JPH0531941B2 - - Google Patents

Description

[Detailed Description of the Invention] [Field of Application of the Invention] The present invention relates to an apparatus for monitoring the condition of aquatic organisms, and in particular, a system for determining the presence or absence of toxic substances in raw water of a water treatment plant or inflowing sewage of a sewage treatment plant using aquatic organisms. This invention relates to an aquatic life monitoring device suitable for monitoring.

[Background of the invention]

At water treatment plants, in order to determine whether or not poisonous substances have been mixed into the raw water, a portion of the raw water is channeled into an aquarium where aquatic creatures such as crucian carp, carp, dace, tanager, haya, and oysterfish are raised. . Multiple individuals must be kept as they may die naturally.
When a poisonous substance is mixed into the raw water, the inflow of the poisonous substance into the raw water is monitored using the phenomenon that fish behave abnormally or die. Additionally, sewage treatment plants need to know whether legally prohibited toxic substances have entered the incoming sewage, and rely on intermittent manual water quality analysis.

As described above, monitoring of toxic substances in water currently relies on human visual observation and analysis. As a result, continuous monitoring and early detection were not possible, and countermeasures such as stopping water distribution to customers had to be taken late.

As a method for monitoring fish, a method has been proposed in which fish in an aquarium are detected from the top of the tank using an industrial television camera (ITV) and the images are processed (Reference: 36th National Water Supply Research Conference, Lecture Collection p464-466). ing. According to this method, when a fish floats on the water surface with its belly on its side, it can be recognized as an ``independent bright spot of a certain size or more'', and the high brightness area of the fish near the water surface and the By extracting only the changes in light due to the unevenness of the surface, it is possible to organize the background and determine the behavior of the fish. However, with this method, it is always difficult to recognize the movements of multiple fish individually.

In addition to this, Japanese Patent Application Laid-Open No. 143296/1984 uses a photoelectric conversion device to measure floating objects in water.

[Purpose of the invention]

An object of the present invention is to provide an aquatic organism condition monitoring device that can continuously monitor the condition of a plurality of aquatic organisms.

[Summary of the invention]

An object of the present invention is to provide: an aquarium into which test water is introduced and aquatic organisms are raised; an imaging device that photographs the aquatic organisms in the aquarium from the side and outputs a plurality of pieces of image information taken at different photographing times; means for identifying aquatic organisms and the background by converting each of the image information into binarized or ternarized images; a means for determining barycenter coordinates of aquatic organisms from the obtained binarized or ternary images; Means for determining an average moving speed of an aquatic creature based on a plurality of center-of-gravity coordinates, and means for determining whether the obtained average moving speed is greater than or equal to a first set speed or less than a second set speed. This is achieved by a condition monitoring device for aquatic organisms characterized by the following.

Further, in the specific implementation of the present invention, it is preferable to provide an aquarium into which test water is introduced and aquatic organisms are kept in a plurality of breeding spaces.

[Embodiments of the invention]

FIG. 1 shows an embodiment of the present invention.

In FIG. 1, the water tank 100 includes a water supply pipe 11.
Test water is supplied from the top by a water supply pump 120 and a water supply pump 120. This test water is raw water at a water treatment plant, and inflow sewage at a sewage treatment plant. In addition, when monitoring toxic substances in rivers, river water is used. Aquarium 1
Inside 00, there are 101 stones laid out in the style of rivers. The water introduced into the water tank 100 is drained through a drain pipe, but the illustration is omitted since it would complicate the illustration.

The water tank 100 has a rectangular parallelepiped shape with a bottom and an open top.

Inside the aquarium 100, the central part is divided into two rooms 100A and 100 by a partition plate 180 such as a wire mesh or a perforated plate.
A fish 140A and a fish 140B are kept as aquatic organisms in each room. The partition plate 180 is used to raise the fish 140A and 140B separately, and allows the water in the aquarium 100 to circulate. Rooms 100A and 100B
is simply the number of fish kept. Since a plurality of fish are kept, each room is complicated, but in order to simplify the explanation in this embodiment, an example with two rooms will be explained.

The fish 140A and 140B are of the type that inhabit the supplied test water. For example, crucian carp, carp, dace, tanago, and oysterfish. Lighting devices 150A and 150B are provided at the top of aquarium 100 to illuminate fish 140A and 140B. Since the present invention applies image processing technology, uniform illumination is required. For this reason, the lighting device 150
A light scattering plate 160 made of ground glass or the like is provided between A and 150B and the water tank 100.

Note that it goes without saying that the number of lighting devices may be one or two or more.

Back screens 170A, 170B and 17
0C is installed to recognize fish 140A and 140B with good contrast. For this reason, fish 14
When the backs of 0A and 140B are black, the back screens 170A, 170B and 170C are preferably white. Conversely, fish 140A and 140
It goes without saying that if B's back color is white, a black one is better.

Furthermore, the frame 105 of the aquarium 100 has the same brightness as the back screens 170A, 170B, and 170C, or has a brightness close to this. If the brightness of the background is b and the brightness of the fish is f, then by increasing the brightness difference between b and f, the brightness of the difference image between the background and the fish increases. Therefore, fish 140A and 140
B can be extracted with high accuracy based on the difference from the background. A detailed explanation will be given later.

The imaging device 200 captures the fish 140A in the aquarium 100.
It sequentially captures and converts images of 140B and 140B into electrical signals, and is preferably an industrial television camera (ITV), which outputs electrical signals with different output voltages depending on the brightness (luminance) of the imaged pixels. ITV
is located on the side of the tank and is designed to photograph the side of the fish. By photographing the fish from the side, you can accurately grasp the movement of the fish. The electrical signal output from the imaging device 200 is transmitted to the moving speed detection means 300. On the other hand, the imaging device 200 receives a horizontal synchronization signal and a vertical synchronization signal from the imaging control circuit 210 to control the imaging timing.

The moving speed detecting means 300 detects the fish 1 based on the images of the fish 140A and 140B obtained by the imaging device 200.
Movement speeds Va and Vb of 40A and 140B, respectively
Detect. Specifically, an image processing device is used as the moving speed detection means 300. In calculating the instantaneous moving velocities Va and Vb, the average moving velocities Vam and Vbm are used to reduce noise caused by disturbances. The detailed configuration and operation within the moving speed detection means 300 will be explained later. Movement speed detection means 300
Signals Va and Vb of the moving speeds of the fish 140A and 140B determined in are transmitted to the moving speed determiner 500.

360 is an address processor that reads and writes image memory information, captures images from the imaging device 200, and controls the display of the monitor 370. The monitor 370 is the imaging device 20
0 image, image processing process and results. 380 is a system processor that includes an address processor 360, an image memory (not shown in FIG. 1) and an image processor (not shown in FIG. 1) in the moving speed detection means 300.
This is a processor that manages and controls the 390 is a console display and system processor 38
0 management control information is input and displayed. 395 inputs the necessary information using the keyboard. 4
00 is a floppy disk that stores image information and image processing programs.

The moving speed determiner 500 is the first moving speed setter 5
The first moving speed Vmax and the second moving speed Vmin are input from the moving speed setter 10 and the second moving speed setting device 520, respectively. Setting values Vmax and Vmin in the first travel speed setter 510 and the second travel speed setter 520
can be changed depending on the type of fish, water temperature, and other environmental conditions. This changing operation can be performed manually or automatically. For example, in response to a change in water temperature, first, the water temperature in the aquarium is measured (not shown), and based on this measurement value, the first movement speed Vmax and the second movement speed Vmin are changed. and change.

The first movement speed Vmax is set to a movement speed at which the fish 140A and 140B are abnormally active,
Movement speed greater than Vmax indicates abnormal behavior due to influx of toxic substances. The second movement speed Vmin is set to a movement speed at which the movement of the fish 140A and 140B is considered to be abnormally small, and a value smaller than Vmin indicates that the movement of the fish 140 is extremely small or the fish 140 has died due to the inflow of poisonous substances. Therefore, the second moving speed is set to a value close to zero.

The moving speed determiner 500 uses the moving speeds Va and Vb of each of the fish 140A and 140B determined by the moving speed detecting means 300 and the first moving speed setting device 51.
0 and the second moving speed determiner 502
Compare Vmax and Vmin respectively. That is, the moving speed determiner 500 determines the average moving speed Vam and
When Vbm is larger than the first movement speed Vmax or smaller than the second movement speed Vmin, the fish 140A
Or it is determined that the movement of 140B is abnormal. The determination result is output as an on/off signal. Although not shown, if an abnormal state is indicated by turning on, an alarm is sounded or a message is outputted in voice to prompt the supervisor to investigate the water quality.

FIG. 2 shows a detailed configuration of the moving speed detection means 300. Let's explain its operation. The moving speed detection means includes an image memory 700, an image processor 600, and an arithmetic unit 600. The image memory 700, the image processor 800, and the arithmetic unit 800 include the address processor 360 and the system processor 38.
Controlled by 0.

The moving speed detecting means 500 alternately detects the average moving speeds Vam and Vbm of each fish 140A and 140B from images obtained by the imaging device 200. The explanation will first explain how to calculate the average moving speed Vam of the fish 140A, but the average moving speed of the fish 140B will be explained first.
The calculation method for Vbm is the same as for Fish 140A.

The imaging device 200 internally has imaging elements arranged in a grid vertically and horizontally, and at time t, the luminance G(i, j, t) of the image in the i-th row and the j-th column corresponding to each imaging element is calculated as follows: Output. Imaging device 200
The image obtained is shown in Figure 3.

A window setting circuit 610 sets a window (processing target area) in the captured image or the image memory 700. Windows 315A and 315B are set for images of room 100A and room 100B, but first, window 315A is set for room 100A, and the fish 140 in this window is set for room 100A.
The movement of A is recognized as an image. Below, fish 140A
We will explain how to calculate the moving speed Va of .

710 (710A, 711A, 710B and 7
11B) is a grayscale image memory that stores grayscale images. Initially, brightness information G(i,
j, t). First, image memory 710A
is the room 100 at time t of the imaging device 200
The grayscale image Ga(i, j, t) of A is received and stored. Subsequently, the image memory 711A stores the image of the room 100A after time h, that is, at time t+h.
A grayscale image Ga(i, j, t+h) of is stored. The time h is about 0.1 seconds to 10 seconds.

The image difference circuit 620 has a grayscale image memory 710A.
The image brightness information Ga(i,
j, t) and Ga(i, j, t+h), calculate the difference therebetween, and store it in the grayscale image memory 720.
Specifically, the image difference circuit 620 calculates the difference image Sa(i, j, t) using equation (1) for all pixels.

Sa (i, j, t) = Ga (i, j, t + h) - Ga
(i, j, t) ...(1) A method for extracting a moving object using a difference image will be specifically explained.

As an example, consider a case where the fish 140A is black and the background of the water portion is white. Since high values of brightness represent bright objects and low values represent dark objects, the brightness of the fish f
is low, and the background brightness b is high. FIG. 4 represents a grayscale image of the fish at time t, and FIG. 5 represents a grayscale image of the fish at time t+h. The difference between these grayscale images is performed as follows. The background portion having the same brightness b is subtracted so that bb=0, so the background difference image has a brightness value close to 0. time t+h
The image of the fish 140A at time t is obtained by subtracting the background brightness b at time t from the fish's dark brightness f.
Takes a negative value such that -b<0. On the other hand, in Fig. 4, the image of the fish before it moves at time t is conversely changed from the bright brightness b of the background at time t+h to the time t.
Since the dark brightness f of the fish in is subtracted, b-f>0, which is a positive value.

FIG. 6 shows a difference image between the two. This image is stored in the grayscale image memory 720. In FIG. 6, A represents the water part, B represents the image of the fish before it moves, and C represents the image of the fish after it moves. Although the respective brightnesses are not shown in Fig. 6, A is near 0, B
takes a negative value, and C takes a positive value.

In this way, the image difference circuit 620 obtains a difference image (FIG. 6) between the fish image at time t (FIG. 4) and the fish image at time t+h (FIG. 5), and stores it in the grayscale image memory 720. is stored in

Subsequently, the moving object ternarization circuit 640 digitizes the difference image for all pixels according to the following equation, and stores the result in the grayscale image memory 740.
Let Ls be a threshold value for ternarization.

Sa(i, j, t)<-Ls; Sa(i, j, t)=-
1 ...(2) Sa (i, j, t) >-Ls; Sa (i, j, t) = 1
...(3) −Ls<Sa(i,j,t)<Ls;Sa(i,j,t)
=0...(4) The threshold Ls is set to be less than or equal to the difference b-f between the brightness b of water and the brightness f of fish. If the threshold value Ls is close to 0, it is easy to pick up noise, so it is set to a certain level, for example, 3 brightness levels or more.

In the moving object ternarization circuit 640, equations (2), (3), and (4)
Therefore, the fish 140 at time t is set to a value of "-1", the fish 140 at time t+h is set to a value of "1", and the water part is set to a value of "0". The image stored in the grayscale image memory 740 is shown in FIG. Numbers in the figure represent brightness.

The binarization circuit 660 binarizes the image of the fish 140A stored in the grayscale image memory 740 and stores it in the binary image memories 760 and 761. The binarization circuit 660 performs calculations on the image of the fish before it moves and the image of the fish after it moves. First, the following calculation is performed on the image of the fish before it moves.

Sa(i, j, t)≦−1; Fa(i, j, t)=1
...(5) Sa (i, j, t) >-1; Fa (i, j, t) = 0
...(6) Here, Fa (i, j, t) having a value of "1" represents the binarized image of the fish, and a value of "0" represents the background. This binary image Fa (i, j, t) is stored in the binary image memory 760. FIG. 8 shows this binary image.

The following calculations are performed on the image of the fish after it has moved.

Sa (i, j, t) ≧ 1; Fa (i, j, t) = 1
...(7) Sa (i, j, t) <1; Fa (i, j, t) = 0
(8) The binarized fish binary image Fa (i, j, t) is stored in the binary image memory 761. FIG. 9 shows this binary image.

The reduction circuit 680 and the expansion circuit 690 are for removing noise from the image. First, the reduction circuit 680 deletes the outline of the fish image from the binary memories 760 and 761 pixel by pixel, and stores it in the binary image memories 780 and 781, respectively. Subsequently, the expansion circuit 690 adds one pixel to the outline of the fish image in the binary memories 780 and 781, and stores the added pixels in the binary image memories 790 and 791, respectively. Through these processes, noise of two pixels or less is removed.

It goes without saying that the reduction and expansion can be repeated multiple times depending on the size of the noise. For example, if multiple objects are labeled in the images in the binary image memories 790 and 791 and the number of objects is N, then until this N becomes less than a predetermined value,
Repeat contraction-expansion. With this operation, noise other than fish can be effectively removed.

In this embodiment, there is only one fish 140A within the window 315A, so it will not overlap with another fish. Therefore, images of each fish can be extracted accurately.

The arithmetic device 800 has binary image memories 790, 7
The average moving speeds Vam and Vbm of the fish 140A and 140B are calculated from the information of 91.

The centroid calculation circuit 810 calculates the centroids of the images in the binary image memories 790 and 791, and stores the calculation results in the centroid memories 815 and 816. The center of gravity coordinates are (Gx, Gy), and the binary image memories 790 and 79
Assuming that the image of the fish part 1 is (fx, fy) and the number of pixels is n, the formula for calculating the center of gravity is as follows.

Gx=Σfx/n...(9) Gy=Σfy/n...(10) The results stored in the center of gravity memories 815 and 816 are expressed as G ^- (Gx, Gy, t) and G ⁺ (Gx, Gy, t), respectively. ).

Even if there are multiple objects in the images of the binary image memories 790 and 791, the calculations of equations (9) and (10) are performed for pixels whose value is "1" in the images of the binary image memories 790 and 791. Calculate as is.

The moving speed calculation circuit 820 has a center of gravity memory 815,
816, the moving speed of the fish 140 is calculated using the following formula, and the calculated value Va(t) is stored in the moving speed memory 82.
Store in 5.

Va(t)=|+G(Gx, Gy, t)−G ^- (Gx,
Gy, t) |/h (11) Since only the instantaneous speed is known in one movement speed calculation, the average movement speed for a certain period is calculated. The average number determination circuit 840 specifies the average number of times, and if it is less than or equal to this number, the imaging device 2
00 to the grayscale image memory 710. In order to continuously monitor the moving speed of fish 140A and 140B, when the moving speed of fish 140A has been calculated, next window 3
The moving speed of fish 140B within 15B is calculated. FIG. 10 shows changes over time in the memory contents during the calculation process. In Figure 10, from time t+2h to t+3h,
The image of fish 140B is processed.

The calculation of the moving speed of the fish 140B within the window 315B is performed using the window 315A described above.
The same is true for the fish 140A inside. The outline will be explained below.

For example, the image difference circuit 620 uses the image brightness information Gb (i, j, t) and Gb (i, j, t) of the fish 140B captured in the grayscale image memories 710B and 711B.
+h), and the difference between them is calculated and stored in the grayscale image memory 720. Moving object ternarization circuit 64
0 and the binarization circuit 660, the binary image Fb(i, j, t+2h) before moving and the binary image after moving
Fb (i, j, t+3h) is stored in binary image memories 760 and 761. Subsequently, the contraction-expansion circuit 6
80 and 690 to remove noise, fish 140
A binary image of B is obtained. Furthermore, the center of gravity calculation circuit 81
0 calculates the center of gravity coordinates of the fish 140B, and the movement speed calculation circuit 820 calculates the movement speed Vb (t+2h) of the fish 140.
Calculate. As shown in FIG. 10, the calculations are repeated alternately to continuously monitor the movements of fish 140A and fish 140B.

In this way, at times t, t+2h, t+4h,
Each center of gravity moving speed Va(t) at t+6h...
Vb(t+2h), Vb(t+4h), Vb(t+6h)... are calculated one after another every 2h, and the movement speed memory 8
25. Average number of times determination circuit 840
determines whether or not the predetermined average number m has been reached for each of the fish 140A and 140B, and if it is less than or equal to m, the grayscale image is imported from the imaging device 200 to the grayscale image memory 710 again, and the average number is equal to or higher than m. Then, an averaging circuit 860 calculates the average moving speed.

That is, the averaging circuit 860 calculates the center of gravity moving speeds Va(t) and Vb stored in the moving speed memory 825.
Average moving speeds Vam and Vbm are calculated from the values of (t+2h), Vb (t+4h), Vb (t+6h), . . . using the following equations.

Vam={ΣVa(t+ph)}/m...(12) Vbm={ΣVb(t+qh)}/m...(13) The average number of times m is about 3 to 1000 times. The average moving speed Vam obtained by the averaging circuit 860 and
Vbm is output from the moving speed detection means 300.

In this way, the moving speed V of the fish 140 determined by the moving speed detecting means 300 is transmitted to the moving speed determiner 500 shown in FIG. In FIG. 1, the moving speed determiner 400 uses the moving speed detecting means 3
00, the average moving speed V of the fish 140, and the first
Travel speed setter 510 and second travel speed setter 52
The first moving speed Vmax and the second moving speed Vmin transmitted from 0 are compared respectively. That is, the movement speed determiner 500 determines that the movement of the fish 140 is abnormal when the movement speed V of the fish 140 is greater than the first movement speed Vmax or smaller than the second movement speed Vmin.

Here, it goes without saying that the settings for Vmax and Vmin can be changed depending on environmental conditions such as the type of fish and water temperature.

In the embodiment shown in FIG. 1, fish are kept in separate rooms and the movement of each fish is monitored, so overlapping of fish can be prevented and abnormalities in fish behavior can be detected with high accuracy.

FIG. 11 shows another example of the water tank.

In FIG. 11, the water tank 100 is separated by a partition plate 180.
Partitioned by A, 180B, 180C and 180D,
5 fish 140A, 140B, 140C, 140
D and 140E in 5 rooms 100A, 100
B, 100C, 100D and 100E. In this case, the grayscale image memory 710 that stores grayscale images of fish is for 10 screens. FIG. 12 shows the calculation cycle. The grayscale image memory is 710
A, 711A, 710B, 711B, 710C,
711C, 710D, 711D, 710E, 71
It is 1E. The storage order of gray scale images is different from the embodiment shown in FIG. In the embodiment shown in FIG. 1, focusing on the time difference image of the fish 140A, the gray scale image is stored at 710.
A, then 711A. Therefore, if the time interval for capturing images of the fish 140A is 1.0 seconds, it will take 10 seconds for five fish.

In the aquarium shown in FIG. 11, gradation images of fish movement values at predetermined intervals are shown at 710A, 710B, and 710, respectively.
C, 710D, and 710E, and then the grayscale images after the fish move are stored in 711A and 711, respectively.
B, 711C, 711D, and 711E.
If the image storage time interval is set to 0.2 seconds, it will take a total of 2.0 seconds to store the grayscale images of the five fish before and after they move, each 1.0 seconds. At this time, the time interval for capturing images of one fish is kept at 1.0 seconds, and the total processing time is 2 seconds. The aquarium shown in Fig. 11 requires 2/5 of the time required for the aquarium shown in Fig. 1, and has the advantage of being able to be monitored continuously.

FIG. 13 shows another example of the moving speed detection means.

In addition to determining the moving speed of the fish, FIG. 13 shows an arrangement in which abnormalities in the fish are determined from the detection of the fish's position coordinates. That is, the center of gravity memories 815, 81
The center of gravity coordinates of No. 6 are input to the position determination circuit 870, and an alarm is issued even when the position of the fish is close to the water surface. In the determination, both cases of movement speed abnormality and position abnormality are considered abnormal. This monitors the movement of fish when dissolved oxygen is depleted or toxic substances enter the system, making it possible to more safely determine abnormalities in raw water.

In Figure 13, the position of the fish is also monitored, so
This has the effect of more effectively detecting abnormalities in fish.

In the above embodiments, each fish is kept in a separate room, and the movements of these fish are sequentially monitored. Therefore, there is an effect that the movements of a plurality of fish can be recognized independently. That is, by monitoring each individual fish, abnormalities in the test water can be detected with high reliability.

In addition, since the movement of fish is monitored based on the difference between grayscale images, it is also effective when the water becomes cloudy.

〔Effect of the invention〕

As described above, according to the present invention, it is possible to continuously monitor the movements of aquatic organisms and their abnormalities, so it is possible to quickly and automatically determine whether or not a poisonous substance has entered the test water. Therefore, when applied to the monitoring of toxic substances in inflow water at a water purification plant or a sewage treatment plant, the reliability of toxic substance monitoring can be improved.

[Brief explanation of the drawing]

FIG. 1 is a configuration diagram showing one embodiment of the present invention, and FIG.
The figure is a detailed configuration diagram showing an example of the moving speed detection means in Figure 1, Figures 3 to 10 are explanatory diagrams of the present invention,
Figure 11 is another configuration diagram of the aquarium, Figure 12 is the 11th
FIG. 13 is a detailed configuration diagram showing another example of the moving speed detection means. 100...Aquarium, 140...Fish, 150A,B
...Lighting device, 170...Back screen, 2
00...Imaging device, 300...Moving speed detection means, 500...Moving speed determiner, 510...First
Movement speed determiner, 520...Second movement speed determiner.

Claims (1)

[Scope of Claims] 1. An aquarium into which test water is introduced and aquatic organisms are raised; an imaging device that photographs the aquatic organisms in the aquarium from the side and outputs a plurality of image information taken at different times; A means for identifying aquatic organisms and a background by converting each of the plurality of image information into binarized or ternarized images; a means for determining the coordinates of the center of gravity of the aquatic organisms from the obtained binarized or ternarized images; means for determining an average moving speed of an aquatic creature based on a plurality of coordinates of the center of gravity; and means for determining whether the obtained average moving speed is greater than or equal to a first set speed or less than a second set speed; An aquatic organism condition monitoring device characterized by comprising: