehicle according to claim 10.   A fishing boat position grasping device mounted on the fishing boat and grasping the position of the fishing boat and inputting it to the fishing boat side control device is further provided, and the fishing boat side control device is obtained from the fishing boat position grasping device. The distance between the fishing boat and the unmanned aircraft is calculated from the position of the aircraft and the position of the unmanned aircraft obtained from the aircraft position grasping device, and when the calculated distance exceeds a predetermined location, the unmanned aircraft is transferred to the fishing boat. The fishery support system using an unmanned aerial vehicle according to any one of claims 1 to 11, wherein the fishery support system is configured to instruct the homing.   The unmanned aerial vehicle according to any one of claims 1 to 12, wherein the imaging device includes a visible light camera used in a bright state and an infrared camera used in a dark state. Had a fishery support system.   The said aircraft side control apparatus is comprised so that this unmanned aircraft may be returned to the said fishing boat when the charge amount of the battery of the said unmanned aircraft falls, The any one of Claim 1 to 13 characterized by the above-mentioned. Fishery support system using unmanned aerial vehicles described in 1.   The unmanned aerial vehicle according to any one of claims 1 to 14, wherein the aircraft-side control device is configured to fly the unmanned aerial vehicle along a preset transect route. Had a fishery support system.