KR20230053846A - Method for collecting garbage - Google Patents

Abstract

An embodiment of the present disclosure relates to a method for collecting trash, which is performed by a computing device. The method includes the steps of: using a pre-trained neural network model, generating trash information including the amount and location of trash based on a previously captured first image; and transmitting the trash information to a plurality of terminals. Therefore, the trash can be collected efficiently using the neural network model.

Description

Method for collecting garbage {METHOD FOR COLLECTING GARBAGE}

The present disclosure relates to a method for collecting garbage, and more particularly, to a method for collecting garbage using a neural network model.

In general, the amount of garbage generated has been continuously increasing in recent years and effective solutions are needed to collect it. In order to solve this problem, there are attempts to use IoT-based garbage tracking, RFID-based location tags, etc., but this has a problem that a new sensor must be attached, and the sensor can also become garbage.

Republic of Korea Patent Publication No. 10-2015-0100092 (2017.01.23. Publication)

The present disclosure has been made in response to the above background art, and an object of the present disclosure is to provide a method for collecting garbage using a neural network model.

The technical problems of the present disclosure are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art from the following description.

As a method for collecting garbage performed by a computing device including one or more processors according to some embodiments of the present disclosure to solve the above problems, using a pre-trained neural network model, 1 generating garbage information including an amount and location of garbage based on the image; and transmitting the garbage information to a plurality of terminals.

In an alternative embodiment, the step of generating garbage information including the amount and location of garbage based on the pre-captured first image using the pre-trained neural network model may include: Receiving location information of a first image and the pre-captured first image; identifying the garbage based on the pre-captured first image using the pretrained neural network model; calculating the amount of the garbage based on the size of the identified garbage; and generating the garbage information including quantitative information about the calculated amount of garbage and location information of the previously photographed first image.

As an alternative embodiment, the plurality of terminals may include at least one of a terminal belonging to a specific affiliation or a personal terminal not included in the specific affiliation.

As an alternative embodiment, when receiving the first response to collect the garbage and the location information of the first terminal from a first terminal that is the belonging terminal among the plurality of terminals, the location information of the first terminal and the generating recommended route information from the first terminal to the garbage based on garbage information; and transmitting the generated recommendation route information to the first terminal.

As an alternative embodiment, when receiving the second response to collect the garbage and the location information of the second terminal from the second terminal that is the personal terminal among the plurality of terminals, the garbage information and the garbage collection The method may further include providing reward information including a reward to the second terminal based on the information about the collection point.

As an alternative embodiment, the step of providing reward information including a reward to the second terminal based on the garbage information and information about a collection point for collecting the garbage may include photographing the garbage from the second terminal. Receiving information on a second image; receiving information about a third image of the collection point from the second terminal; calculating a movement time based on the information on the second image and the information on the third image; Calculating a moving distance based on the garbage information and information about the collection point; determining reward information including the reward based on the calculated travel time and travel distance; and providing reward information including the determined reward to the second terminal.

As a computer program stored in a computer readable storage medium according to some embodiments of the present disclosure for solving the above problems, the computer program, when executed in one or more processors, causes the processor to collect garbage. The method includes: generating garbage information including an amount and location of garbage based on a pre-photographed first image using a pretrained neural network model; and transmitting the garbage information to a plurality of terminals.

A computing device for collecting garbage according to some embodiments of the present disclosure for solving the above problems, comprising: one or more processors including one or more cores; a memory containing program codes executable by the processor; and a network unit, wherein the processor generates garbage information including an amount and location of garbage based on a pre-photographed first image using a pretrained neural network model, and converts the garbage information into a plurality of pieces. can be transmitted to terminals of

The present disclosure can efficiently collect garbage using a neural network model.

Effects obtainable in the present disclosure are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the description below. .

Various aspects are now described with reference to the drawings, wherein like reference numbers are used to collectively refer to like elements. In the following embodiments, for explanation purposes, numerous specific details are set forth in order to provide a thorough understanding of one or more aspects. However, it will be apparent that such aspect(s) may be practiced without these specific details.
1 is a diagram illustrating a system for collecting garbage according to some embodiments of the present disclosure.
2 is a block diagram of a computing device for performing garbage collection according to some embodiments of the present disclosure.
3 is a flowchart illustrating a garbage collection process performed in a computing device according to some embodiments of the present disclosure.
4 is a flowchart illustrating a process of generating garbage information performed in a computing device according to some embodiments of the present disclosure.
5 is a flowchart illustrating a process of transmitting recommended route information performed by a computing device according to some embodiments of the present disclosure.
6 and 7 are flowcharts illustrating a process of providing reward information including a reward performed by a computing device according to some embodiments of the present disclosure.
8 is a schematic diagram illustrating a neural network according to some embodiments of the present disclosure.
9 depicts a simplified and general schematic diagram of an exemplary computing environment in which embodiments of the present disclosure may be implemented.

Various embodiments are now described with reference to the drawings. In this specification, various descriptions are presented to provide an understanding of the present disclosure. However, it is apparent that these embodiments may be practiced without these specific details.

The terms “component,” “module,” “system,” and the like, as used herein, refer to a computer-related entity, hardware, firmware, software, a combination of software and hardware, or an execution of software. For example, a component may be, but is not limited to, a procedure, processor, object, thread of execution, program, and/or computer running on a processor. For example, both an application running on a computing device and a computing device may be components. One or more components may reside within a processor and/or thread of execution. A component can be localized within a single computer. A component may be distributed between two or more computers. Also, these components can execute from various computer readable media having various data structures stored thereon. Components may be connected, for example, via signals with one or more packets of data (e.g., data and/or signals from one component interacting with another component in a local system, distributed system) to other systems and over a network such as the Internet. data being transmitted) may communicate via local and/or remote processes.

In addition, the term “or” is intended to mean an inclusive “or” rather than an exclusive “or”. That is, unless otherwise specified or clear from the context, “X employs A or B” is intended to mean one of the natural inclusive substitutions. That is, X uses A; X uses B; Or, if X uses both A and B, "X uses either A or B" may apply to either of these cases. Also, the term "and/or" as used herein should be understood to refer to and include all possible combinations of one or more of the listed related items.

Also, the terms "comprises" and/or "comprising" should be understood to mean that the features and/or components are present. However, it should be understood that the terms "comprises" and/or "comprising" do not exclude the presence or addition of one or more other features, elements, and/or groups thereof. Also, unless otherwise specified or where the context clearly indicates that a singular form is indicated, the singular in this specification and claims should generally be construed to mean "one or more".

In addition, the term “at least one of A or B” should be interpreted as meaning “when only A is included”, “when only B is included”, and “when A and B are combined”.

Those skilled in the art will further understand that the various illustrative logical blocks, components, modules, circuits, means, logics, and algorithm steps described in connection with the embodiments disclosed herein may be implemented using electronic hardware, computer software, or combinations of both. It should be recognized that it can be implemented as To clearly illustrate the interchangeability of hardware and software, various illustrative components, blocks, configurations, means, logics, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented in hardware or as software depends on the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application. However, such implementation decisions should not be interpreted as causing a departure from the scope of this disclosure.

The description of the presented embodiments is provided to enable any person skilled in the art to use or practice the present invention. Various modifications to these embodiments will be apparent to those skilled in the art of this disclosure. The general principles defined herein may be applied to other embodiments without departing from the scope of this disclosure. Thus, the present invention is not limited to the embodiments presented herein. The present invention is to be accorded the widest scope consistent with the principles and novel features set forth herein.

1 is a diagram illustrating a system for collecting garbage according to some embodiments of the present disclosure.

According to some embodiments of the present disclosure, in order to collect garbage, the computing device 100 uses a neural network model to provide garbage information about the amount and location of garbage based on a pre-photographed image received from the camera server 200. It is generated and transmitted to the plurality of terminals 300 so that each user of the plurality of terminals can check the garbage information and collect the garbage. Accordingly, the computing device 100 may efficiently collect garbage using the neural network model.

Referring to FIG. 1 , a system for collecting garbage may include a computing device 100, a camera server 200, a plurality of terminals 300, and a network. However, it is not limited thereto, and the garbage collection system may include other configurations, and only some of the disclosed configurations may constitute a garbage collection system.

A detailed description of the computing device 100 will be described later with reference to FIG. 2 .

2 is a block diagram of a computing device for performing garbage collection according to some embodiments of the present disclosure.

The configuration of the computing device 100 shown in FIG. 2 is only a simplified example. In one embodiment of the present disclosure, the computing device 100 may include other components for performing a computing environment of the computing device 100, and only some of the disclosed components may constitute the computing device 100.

The computing device 100 may include a processor 110 , a memory 130 , and a network unit 150 .

The processor 110 may include one or more cores, and includes a central processing unit (CPU), a general purpose graphics processing unit (GPGPU), and a tensor processing unit (TPU) of a computing device. unit), data analysis, and processors for deep learning. The processor 110 may read a computer program stored in the memory 130 and process data for machine learning according to an embodiment of the present disclosure. According to an embodiment of the present disclosure, the processor 110 may perform an operation for learning a neural network. The processor 110 is used for neural network learning, such as processing input data for learning in deep learning (DL), extracting features from input data, calculating errors, and updating neural network weights using backpropagation. calculations can be performed. At least one of the CPU, GPGPU, and TPU of the processor 110 may process learning of the neural network. For example, the CPU and GPGPU can process learning of neural networks and data classification using neural networks. In addition, in an embodiment of the present disclosure, the neural network learning and data classification using the neural network may be processed by using processors of a plurality of computing devices together. In addition, a computer program executed in a computing device according to an embodiment of the present disclosure may be a CPU, GPGPU or TPU executable program.

A garbage collection method performed by the processor 110 according to some embodiments of the present disclosure will be described later with reference to FIGS. 3 to 7 .

3 is a flowchart illustrating a garbage collection process performed in a computing device according to some embodiments of the present disclosure.

Referring to FIG. 3 , the processor 110 may generate garbage information including an amount and location of garbage based on a pre-photographed first image using a pretrained neural network model (S110). A specific method of generating garbage information in the processor 110 will be described later with reference to FIG. 4 .

4 is a flowchart illustrating a process of generating garbage information performed in a computing device according to some embodiments of the present disclosure.

Referring to FIG. 4 , the processor 110 may receive a pre-captured first image and location information of the pre-captured first image from the camera server 200 (S111).

For example, the processor 110 may receive a pre-captured first image and location information of the pre-captured first image from the camera server 200 every predetermined time (eg, 5 minutes, etc.).

The processor 110 may identify garbage based on a pre-photographed first image using a pretrained neural network model (S112).

The neural network model may include an object detection model learned to detect an object based on the preprocessed image data after performing preprocessing of converting a plurality of stored previously captured images into a 2D pixel coordinate system. The object detection model may include a You Only Look Once (YOLO) model, a Region-based Convolutional Neural Network (R-CNN) model, and the like. However, it is not limited thereto.

The processor 110 may calculate the amount of garbage based on the size of the identified garbage (S113).

For example, the processor 110 may estimate and calculate the amount of garbage according to the size of the identified garbage. Specifically, the processor 110 may calculate the amount of garbage based on the number of pixels occupied by the identified garbage.

The processor 110 may generate garbage information including quantitative information about the calculated amount of garbage and location information of a pre-captured first image (S114).

For example, the processor 110 receives the location information of the previously captured first image from the camera server 200 and generates quantitative information on the calculated amount of garbage and garbage including the location information of the previously captured first image. information can be generated.

Referring back to FIG. 3 , the processor 110 may transmit garbage information to a plurality of terminals 30 (S120).

For example, the processor 110 may transmit garbage information to a plurality of terminals 30 existing within a certain distance (eg, 1 km) based on the garbage location included in the garbage information. The processor 110 may receive location information of the plurality of terminals 30 from the plurality of terminals 30 at intervals of a predetermined time (eg, 10 minutes) and store them in the memory 130 . Therefore, the processor 110 does not transmit garbage information to the entire plurality of terminals 30 stored in the memory 130, but sends garbage information to at least one of the plurality of terminals 30 existing within a certain distance. can transmit Accordingly, the processor 110 may efficiently transmit garbage information to the plurality of terminals 30 .

For another example, the processor 110 may transmit garbage information only to a specific terminal among a plurality of terminals 30 based on quantitative information about the amount of garbage. The processor 110 may determine the type of the plurality of terminals 200 according to whether or not they belong to a specific group. Here, the plurality of terminals 200 may include at least one of a terminal belonging to a specific affiliation or a personal terminal not included in a specific affiliation. That is, the processor 110 may determine the plurality of terminals 200 as belonging terminals or individual terminals. Here, the specific affiliation may be a group that specializes in garbage collection. For example, a specific affiliation may be a group of trucks. However, a specific affiliation is not limited to this. Also, the user of the personal terminal may be an ordinary person who is not included in a specific affiliation.

Accordingly, the processor 110 may transmit garbage information only to a terminal belonging to a specific belonging among the plurality of terminals 30 . That is, when the amount of garbage is too large, the processor 110 transfers garbage information only to the belonging terminal, which is a terminal of the user capable of collecting a large amount of garbage, and unnecessary garbage to a personal terminal that cannot collect a large amount of garbage. Information can be prevented from being transmitted.

As described above with reference to FIGS. 3 and 4 , the processor 110 of the computing device 100 identifies garbage using a neural network model, generates garbage information about the amount and location of garbage, and converts the generated garbage information to It can be transmitted to a plurality of terminals. Accordingly, the processor 110 of the computing device 100 recognizes irregularly generated garbage based on the image transmitted from the camera server 200 and sends the garbage information to the plurality of terminals 300 of the collector that collects the garbage. It can be sent to the collector to collect the garbage in real time.

The processor 110 may transmit recommended route information to the plurality of terminals 200 . A detailed description of this will be described later with reference to FIG. 5 .

5 is a flowchart illustrating a process of transmitting recommended route information performed by a computing device according to some embodiments of the present disclosure.

Referring to FIG. 5 , when the processor 110 receives a first response to collect garbage and location information of the first terminal from a first terminal among a plurality of terminals 300, the processor 110 collects the location information of the first terminal and the garbage. Based on the information, it is possible to generate recommended route information from the first terminal to the garbage (S130).

For example, the processor 110 may generate recommended route information when receiving a first response to collect garbage from a first terminal among the plurality of terminals 300 and location information of the first terminal. there is.

The recommended route information may mean a route that can go between the location information of the first terminal and the location of the garbage by the shortest distance. Accordingly, the processor 110 may generate recommended route information through a wayfinding algorithm (eg, an A* algorithm).

The processor 110 may transmit the generated recommended route information to the first terminal (S140).

As described above with reference to FIGS. 3 and 4 , the processor 110 of the computing device 100, when the user of the belonging terminal collects garbage, additionally generates recommended route information and transmits the information to the belonging terminal to the user of the belonging terminal. can be used to quickly collect trash.

Meanwhile, the processor 110 may provide reward information including a reward to the plurality of terminals 200 . A detailed description thereof will be described later with reference to FIGS. 6 and 7 .

6 and 7 are flowcharts illustrating a process of providing reward information including a reward performed by a computing device according to some embodiments of the present disclosure.

Referring to FIG. 6 , when the processor 110 receives a second response to collect garbage from a second terminal among a plurality of terminals and location information of the second terminal, the processor 110 determines information about the garbage information and a collection point for collecting garbage. Based on the information, reward information including a reward may be provided to the second terminal (S150).

For example, when the processor 110 receives a second response to collect garbage and location information of the second terminal from a second terminal that is a personal terminal among a plurality of terminals, the processor 110 sends the garbage information and the garbage collection site to the garbage collection site. Reward information including a reward may be provided to the second terminal based on the information about. Accordingly, the processor 110 may induce the general public, a user of the personal terminal, to participate in garbage collection by providing reward information including a reward for garbage collection to the second terminal, which is a personal terminal.

Referring to FIG. 7 , the processor 110 may receive information about a second image of garbage captured from a second terminal (S151).

For example, the processor 110 may receive information about a second image including information (eg, an ID number, GPS coordinates, etc.) of a recycle bin containing garbage from the second terminal. Accordingly, the processor 110 may estimate the location of the trash bin through information on the trash bin. In addition, the processor 110 may also receive information about the time the second image was taken from the second terminal.

The processor 110 may receive information about a third image of the collection point from the second terminal (S152).

For example, the processor 110 may receive information about a third image including information of a collection point (location of a collection point, etc.) from the second terminal. In detail, the processor 110 may receive information about a third image including information on the collection point obtained by scanning a QR code attached to the collection point in the second terminal. In addition, the processor 110 may also receive information about the time when the third image was captured from the second terminal.

The processor 110 may calculate the movement time based on the information on the second image and the information on the third image (S153).

For example, the processor 110 may calculate the actual movement time of the user of the second terminal based on information about the time the second image was captured and information about the time the third image was captured.

The processor 110 may compare the calculated actual travel time with the estimated travel time predicted based on the garbage information and information on the collection point.

For example, if the difference between the calculated travel time and the predicted travel time does not correspond within a preset range (eg, 5 minutes), the processor 110 sets the travel time as the predicted travel time instead of the actual travel time. time can be determined. Accordingly, the processor 110 can prevent the user of the terminal from extending the travel time to receive more rewards.

The processor 110 may calculate the moving distance based on the garbage information and the information on the collection point (S154).

For example, the processor 110 may calculate the moving distance based on the location of the garbage and the location of the collection point. The movement distance may be an optimal distance. Accordingly, the processor 110 can prevent the user of the terminal from increasing the moving distance to receive more rewards.

The processor 110 may determine reward information including a reward based on the calculated travel time and travel distance (S155).

The reward may be virtual currency that can be used in an application provided by the computing device 100 . For example, virtual currency may be provided as coins, tokens, points, and the like. However, it is not limited thereto.

Accordingly, the processor 110 may determine how much of a reward is to be provided based on the moving time and the moving distance, and may generate reward information including the reward.

The processor 110 may provide reward information including the determined reward to the second terminal (S156).

According to an embodiment of the present disclosure, the memory 130 may store any type of information generated or determined by the processor 110 and any type of information received by the network unit 150 . For example, the memory 130 may store location information, image information, and image information received from the camera server 200 received from the plurality of terminals 300 . However, it is not limited thereto. In addition, the processor 110 can prevent the camera server 200 and the plurality of terminals 300 from accessing data stored in the memory 130, and the location received from the plurality of terminals 300. Information can be deleted from the memory 130 after a certain period of time.

According to an embodiment of the present disclosure, the memory 130 is a flash memory type, a hard disk type, a multimedia card micro type, or a card type memory (eg, SD or XD memory, etc.), RAM (Random Access Memory, RAM), SRAM (Static Random Access Memory), ROM (Read-Only Memory, ROM), EEPROM (Electrically Erasable Programmable Read-Only Memory), PROM (Programmable Read-Only Memory) -Only Memory), a magnetic memory, a magnetic disk, and an optical disk may include at least one type of storage medium. The computing device 100 may operate in relation to a web storage that performs a storage function of the memory 130 on the Internet. The above description of the memory is only an example, and the present disclosure is not limited thereto.

According to an embodiment of the present disclosure, the network unit 150 may use any type of known wired or wireless communication system.

The network unit 150 transmits and receives a data set necessary for learning or inference of a neural network model, information processed by the processor 110, a user interface, etc. through communication with the camera server 200 or a plurality of terminals 300. can do. For example, the network unit 150 may receive a data set necessary for learning a neural network model through communication with a database of the camera server 200 . The network unit 150 may transmit the output of the neural network model learned by the processor 110 to an external database.

Meanwhile, the computing device 100 according to an embodiment of the present disclosure may include a server as a computing system that transmits and receives data through communication with the camera server 200 or a plurality of terminals 300 . For example, the computing device 100 that is a server may train at least one neural network model through a training data set.

In an additional embodiment, the computing device 100 may include any type of terminal that receives a data resource generated by an arbitrary server (eg, the camera server 200) and performs additional information processing.

Referring back to FIG. 1 , the camera server 200 may receive images taken in real time from a plurality of cameras (eg, CCTVs). The camera server 200 may store location information of a plurality of cameras. Accordingly, when the camera server 200 receives image information from one of the plurality of cameras, the computing device 100 transmits the received image and location information of the camera that sent the image (location information of the received image) through the network. can be sent to However, the camera server 200 is not limited thereto and may transmit various types of information to the computing device 100 .

The plurality of terminals 300 receive garbage information, recommended route information, reward information, etc. from the computing device 100, and display a response to collect garbage, a second image of the garbage, a third image of the collection point, etc. can transmit. Also, as described above, the plurality of terminals 300 may include at least one of a terminal belonging to a specific affiliation or a personal terminal not included in a specific affiliation.

The plurality of terminals 300 may transmit the above-described information to the computing device 100 through applications. In addition, the plurality of terminals 300 may use the reward received from the computing device 100 through an application. For example, the plurality of terminals 300 may be used by a specific affiliated company (eg, a store, a movie theater, etc.).

The network may include any wired or wireless communication network through which the computing device 100, the camera server 200, and the plurality of terminals 300 can transmit and receive arbitrary types of data and signals.

8 is a schematic diagram illustrating a neural network according to some embodiments of the present disclosure.

A neural network model according to an embodiment of the present disclosure may include a neural network for performing a specific task such as detection, classification, segmentation, and regression. A neural network may consist of a set of interconnected computational units, which may generally be referred to as nodes. These nodes may also be referred to as neurons. A neural network includes one or more nodes. Nodes (or neurons) constituting neural networks may be interconnected by one or more links.

In a neural network, one or more nodes connected through a link may form a relative relationship of an input node and an output node. The concept of an input node and an output node is relative, and any node in an output node relationship with one node may have an input node relationship with another node, and vice versa. As described above, an input node to output node relationship may be created around a link. More than one output node can be connected to one input node through a link, and vice versa.

In a relationship between an input node and an output node connected through one link, the value of data of the output node may be determined based on data input to the input node. Here, a link interconnecting an input node and an output node may have a weight. The weight may be variable, and may be changed by a user or an algorithm in order to perform a function desired by the neural network. For example, when one or more input nodes are interconnected by respective links to one output node, the output node is set to a link corresponding to values input to input nodes connected to the output node and respective input nodes. An output node value may be determined based on the weight.

As described above, in the neural network, one or more nodes are interconnected through one or more links to form an input node and output node relationship in the neural network. Characteristics of the neural network may be determined according to the number of nodes and links in the neural network, an association between the nodes and links, and a weight value assigned to each link. For example, when there are two neural networks having the same number of nodes and links and different weight values of the links, the two neural networks may be recognized as different from each other.

A neural network may be composed of a set of one or more nodes. A subset of nodes constituting a neural network may constitute a layer. Some of the nodes constituting the neural network may form one layer based on distances from the first input node. For example, a set of nodes having a distance of n from the first input node may constitute n layers. The distance from the first input node may be defined by the minimum number of links that must be passed through to reach the corresponding node from the first input node. However, the definition of such a layer is arbitrary for explanation, and the order of a layer in a neural network may be defined in a method different from the above. For example, a layer of nodes may be defined by a distance from a final output node.

An initial input node may refer to one or more nodes to which data is directly input without going through a link in relation to other nodes among nodes in the neural network. Alternatively, in a relationship between nodes based on a link in a neural network, it may mean nodes that do not have other input nodes connected by a link. Similarly, the final output node may refer to one or more nodes that do not have an output node in relation to other nodes among nodes in the neural network. Also, the hidden node may refer to nodes constituting the neural network other than the first input node and the last output node.

In the neural network according to an embodiment of the present disclosure, the number of nodes in the input layer may be the same as the number of nodes in the output layer, and the number of nodes decreases and then increases again as the number of nodes progresses from the input layer to the hidden layer. can In addition, the neural network according to another embodiment of the present disclosure may be a neural network in which the number of nodes of the input layer may be less than the number of nodes of the output layer and the number of nodes decreases as the number of nodes increases from the input layer to the hidden layer. there is. In addition, the neural network according to another embodiment of the present disclosure is a neural network in which the number of nodes in the input layer may be greater than the number of nodes in the output layer, and the number of nodes increases as the number of nodes increases from the input layer to the hidden layer. can A neural network according to another embodiment of the present disclosure may be a neural network in the form of a combination of the aforementioned neural networks.

A deep neural network (DNN) may refer to a neural network including a plurality of hidden layers in addition to an input layer and an output layer. Deep neural networks can reveal latent structures in data. In other words, it can identify the latent structure of a photo, text, video, sound, or music (e.g., what objects are in the photo, what the content and emotion of the text are, what the content and emotion of the audio are, etc.). . Deep neural networks include convolutional neural networks (CNNs), recurrent neural networks (RNNs), auto encoders, generative adversarial networks (GANs), restricted Boltzmann machines ( It may include a restricted boltzmann machine (RBM), a deep belief network (DBN), a Q network, a U network, a Siamese network, and the like. The description of the deep neural network described above is only an example, and the present disclosure is not limited thereto.

In one embodiment of the present disclosure, a neural network may include an autoencoder. An autoencoder may be a type of artificial neural network for outputting output data similar to input data. An auto-encoder may include at least one hidden layer, and an odd number of hidden layers may be disposed between input and output layers. The number of nodes of each layer may be reduced from the number of nodes of the input layer to an intermediate layer called the bottleneck layer (encoding), and then expanded symmetrically with the reduction from the bottleneck layer to the output layer (symmetrical to the input layer). Autoencoders can perform non-linear dimensionality reduction. The number of input layers and output layers may correspond to dimensions after preprocessing of input data. In the auto-encoder structure, the number of hidden layer nodes included in the encoder may decrease as the distance from the input layer increases. If the number of nodes in the bottleneck layer (the layer with the fewest nodes located between the encoder and decoder) is too small, a sufficient amount of information may not be conveyed, so more than a certain number (e.g., more than half of the input layer, etc.) ) may be maintained.

The neural network may be trained using at least one of supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Learning of the neural network may be a process of applying knowledge for the neural network to perform a specific operation to the neural network.

A neural network can be trained in a way that minimizes output errors. In the learning of the neural network, the learning data is repeatedly input into the neural network, the output of the neural network for the training data and the error of the target are calculated, and the error of the neural network is transferred from the output layer of the neural network to the input layer in the direction of reducing the error. It is a process of updating the weight of each node of the neural network by backpropagating in the same direction. In the case of teacher learning, the learning data in which the correct answer is labeled is used for each learning data (ie, the labeled learning data), and in the case of comparative teacher learning, the correct answer may not be labeled in each learning data. That is, for example, learning data in the case of teacher learning regarding data classification may be data in which each learning data is labeled with a category. Labeled training data is input to a neural network, and an error may be calculated by comparing an output (category) of the neural network and a label of the training data. As another example, in the case of comparative history learning for data classification, an error may be calculated by comparing input learning data with a neural network output. The calculated error is back-propagated in a reverse direction (ie, from the output layer to the input layer) in the neural network, and the connection weight of each node of each layer of the neural network may be updated according to the back-propagation. The amount of change in the connection weight of each updated node may be determined according to a learning rate. The neural network's computation of input data and backpropagation of errors can constitute a learning cycle (epoch). The learning rate may be applied differently according to the number of iterations of the learning cycle of the neural network. For example, a high learning rate may be used in the early stage of neural network training to increase efficiency by allowing the neural network to quickly obtain a certain level of performance, and a low learning rate may be used in the late stage to increase accuracy.

In neural network learning, generally, training data can be a subset of real data (ie, data to be processed using the trained neural network), and therefore, errors for training data are reduced, but errors for real data are reduced. There may be incremental learning cycles. Overfitting is a phenomenon in which errors for actual data increase due to excessive learning on training data. For example, a phenomenon in which a neural network that has learned a cat by showing a yellow cat does not recognize that it is a cat when it sees a cat other than yellow may be a type of overfitting. Overfitting can act as a cause of increasing the error of machine learning algorithms. Various optimization methods can be used to prevent such overfitting. To prevent overfitting, methods such as increasing the training data, regularization, inactivating some nodes in the network during learning, and using a batch normalization layer should be applied. can

Meanwhile, according to an embodiment of the present disclosure, a computer readable medium storing a data structure is disclosed.

Data structure can refer to the organization, management, and storage of data that enables efficient access and modification of data. Data structure may refer to the organization of data to solve a specific problem (eg, data retrieval, data storage, data modification in the shortest time). A data structure may be defined as a physical or logical relationship between data elements designed to support a specific data processing function. A logical relationship between data elements may include a connection relationship between user-defined data elements. A physical relationship between data elements may include an actual relationship between data elements physically stored in a computer-readable storage medium (eg, a persistent storage device). The data structure may specifically include a set of data, a relationship between data, and a function or command applicable to the data. Through an effectively designed data structure, a computing device can perform calculations while using minimal resources of the computing device. Specifically, the computing device can increase the efficiency of operation, reading, insertion, deletion, comparison, exchange, and search through an effectively designed data structure.

The data structure can be divided into a linear data structure and a non-linear data structure according to the shape of the data structure. A linear data structure may be a structure in which only one data is connected after one data. Linear data structures may include lists, stacks, queues, and decks. A list may refer to a series of data sets in which order exists internally. The list may include a linked list. A linked list may be a data structure in which data are connected in such a way that each data is connected in a single line with a pointer. In a linked list, a pointer can contain information about connection to the next or previous data. A linked list can be expressed as a singly linked list, a doubly linked list, or a circular linked list depending on the form. A stack can be a data enumeration structure that allows limited access to data. A stack can be a linear data structure in which data can be processed (eg, inserted or deleted) at only one end of the data structure. The data stored in the stack may be a LIFO-Last in First Out (Last in First Out) data structure. A queue is a data listing structure that allows limited access to data, and unlike a stack, it can be a data structure (FIFO-First in First Out) in which data stored later comes out later. A deck can be a data structure that can handle data from either end of the data structure.

The nonlinear data structure may be a structure in which a plurality of data are connected after one data. The non-linear data structure may include a graph data structure. A graph data structure can be defined as a vertex and an edge, and an edge can include a line connecting two different vertices. A graph data structure may include a tree data structure. The tree data structure may be a data structure in which one path connects two different vertices among a plurality of vertices included in the tree. That is, it may be a data structure that does not form a loop in a graph data structure.

The data structure may include a neural network. And the data structure including the neural network may be stored in a computer readable medium. The data structure including the neural network may also include preprocessed data for processing by the neural network, data input to the neural network, weights of the neural network, hyperparameters of the neural network, data obtained from the neural network, activation function associated with each node or layer of the neural network, and neural network It may include a loss function for learning of . A data structure including a neural network may include any of the components described above. That is, the data structure including the neural network includes preprocessed data for processing by the neural network, data input to the neural network, weights of the neural network, hyperparameters of the neural network, data obtained from the neural network, activation function associated with each node or layer of the neural network, and neural network. It may be configured to include all or any combination thereof, such as a loss function for learning of . In addition to the foregoing configurations, the data structure comprising the neural network may include any other information that determines the characteristics of the neural network. In addition, the data structure may include all types of data used or generated in the computational process of the neural network, but is not limited to the above. A computer readable medium may include a computer readable recording medium and/or a computer readable transmission medium. A neural network may consist of a set of interconnected computational units, which may generally be referred to as nodes. These nodes may also be referred to as neurons. A neural network includes one or more nodes.

The data structure may include data input to the neural network. A data structure including data input to the neural network may be stored in a computer readable medium. Data input to the neural network may include training data input during a neural network learning process and/or input data input to a neural network that has been trained. Data input to the neural network may include pre-processed data and/or data subject to pre-processing. Pre-processing may include a data processing process for inputting data to a neural network. Accordingly, the data structure may include data subject to pre-processing and data generated by pre-processing. The foregoing data structure is only an example, and the present disclosure is not limited thereto.

The data structure may include the weights of the neural network. (In this specification, weights and parameters may be used in the same meaning.) Also, a data structure including weights of a neural network may be stored in a computer readable medium. A neural network may include a plurality of weights. The weight may be variable, and may be changed by a user or an algorithm in order to perform a function desired by the neural network. For example, when one or more input nodes are interconnected by respective links to one output node, the output node is set to a link corresponding to values input to input nodes connected to the output node and respective input nodes. A data value output from an output node may be determined based on the weight. The foregoing data structure is only an example, and the present disclosure is not limited thereto.

As a non-limiting example, the weights may include weights that are varied during neural network training and/or weights for which neural network training has been completed. The variable weight in the neural network learning process may include a weight at the time the learning cycle starts and/or a variable weight during the learning cycle. The weights for which neural network learning has been completed may include weights for which learning cycles have been completed. Accordingly, the data structure including the weights of the neural network may include a data structure including weights that are variable during the neural network learning process and/or weights for which neural network learning is completed. Therefore, it is assumed that the above-described weights and/or combinations of weights are included in the data structure including the weights of the neural network. The foregoing data structure is only an example, and the present disclosure is not limited thereto.

The data structure including the weights of the neural network may be stored in a computer readable storage medium (eg, a memory or a hard disk) after going through a serialization process. Serialization can be the process of converting a data structure into a form that can be stored on the same or another computing device and later reconstructed and used. A computing device may serialize data structures to transmit and receive data over a network. The data structure including the weights of the serialized neural network may be reconstructed on the same computing device or another computing device through deserialization. The data structure including the weights of the neural network is not limited to serialization. Furthermore, the data structure including the weights of the neural network is a data structure for increasing the efficiency of operation while minimizing the resource of the computing device (for example, B-Tree, Trie, m-way search tree, AVL tree, Red-Black Tree). The foregoing is only an example, and the present disclosure is not limited thereto.

The data structure may include hyper-parameters of the neural network. Also, the data structure including the hyperparameters of the neural network may be stored in a computer readable medium. A hyperparameter may be a variable variable by a user. Hyperparameters include, for example, learning rate, cost function, number of learning cycle iterations, weight initialization (eg, setting the range of weight values to be targeted for weight initialization), hidden unit number (eg, the number of hidden layers and the number of nodes in the hidden layer). The foregoing data structure is only an example, and the present disclosure is not limited thereto.

9 depicts a simplified and general schematic diagram of an exemplary computing environment in which embodiments of the present disclosure may be implemented.

Although the present disclosure has been described above as being generally embodied by a computing device, those skilled in the art will understand that the present disclosure may be combined with computer-executable instructions and/or other program modules that may be executed on one or more computers and/or may be implemented in hardware and software. It will be appreciated that it can be implemented as a combination.

Generally, program modules include routines, programs, components, data structures, etc. that perform particular tasks or implement particular abstract data types. In addition, those skilled in the art will understand that the methods of the present disclosure can be applied to single-processor or multiprocessor computer systems, minicomputers, mainframe computers as well as personal computers, handheld computing devices, microprocessor-based or programmable consumer electronics, and the like ( It will be appreciated that each of these may be implemented with other computer system configurations, including those that may be operative in connection with one or more associated devices.

The described embodiments of the present disclosure may also be practiced in distributed computing environments where certain tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

Computers typically include a variety of computer readable media. Computer readable media can be any medium that can be accessed by a computer, including volatile and nonvolatile media, transitory and non-transitory media, removable and non-transitory media. Includes removable media. By way of example, and not limitation, computer readable media may include computer readable storage media and computer readable transmission media. Computer readable storage media are volatile and nonvolatile media, transitory and non-transitory, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. includes media Computer readable storage media may include RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital video disk (DVD) or other optical disk storage device, magnetic cassette, magnetic tape, magnetic disk storage device or other magnetic storage device. device, or any other medium that can be accessed by a computer and used to store desired information.

A computer readable transmission medium typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism. Including all information delivery media. The term modulated data signal means a signal that has one or more of its characteristics set or changed so as to encode information within the signal. By way of example, and not limitation, computer readable transmission media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared, and other wireless media. Combinations of any of the above are also intended to be included within the scope of computer readable transmission media.

An exemplary environment 1100 implementing various aspects of the present disclosure is shown including a computer 1102, which includes a processing unit 1104, a system memory 1106, and a system bus 1108. do. System bus 1108 couples system components, including but not limited to system memory 1106 , to processing unit 1104 . Processing unit 1104 may be any of a variety of commercially available processors. Dual processor and other multiprocessor architectures may also be used as the processing unit 1104.

System bus 1108 may be any of several types of bus structures that may additionally be interconnected to a memory bus, a peripheral bus, and a local bus using any of a variety of commercial bus architectures. System memory 1106 includes read only memory (ROM) 1110 and random access memory (RAM) 1112 . A basic input/output system (BIOS) is stored in non-volatile memory 1110, such as ROM, EPROM, or EEPROM, and is a basic set of information that helps transfer information between components within computer 1102, such as during startup. contains routines. RAM 1112 may also include high-speed RAM, such as static RAM, for caching data.

The computer 1102 may also include an internal hard disk drive (HDD) 1114 (eg, EIDE, SATA) - the internal hard disk drive 1114 may also be configured for external use within a suitable chassis (not shown). Yes—a magnetic floppy disk drive (FDD) 1116 (e.g., for reading from or writing to a removable diskette 1118), and an optical disk drive 1120 (e.g., a CD-ROM) for reading disc 1122 or reading from or writing to other high capacity optical media such as DVDs). The hard disk drive 1114, magnetic disk drive 1116, and optical disk drive 1120 are connected to the system bus 1108 by a hard disk drive interface 1124, magnetic disk drive interface 1126, and optical drive interface 1128, respectively. ) can be connected to The interface 1124 for external drive implementation includes at least one or both of USB (Universal Serial Bus) and IEEE 1394 interface technologies.

These drives and their associated computer readable media provide non-volatile storage of data, data structures, computer executable instructions, and the like. In the case of computer 1102, drives and media correspond to storing any data in a suitable digital format. Although the description of computer readable media above refers to HDDs, removable magnetic disks, and removable optical media such as CDs or DVDs, those skilled in the art can use zip drives, magnetic cassettes, flash memory cards, cartridges, etc. It will be appreciated that other tangible media readable by the computer, such as the like, may also be used in the exemplary operating environment and any such media may include computer executable instructions for performing the methods of the present disclosure.

A number of program modules may be stored on the drive and RAM 1112, including an operating system 1130, one or more application programs 1132, other program modules 1134, and program data 1136. All or portions of the operating system, applications, modules and/or data may also be cached in RAM 1112. It will be appreciated that the present disclosure may be implemented in a variety of commercially available operating systems or combinations of operating systems.

A user may enter commands and information into the computer 1102 through one or more wired/wireless input devices, such as a keyboard 1138 and a pointing device such as a mouse 1140. Other input devices (not shown) may include a microphone, IR remote control, joystick, game pad, stylus pen, touch screen, and the like. Although these and other input devices are often connected to the processing unit 1104 through an input device interface 1142 that is connected to the system bus 1108, a parallel port, IEEE 1394 serial port, game port, USB port, IR interface, may be connected by other interfaces such as the like.

A monitor 1144 or other type of display device is also connected to the system bus 1108 through an interface such as a video adapter 1146. In addition to the monitor 1144, computers typically include other peripheral output devices (not shown) such as speakers, printers, and the like.

Computer 1102 may operate in a networked environment using logical connections to one or more remote computers, such as remote computer(s) 1148 via wired and/or wireless communications. Remote computer(s) 1148 may be a workstation, computing device computer, router, personal computer, handheld computer, microprocessor-based entertainment device, peer device, or other common network node, and generally includes It includes many or all of the components described for, but for simplicity, only memory storage device 1150 is shown. The logical connections shown include wired/wireless connections to a local area network (LAN) 1152 and/or a larger network, such as a wide area network (WAN) 1154 . Such LAN and WAN networking environments are common in offices and corporations and facilitate enterprise-wide computer networks, such as intranets, all of which can be connected to worldwide computer networks, such as the Internet.

When used in a LAN networking environment, computer 1102 connects to local network 1152 through wired and/or wireless communication network interfaces or adapters 1156. Adapter 1156 may facilitate wired or wireless communications to LAN 1152, which also includes a wireless access point installed therein to communicate with wireless adapter 1156. When used in a WAN networking environment, computer 1102 may include a modem 1158, be connected to a communicating computing device on WAN 1154, or establish communications over WAN 1154, such as over the Internet. have other means. A modem 1158, which may be internal or external and a wired or wireless device, is connected to the system bus 1108 through a serial port interface 1142. In a networked environment, program modules described for computer 1102, or portions thereof, may be stored on remote memory/storage device 1150. It will be appreciated that the network connections shown are exemplary and other means of establishing a communication link between computers may be used.

Computer 1102 is any wireless device or entity that is deployed and operating in wireless communication, eg, printers, scanners, desktop and/or portable computers, portable data assistants (PDAs), communication satellites, wireless detectable tags associated with It operates to communicate with arbitrary equipment or places and telephones. This includes at least Wi-Fi and Bluetooth wireless technologies. Thus, the communication may be a predefined structure as in conventional networks or simply an ad hoc communication between at least two devices.

Wi-Fi (Wireless Fidelity) makes it possible to connect to the Internet without wires. Wi-Fi is a wireless technology, such as a cell phone, that allows such devices, eg, computers, to transmit and receive data both indoors and outdoors, i.e. anywhere within coverage of a base station. Wi-Fi networks use a radio technology called IEEE 802.11 (a, b, g, etc.) to provide secure, reliable, and high-speed wireless connections. Wi-Fi can be used to connect computers to each other, to the Internet, and to wired networks (using IEEE 802.3 or Ethernet). Wi-Fi networks can operate in the unlicensed 2.4 and 5 GHz radio bands, for example, at 11 Mbps (802.11a) or 54 Mbps (802.11b) data rates, or in products that include both bands (dual band) .

Those skilled in the art will understand that information and signals may be represented using any of a variety of different technologies and techniques. For example, data, instructions, instructions, information, signals, bits, symbols and chips that may be referenced in the above description are voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields s or particles, or any combination thereof.

Those skilled in the art will understand that the various illustrative logical blocks, modules, processors, means, circuits, and algorithm steps described in connection with the embodiments disclosed herein are electronic hardware, (for convenience) , may be implemented by various forms of program or design code (referred to herein as software) or a combination of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the particular application and the design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

Various embodiments presented herein may be implemented as a method, apparatus, or article of manufacture using standard programming and/or engineering techniques. The term article of manufacture includes a computer program, carrier, or media accessible from any computer-readable storage device. For example, computer-readable storage media include magnetic storage devices (eg, hard disks, floppy disks, magnetic strips, etc.), optical disks (eg, CDs, DVDs, etc.), smart cards, and flash memory devices (eg, EEPROM, cards, sticks, key drives, etc.), but are not limited thereto. Additionally, various storage media presented herein include one or more devices and/or other machine-readable media for storing information.

It is to be understood that the specific order or hierarchy of steps in the processes presented is an example of exemplary approaches. Based upon design priorities, it is to be understood that the specific order or hierarchy of steps in the processes may be rearranged within the scope of this disclosure. The accompanying method claims present elements of the various steps in a sample order, but are not meant to be limited to the specific order or hierarchy presented.

The description of the presented embodiments is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art of this disclosure, and the general principles defined herein may be applied to other embodiments without departing from the scope of this disclosure. Thus, the present disclosure is not to be limited to the embodiments presented herein, but is to be interpreted in the widest scope consistent with the principles and novel features presented herein.

Claims (8)

A method for garbage collection performed by a computing device comprising one or more processors, comprising:
generating garbage information including an amount and location of garbage based on a pre-photographed first image using a pretrained neural network model; and
transmitting the garbage information to a plurality of terminals;
including,
method.
According to claim 1,
Generating garbage information including the amount and location of garbage based on the pre-photographed first image using the pretrained neural network model,
Receiving the pre-captured first image and location information of the pre-captured first image from a camera server;
identifying the garbage based on the pre-captured first image using the pretrained neural network model;
calculating the amount of the trash based on the size of the identified trash; and
generating the garbage information including quantitative information about the calculated amount of garbage and location information of the pre-captured first image;
including,
method.
According to claim 1,
The plurality of terminals,
Including at least one of a belonging terminal included in a specific belonging or a personal terminal not included in the specific belonging,
method.
According to claim 3,
When receiving a first response to collect the garbage and location information of the first terminal from a first terminal among the plurality of terminals, which is the belonging terminal,
generating recommended route information from the first terminal to the garbage based on the location information of the first terminal and the garbage information; and
transmitting the generated recommendation route information to the first terminal;
Including more,
method.
According to claim 3,
When receiving a second response to collect the garbage and location information of the second terminal from a second terminal that is the personal terminal among the plurality of terminals,
providing reward information including a reward to the second terminal based on the garbage information and information about a collection point for collecting the garbage;
Including more,
method.
According to claim 5,
Providing reward information including a reward to the second terminal based on the garbage information and information about a collection point for collecting the garbage,
Receiving information about a second image of the garbage captured from the second terminal;
receiving information about a third image of the collection point from the second terminal;
calculating a movement time based on the information on the second image and the information on the third image;
Calculating a moving distance based on the garbage information and information about the collection point;
determining reward information including the reward based on the calculated travel time and travel distance; and
providing reward information including the determined reward to the second terminal;
including,
method.
A computer program stored on a computer-readable storage medium, which, when executed on one or more processors, causes the processor to perform a method for collecting garbage, the method comprising:
generating garbage information including an amount and location of garbage based on a pre-photographed first image using a pretrained neural network model; and
transmitting the garbage information to a plurality of terminals;
including,
A computer program stored on a computer-readable storage medium.
A computing device for collecting garbage,
one or more processors including one or more cores;
a memory containing program codes executable by the processor; and
network unit;
including,
the processor,
Using a pretrained neural network model, garbage information including the amount and location of garbage is generated based on a pre-photographed first image, and
Transmitting the garbage information to a plurality of terminals,
computing device.

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