Patent Description:
<CIT> discloses a method of distinguishing fish species using an echo signal of an ultrasonic wave transmitted underwater. Further technological background can be found in <NPL>.

Since the management of a fish catch has been increasingly strict in the field of fishing every year, a further improvement in accuracy of the technology of estimating the fish species before catching is demanded.

The purpose of the present disclosure is to provide a fish species estimating system, a method of estimating fish species, and a program, which can improve accuracy of a fish species estimation. According to the claimed subject-matter, a fish species estimating system according to claim <NUM>, a method of estimating a fish species according to claim <NUM> and a fish species estimating program according to claim <NUM> are presented.

According to one aspect of the present disclosure, a fish species estimating system includes an acquiring module and a reasoning module. The acquiring module acquires a data set at least including echo data generated from a reflected wave of an ultrasonic wave emitted underwater. The reasoning module estimates a fish species of a fish image included in the echo data of the data set acquired by the acquiring module, by using a learned model created by machine learning where the data set is used as input data and the fish species is used as teacher data.

According to another aspect of the present disclosure, a method of estimating a fish species includes acquiring a data set at least including echo data generated from a reflected wave of an ultrasonic wave emitted underwater, and estimating a fish species of a fish image included in the echo data of the acquired data set, by using a learned model created by machine learning where the data set is used as input data and the fish species is used as teacher data.

According to still another aspect of the present disclosure, a program causes a computer to acquire a data set at least including echo data generated from a reflected wave of an ultrasonic wave emitted underwater, and estimate a fish species of a fish image included in the echo data of the acquired data set, by using a learned model created by machine learning where the data set is used as input data and the fish species is used as teacher data.

According to the present disclosure, it is possible to improve accuracy of a fish species estimation.

The present disclosure is illustrated by way of example and not by way of limitation in the figures of the accompanying drawings, in which like reference numerals indicate like elements and in which:.

Hereinafter, one embodiment of the present disclosure will be described with reference to the accompanying drawings. Note that the following embodiment illustrates a method and device for implementing the technical idea of the present disclosure, and the technical idea of the present disclosure is not intended to be limited to the following method and device. The technical idea of the present disclosure may be variously changed or modified within the technical scope of the present disclosure which is defined in the appended claims.

<FIG> is a block diagram illustrating one example of a configuration of a fish species estimating system <NUM> according to one embodiment. The fish species estimating system <NUM> may include a fish species estimation device <NUM>, a camera <NUM>, a GPS plotter <NUM>, a fish finder <NUM>, and a database <NUM>.

The fish species estimation device <NUM>, the camera <NUM>, the GPS plotter <NUM>, the fish finder <NUM>, and the database <NUM> included in the fish species estimating system <NUM> are mounted, for example, on a ship, such as a fishing boat. Without limiting to this configuration a part or all of functions of the fish species estimation device <NUM> may be realized by a server device installed on the land, for example.

The fish species estimation device <NUM> may be a computer provided with a processing circuitry <NUM> including a CPU, a RAM, a ROM, a nonvolatile memory, and an input/output interface. The processing circuitry <NUM> includes an image-fish species distinguishing module <NUM>, a fish species learning/reasoning module <NUM>, and a data acquiring module <NUM>. These functional modules may be realized by the CPU of the processing circuitry <NUM> executing information processing according to a program loaded to the RAM from the ROM or the nonvolatile memory. The program may be provided, for example, through an information storage medium, such as an optical disc or a memory card, or may be provided, for example, through a communication network, such as the Internet.

The fish species estimation device <NUM> may be accessible to the database <NUM>. The database <NUM> may be realized in the fish species estimation device <NUM>, or may be realized in the GPS plotter <NUM> or the fish finder <NUM>, or may be realized in the server device installed on the land, etc..

The camera <NUM> may image fish when or after it is caught to generate image data. For example, the camera <NUM> may image fish caught in a net when the net is raised, or may image fish raised on the deck, or may image fish put in a fish tank, or may image fish which is landed. The image data generated by the camera <NUM> is outputted to the fish species estimation device <NUM>, and it is used for a learning phase of the fish species learning/reasoning module <NUM> via the image-fish species distinguishing module <NUM>, as will be described later.

The GPS plotter <NUM> may generate position data indicative of the current position of the ship based on electric waves received from the GPS (Global Positioning System) and plots the current position of the ship on a nautical chart image displayed on a display unit (not illustrated). The position data generated by the GPS plotter <NUM> may be outputted to the fish species estimation device <NUM>, and it may be used for the learning phase and a reasoning phase of the fish species learning/reasoning module <NUM>, as will be described later.

The fish finder <NUM> may generate echo data from a reflected wave of an ultrasonic wave emitted underwater, and display an echo image based on the echo data on a display unit <NUM> (refer to <FIG>). The concrete configuration of the fish finder <NUM> will be described later. As illustrated in the example of <FIG>, the echo image may include a fish image F indicative of a component reflected on underwater fish, and a waterbed or seabed image G indicative of a component reflected on the waterbed or seabed.

Further, the fish finder <NUM> may detect water depth data based on the echo data and detects water temperature data by a temperature sensor. The echo data, the water depth data, and the water temperature data which are generated by the fish finder <NUM> may be outputted to the fish species estimation device <NUM>, and they may be used for the learning phase and the reasoning phase of the fish species learning/reasoning module <NUM>, as will be described later.

The image data from the camera <NUM>, the position data from the GPS plotter <NUM>, the echo data, the water depth data, the water temperature data from the fish finder <NUM>, etc. are acquired by the data acquiring module <NUM>, and they may be stored in the database <NUM>.

Among these, a data set including the echo data, the position data, the water depth data, and the water temperature data may be used as input data of the fish species learning/reasoning module <NUM>. The data set may be stored at every given time. The data set may at least include the echo data, and one or more of the position data, the water depth data and the water temperature data may be omitted, or the data set may further include other data, such as time and/or day data, and tidal current data.

The image data is used as input data of the image-fish species distinguishing module <NUM>. The image data may be stored so as to be associated with the data set acquired before catching fish. Specifically, the image data may be associated with the data set so that fish imaged by the camera <NUM> matches with fish detected by the echo data. For example, they may be associated with each other automatically in consideration of the time required for catching the fish, or may be associated with each other, or manually by a user.

For example, the position when the fish finder <NUM> detects a school of fish may be stored, and the image data and the data set may be associated with each other, when satisfying a condition that a distance between the position when the school of fish (signs of fish) is detected and the position when the school of fish is caught (i.e., the position where the image data is generated) is below a threshold. A condition that a difference between the time at which the school of fish is detected and the time at which the school of fish is caught is below a threshold may be further combined with the condition described above. The position at which the school of fish is caught is, for example, the position of the ship when the fish is caught if it is fishing with a fishing pole, and the starting position of hauling a net or the center position of the net, if it is fishing with a round haul net.

The image-fish species distinguishing module <NUM> is one example of a distinguishing module. When the image data is inputted, the image-fish species distinguishing module <NUM> may distinguish a fish species of a fish image included in the image data, and output it. The fish species outputted from the image-fish species distinguishing module <NUM> is used as a teacher data in the learning phase of the fish species learning/reasoning module <NUM>, as will be described later. The fish species may be distinguished beforehand and stored in the database <NUM> so as to be associated with the data set as well as the image data, or may be distinguished during the learning phase of the fish species learning/reasoning module <NUM> and directly inputted into the fish species learning/reasoning module <NUM>.

In this embodiment, the image-fish species distinguishing module <NUM> may be a learning/reasoning module which realizes a learning phase and a reasoning phase by machine learning, similar to the fish species learning/reasoning module <NUM>. Specifically, the image-fish species distinguishing module <NUM> may create a learned model by the machine learning in the learning phase, using the image data as input data and the fish species as the teacher data. The fish species as the teacher data is inputted, for example, by the user. Moreover, the image-fish species distinguishing module <NUM> uses the learned model created in the learning phase, estimate in a reasoning phase the fish species of the fish image included in the image data by using the image data as the input data, and output the fish species.

Without limiting to the configuration described above, the image-fish species distinguishing module <NUM> may omit the part which realizes the learning phase and may only be comprised of the part which realizes the reasoning phase.

The fish species learning/reasoning module <NUM> is one example of the learning module and the reasoning module, and may realize the learning phase and the reasoning phase by the machine learning. Specifically, the fish species learning/reasoning module <NUM> creates in the learning phase the learned model by the machine learning by using the data set including the echo data etc. as the input data, and the fish species distinguished by the image-fish species distinguishing module <NUM> as the teacher data. Moreover, the fish species learning/reasoning module <NUM> uses the learned model created in the learning phase, estimates in the reasoning phase the fish species of the fish image included in the echo data by using the data set including the echo data etc. as the input data, and outputs the fish species.

The machine learning may use, for example, a neural network. Particularly, deep learning using a deep neural network where multiple layers of neurons are combined may be suitable. Without limiting to the configuration described above, machine learning other than the neural network, such as a support vector machine, a decision tree, etc. may be used.

Note that, since the part which realizes the learning phase in the image-fish species distinguishing module <NUM> and the fish species learning/reasoning module <NUM> requires a high calculation throughput, it may be realized by a server device installed on the land, etc. In such a case, data may be communicated sequentially using satellite communication etc., or data may be stored while the ship is traveling and the data may be communicated all at once when the ship returns to a port.

<FIG> is a block diagram illustrating the learning phase of the fish species learning/reasoning module <NUM>. <FIG> is a flowchart illustrating one example of a procedure of the learning phase of the fish species learning/reasoning module <NUM>.

First, the fish species learning/reasoning module <NUM> acquires the data set and the fish species (S11). In the learning phase, the data set and the fish species associated with the data set may be read from the database <NUM>. The data set includes the position data from the GPS plotter <NUM>, and the echo data, the water depth data, and the water temperature data from the fish finder <NUM>, etc. The fish species is distinguished by the image-fish species distinguishing module <NUM> based on the image data from the camera <NUM>. The fish species may be read from the database <NUM> or may be directly inputted from the image-fish species distinguishing module <NUM>.

Note that, although arrows indicate a flow of the data in <FIG> for convenience of explanation, each data may be, in fact, stored in the database <NUM> and read from the database <NUM>. Without limiting to this configuration, each data may be directly inputted and outputted, without the intervening database <NUM>.

Next, the fish species learning/reasoning module <NUM> extracts a part of a plurality of groups of data sets and fish species, as training data for the machine learning (S12), and execute the machine learning using the extracted training data (S13). The machine learning is performed using the data set as the input data and the fish species as the teacher data. Thus, the learned model for estimating the fish species of the fish image included in the echo data is created.

Next, the fish species learning/reasoning module <NUM> may extract a part different from the training data from the plurality of groups of the data set and the fish species, as test data (S14), and evaluate the learned model using the extracted test data (S15). Then, the fish species learning/reasoning module <NUM> may store in the database <NUM> the learned model(s) for which the evaluation is satisfactory (S16), and then end the learning phase.

<FIG> is a block diagram illustrating the reasoning phase of the fish species learning/reasoning module <NUM>. <FIG> is a flowchart illustrating one example of a procedure of the reasoning phase of the fish species learning/reasoning module <NUM>.

First, the fish species learning/reasoning module <NUM> acquires a data set (S21, processing as an acquiring module). In the reasoning phase, the echo data, the position data, the water depth data, the water temperature data, etc. which are included in the data set may be directly inputted into the fish species learning/reasoning module <NUM> from the GPS plotter <NUM> and the fish finder <NUM>. Without limiting to this configuration, the data set may once be stored in the database <NUM>, and may be read from the database <NUM>.

Next, the fish species learning/reasoning module <NUM> uses the data set including the acquired echo data as the input data, compare the input data with the learned model created in the learning phase (S22), and estimate and output the fish species of the fish image included in the echo data (S23). The comparison with the learned model may be, for example, pattern matching of the echo image based on the echo data with the learned model image. The fish species estimated by the fish species learning/reasoning module <NUM> may be outputted to the database <NUM>, the GPS plotter <NUM>, and the fish finder <NUM>, etc..

As illustrated in <FIG>, the fish species outputted from the fish species learning/reasoning module <NUM> is, for example, stored in a detected fish species database included in the database <NUM> so as to be associated with the time and/or day data, the position data, etc. Further, the fish species may be, for example, associated with the data, such as the water depth data, the water temperature data, the current data, etc..

According to the above embodiment, it is possible to improve the accuracy of the fish species estimation by using the learned model created by the machine learning and estimating the fish species of the fish image included in the echo data. Moreover, it is possible to further improve the accuracy of the fish species estimation by repeating the machine learning using the data set and the fish species which are acquired each time fish is caught.

Moreover, according to the above embodiment, since the fish species distinguished by the image-fish species distinguishing module <NUM> based on the image data is used as the teacher data in the learning phase of the fish species learning/reasoning module <NUM>, it becomes possible to omit the user's burden for inputting the fish species, thereby improving the accuracy of the fish species estimation, while reducing the user's burden.

Note that, although in the above embodiment only the fish species is treated as the property to be distinguished and estimated, the fish species and fish quantity may be used as the properties to be distinguished by the image-fish species distinguishing module <NUM>, as the teacher data in the learning phase of the fish species learning/reasoning module <NUM>, and as the property to be estimated in the reasoning phase of the fish species learning/reasoning module <NUM>. According to this configuration, since the fish quantity is estimated as well as the fish species, these are further useful for determination of the fish catch.

<FIG> is a block diagram illustrating one example of a configuration of the fish finder <NUM>. The fish finder <NUM> may include a transducer <NUM>, a transmission-and-reception switch <NUM>, a transmitting circuit <NUM>, a receiving circuit <NUM>, an A/D converter <NUM>, a processing circuitry <NUM>, a display unit <NUM>, a user interface <NUM>, and a notifier <NUM> (notifying module). The processing circuitry <NUM> may include a display controlling module <NUM> and a matching determining module <NUM>.

The transducer <NUM> may include an ultrasonic transducer and may be installed in the bottom of the ship. The transducer <NUM> may convert an electrical signal from the transmitting circuit <NUM> into an ultrasonic wave and transmit the ultrasonic wave underwater, and convert a received reflected wave into an electrical signal and output the electrical signal to the receiving circuit <NUM>. The transmission-and-reception switch <NUM> may connect the transmitting circuit <NUM> to the transducer <NUM> upon the transmission, and connect the receiving circuit <NUM> to the transducer <NUM> upon reception.

The receiving circuit <NUM> may amplify the electrical signal from the transducer <NUM>, and output it to the A/D converter <NUM>. The A/D converter <NUM> may convert the electrical signal from the receiving circuit <NUM> into digital data (i.e., echo data), and output it to the processing circuitry <NUM>.

The processing circuitry <NUM> may be a computer including a CPU, a RAM, a ROM, a nonvolatile memory, and an input/output interface. The functional parts included in the processing circuitry <NUM> may be realized by the CPU executing information processing according to a program loaded to the RAM from the ROM or the nonvolatile memory. The program may be provided, for example, through information storage medium, such as an optical disc or a memory card, or may be provided, for example, through a communication network, such as the Internet.

The display unit <NUM> is, for example, a liquid crystal display. The user interface <NUM> is, for example, a button switch or a touch panel. The notifier <NUM> is, for example, a speaker or a buzzer.

<FIG> is a flowchart illustrating one example of a procedure of a display control executed by the processing circuitry <NUM>, in order to realize the display controlling module <NUM>. <FIG> is a view illustrating one example of a mark table which is referred when executing the display control.

The processing circuitry <NUM> may first acquire the echo data from the A/D converter <NUM> (S31), and then generate the echo image based on the acquired echo data (S32). As illustrated in the example of <FIG>, the echo image may include the fish image F and the waterbed image G.

Next, the processing circuitry <NUM> may acquire the estimated fish species from the fish species estimation device <NUM> (S33).

Next, the processing circuitry <NUM> may refer to the mark table and read out a mark corresponding to the acquired fish species (S34). As illustrated in the example of <FIG>, in the mark table, mark characters and a mark image which constitute each mark may be associated with the corresponding fish species. A character string indicative of a fish species is included in a column of the mark characters, and a file name of an image may be included in a column of the mark image. The mark table may be stored in the memory of the processing circuitry <NUM>, or may be stored in the database <NUM>.

Next, the processing circuitry <NUM> may add the read-out mark to the echo image (S35), and output the echo image attached with the mark to the display unit <NUM> (S36). Thus, the echo image with the mark may be displayed on the display unit <NUM>.

As illustrated in the example of <FIG>, over the echo image, the mark image M and the mark characters L may be added so as to be associated with the fish image F. In the illustrated example, the mark image M may be an image of an enclosing line, and it may be disposed so that the enclosing line encloses the fish image F. The mark characters L may be disposed near the mark image M. The example of <FIG> is an echo image illustrating a larger area than that of <FIG>. Like this case, when the echo image includes a plurality of fish images F, the mark image M and the mark characters L may be added to each fish image F. The mark image M may be desirable to be in a different color and have a different shape for every fish species in order to facilitate the discrimination.

As described above, since the echo image to which the marks are added is displayed on the display unit <NUM>, the user is able to recognize the fish species of the fish image included in the echo image. Note that, although in this embodiment the fish species estimated by the fish species estimation device <NUM> is used, the fish species distinguished by the conventional technique disclosed in <CIT> etc. may be used for the addition of marks.

Moreover, the addition of the mark may be performed only for the fish species specified by the user operating the user interface <NUM>. Therefore, it may become easier for the user to recognize a desired fish species. Similarly, the registration to the detected fish species database (refer to <FIG>) described above may also be performed only when the user operates the user interface <NUM>. Therefore, the user can save the detected fish species after checking the mark displayed on the display unit <NUM>.

Note that, although in this embodiment one example in which the mark is added to the echo image displayed on the display unit <NUM> of the fish finder <NUM> is described, the adding of the mark is not limited to on the display unit <NUM>. For example, as illustrated in the example of <FIG>, the mark of the mark image M and the mark character L may be added to the nautical chart image displayed on the display unit (not illustrated) of the GPS plotter <NUM>, based on the fish species and the position data which are registered to the detected fish species database (refer to <FIG>).

<FIG> is a flowchart illustrating one example of a procedure of a fish species notification executed by the processing circuitry <NUM>, in order to realize the matching determining module <NUM>. The processing circuitry <NUM> may first receive a specification of a fish species from the user (S41). The specification of the fish species is performed by the user operating the user interface <NUM>. Next, the processing circuitry <NUM> may acquire the fish species estimated from the fish species estimation device <NUM> (S42).

Next, the processing circuitry <NUM> may determine whether the estimated fish species matches with the fish species specified by the user (S43). If matched (S43: YES), the processing circuitry <NUM> may drive the notifier <NUM> to notify the user that the specified fish species has appeared (S44). The notification may be performed by, for example, voice from the speaker or buzzer.

Claim 1:
A fish species estimating system (<NUM>), comprising:
an acquiring module (<NUM>) configured to acquire a data set at least including position data and echo data generated from a reflected wave of an ultrasonic wave emitted underwater; and
a reasoning module (<NUM>) configured to estimate a fish species of a fish image included in the echo data of the data set acquired by the acquiring module (<NUM>), by using a learned model created by machine learning where the data set is used as input data and the fish species is used as teacher data,
characterized in that the fish species estimating system (<NUM>) further comprises
a distinguishing module (<NUM>) configured to distinguish the fish species of the fish image included in image data generated by a camera configured to image fish caught; and
a learning module (<NUM>) configured to create the learned model by using the fish species distinguished by the distinguishing module (<NUM>) as teacher data,
wherein the distinguishing module (<NUM>) distinguishes the fish species of the fish image included in the image data by using a further learned model created by machine learning where the image data is used as input data and the fish species is used as teacher data, and
wherein the acquiring module (<NUM>) associates the image data with the data set, when a distance between a position when signs of fish are detected based on the echo data and a position where the image data is generated is below a threshold.