Abstract: A method for situation awareness is provided. The method comprises: preparing a neural network trained by a learning set, wherein the learning set includes a plurality of maritime images and maritime information including object type information which includes a first type index for a vessel, a second type index for a water surface and a third type index for a ground surface, and distance level information which includes a first level index indicating that a distance is undefined, a second level index indicating a first distance range and a third level index indicating a second distance range greater than the first distance range; obtaining a target maritime image generated from a camera; and determining a distance of a target vessel based on the distance level index of the maritime information being outputted from the neural network which receives the target maritime image and having the first type index.

Abstract: A method for situation awareness is provided. The method comprises: preparing a neural network trained by a learning set, wherein the learning set includes a plurality of maritime images and maritime information including object type information which includes a first type index for a vessel, a second type index for a water surface and a third type index for a ground surface, and distance level information which includes a first level index indicating that a distance is undefined, a second level index indicating a first distance range and a third level index indicating a second distance range greater than the first distance range; obtaining a target maritime image generated from a camera; and determining a distance of a target vessel based on the distance level index of the maritime information being outputted from the neural network which receives the target maritime image and having the first type index.

Abstract: The present invention relates to a method for acquiring an object information, the method comprising: obtaining an input image acquired by capturing a sea; obtaining a noise level of the input image; when the noise level indicates a noise lower than a predetermined level, acquiring an object information related to an obstacle included in the input image from the input image by using a first artificial neural network, and when the noise level indicates a noise higher than the predetermined level, obtaining a noise-reduced image of which the environmental noise is reduced from the input image by using a second artificial neural network, and acquiring an object information related to an obstacle included in the sea from the noise-reduced image by using the first artificial neural network.

Abstract: The present invention relates to a method for acquiring an object information, the method comprising: obtaining an input image acquired by capturing a sea; obtaining a noise level of the input image; when the noise level indicates a noise lower than a predetermined level, acquiring an object information related to an obstacle included in the input image from the input image by using a first artificial neural network, and when the noise level indicates a noise higher than the predetermined level, obtaining a noise-reduced image of which the environmental noise is reduced from the input image by using a second artificial neural network, and acquiring an object information related to an obstacle included in the sea from the noise-reduced image by using the first artificial neural network.

Abstract: A method for situation awareness is provided. The method comprises: preparing a neural network trained by a learning set, wherein the learning set includes a plurality of maritime images and maritime information including object type information which includes a first type index for a vessel, a second type index for a water surface and a third type index for a ground surface, and distance level information which includes a first level index indicating that a distance is undefined, a second level index indicating a first distance range and a third level index indicating a second distance range greater than the first distance range; obtaining a target maritime image generated from a camera; and determining a distance of a target vessel based on the distance level index of the maritime information being outputted from the neural network which receives the target maritime image and having the first type index.

Abstract: A method for situation awareness is provided. The method comprises: preparing a neural network trained by a learning set, wherein the learning set includes a plurality of maritime images and maritime information including object type information which includes a first type index for a vessel, a second type index for a water surface and a third type index for a ground surface, and distance level information which includes a first level index indicating that a distance is undefined, a second level index indicating a first distance range and a third level index indicating a second distance range greater than the first distance range; obtaining a target maritime image generated from a camera; and determining a distance of a target vessel based on the distance level index of the maritime information being outputted from the neural network which receives the target maritime image and having the first type index.

Abstract: The present invention relates to a method for monitoring a harbor performed by a computing device, the method for monitoring the harbor according to an aspect of the present invention comprising: obtaining a harbor image having a first view attribute; generating a segmentation image having the first view attribute and corresponding to the harbor image by performing an image segmentation using an artificial neural network trained to output information, from an input image, related to an object included in the input image; generating a transformed segmentation image having a second view attribute from the segmentation image having the first view attribute based on a first view transformation information used to transform an image having the first view attribute into an image having the second view attribute different from the first view attribute; and calculating berthing guide information of the ship based on the transformed segmentation image.

Abstract: The present invention relates to a method for acquiring an object information, the method comprising: obtaining an input image acquired by capturing a sea; obtaining a noise level of the input image; when the noise level indicates a noise lower than a predetermined level, acquiring an object information related to an obstacle included in the input image from the input image by using a first artificial neural network, and when the noise level indicates a noise higher than the predetermined level, obtaining a noise-reduced image of which the environmental noise is reduced from the input image by using a second artificial neural network, and acquiring an object information related to an obstacle included in the sea from the noise-reduced image by using the first artificial neural network.

Abstract: The present invention relates to a method for acquiring an object information, the method comprising: obtaining an input image acquired by capturing a sea; obtaining a noise level of the input image; when the noise level indicates a noise lower than a predetermined level, acquiring an object information related to an obstacle included in the input image from the input image by using a first artificial neural network, and when the noise level indicates a noise higher than the predetermined level, obtaining a noise-reduced image of which the environmental noise is reduced from the input image by using a second artificial neural network, and acquiring an object information related to an obstacle included in the sea from the noise-reduced image by using the first artificial neural network.

Abstract: The present invention relates to a method for acquiring an object information, the method comprising: obtaining an input image acquired by capturing a sea; obtaining a noise level of the input image; when the noise level indicates a noise lower than a predetermined level, acquiring an object information related to an obstacle included in the input image from the input image by using a first artificial neural network, and when the noise level indicates a noise higher than the predetermined level, obtaining a noise-reduced image of which the environmental noise is reduced from the input image by using a second artificial neural network, and acquiring an object information related to an obstacle included in the sea from the noise-reduced image by using the first artificial neural network.

Abstract: The present invention relates to a method by which a computing means monitors a harbor, and a harbor monitoring method, according to one aspect of the present invention, comprises the steps of: acquiring a harbor image; generating a segmentation image corresponding to the harbor image; generating a display image corresponding to the harbor image and having a first view attribute; generating a conversion segmentation image, which corresponds to the segmentation image and has a second view attribute different from the first view attribute; matching the display image so as to generate a panoramic image; matching the conversion segmentation image so as to generate a matching segmentation image; calculating ship mooring guide information on the basis of the matching segmentation image; and outputting the mooring guide information together with the panoramic image.

Abstract: A method for situation awareness is provided. The method comprises: preparing a neural network trained by a learning set, wherein the learning set includes a plurality of maritime images and maritime information including object type information which includes a first type index for a vessel, a second type index for a water surface and a third type index for a ground surface, and distance level information which includes a first level index indicating that a distance is undefined, a second level index indicating a first distance range and a third level index indicating a second distance range greater than the first distance range; obtaining a target maritime image generated from a camera; and determining a distance of a target vessel based on the distance level index of the maritime information being outputted from the neural network which receives the target maritime image and having the first type index.

Abstract: A method for learning a neural network performed by a computing means wherein the neural network receives a marine image and outputs information about a type and a distance of at least one object included in the marine image is provided. The method comprises: obtaining a marine image including a sea and an obstacle; obtaining a labelling data generated based on the marine image; obtaining an output data by using a neural network, wherein the neural network receives the marine image and outputs the output data; calculating an error value by using the labelling data and the output data; and updating the neural network based on the error value; wherein the labelling data and the output data are determined by a combination of information about a type and a distance of an object.

Abstract: A method for learning a neural network performed by a computing means wherein the neural network receives a marine image and outputs information about a type and a distance of at least one object included in the marine image is provided. The method comprises: obtaining a marine image including a sea and an obstacle; obtaining a labelling data generated based on the marine image; obtaining an output data by using a neural network, wherein the neural network receives the marine image and outputs the output data; calculating an error value by using the labelling data and the output data; and updating the neural network based on the error value; wherein the labelling data and the output data are determined by a combination of information about a type and a distance of an object.

Abstract: An underground localization method and a system therefor are provided. The underground localization method includes computing location information of a dead reckoning using a gyro sensor and a distance movement sensor of moving equipment, measuring physical information according to the computed location information of the dead reckoning and searching for the physical information which is determined as being matched with the location information of the dead reckoning, and optimizing a location by reflecting location information of the physical information which is determined as being matched with the location information of the dead reckoning.

Abstract: An underground localization method and a system therefor are provided. The underground localization method includes computing location information of a dead reckoning using a gyro sensor and a distance movement sensor of moving equipment, measuring physical information according to the computed location information of the dead reckoning and searching for the physical information which is determined as being matched with the location information of the dead reckoning, and optimizing a location by reflecting location information of the physical information which is determined as being matched with the location information of the dead reckoning.

Abstract: A method of localization and mapping of a mobile robot may reduce position errors in localization and mapping using a plurality of vector field sensors. The method includes acquiring a relative coordinate in a movement space using an encoder, acquiring an absolute coordinate in the movement space by detecting intensity and direction of a signal using vector field sensors, defining a plurality of virtual cells on a surface of the movement space such that each of the cells has a plurality of nodes having predetermined positions, and updating position information about the nodes of the cells based on the relative coordinate acquired through the encoder and the absolute coordinate acquired through the vector field sensors and implementing localization and mapping in the movement space in a manner that position information of a new node is estimated while position information of a previous node is determined.

Abstract: A method of localization and mapping of a mobile robot may reduce position errors in localization and mapping using a plurality of vector field sensors. The method includes acquiring a relative coordinate in a movement space using an encoder, acquiring an absolute coordinate in the movement space by detecting intensity and direction of a signal using vector field sensors, defining a plurality of virtual cells on a surface of the movement space such that each of the cells has a plurality of nodes having predetermined positions, and updating position information about the nodes of the cells based on the relative coordinate acquired through the encoder and the absolute coordinate acquired through the vector field sensors and implementing localization and mapping in the movement space in a manner that position information of a new node is estimated while position information of a previous node is determined.