Abstract: Disclosed is an image processing method. The method includes the steps of receiving an image obtained from a plurality of vehicles positioned on a road, storing the received images according to acquisition information of the received images; determining a reference image and a target image based on images having the same acquisition information among the stored images, performing an image registration using a plurality of feature points extracted from each of the determined reference image and target image, performing a transparency process for each of the reference image and the target image performed with the image registration, extracting static objects from the transparency-processed image, and comparing the extracted static objects with objects on an electronic map pre-stored to updating the electronic map data, when the objects on the electronic map data pre-stored are different from the extracted static objects.

Abstract: There is provided a method for detecting a vehicle including receiving continuously captured front images, setting a search area of the vehicle in a target image based on a location of the vehicle or a vehicle area detected from a previous image among the front images, detecting the vehicle in the search area according to a machine learning model, and tracking the vehicle in the target image by using feature points of the vehicle extracted from the previous image according to a vehicle detection result based on the machine learning model. Since the entire image is not used as a vehicle detection area, a processing speed may be increased, and a forward vehicle tracked in an augmented reality navigation may be continuously displayed without interruption, thereby providing a stable service to the user.

Abstract: An augmented reality (AR)-based route guidance method includes acquiring a driving image captured by an image capturing device of a vehicle which is running, acquiring route data to a destination of the vehicle, recognizing both side lane markings of a lane in which the vehicle is running from the acquired driving image, generating first route guidance linear data based on the recognized both side lane markings for a region in which both side lane markings are recognized in the driving image, generating second route guidance linear data using link linear data of the route data for a region in which both side lane markings are not recognized in the driving image, combining the first route guidance linear data and the second route guidance linear data to generate combined route guidance linear data, and displaying a route guidance object in AR using the generated combined route guidance linear data.

Abstract: A method for measuring an inter-vehicle distance using a processor is provided. The method includes acquiring a driving image photographed by a photographing device of a first vehicle; detecting a second vehicle from the driving image and calculating a ratio between an image width of the detected second vehicle and an image width of a lane in which the second vehicle is located; determining a size class of the second vehicle among a plurality of size classes based on the calculated ratio; determining a width of the second vehicle based on the determined size class of the second vehicle; and calculating a distance from the photographing device to the second vehicle based on the determined width of the second vehicle, a focal length of the photographing device, and the image width of the second vehicle.

Abstract: Disclosed is an image processing method. The method includes the steps of receiving an image obtained from a plurality of vehicles positioned on a road, storing the received images according to acquisition information of the received images; determining a reference image and a target image based on images having the same acquisition information among the stored images, performing an image registration using a plurality of feature points extracted from each of the determined reference image and target image, performing a transparency process for each of the reference image and the target image performed with the image registration, extracting static objects from the transparency-processed image, and comparing the extracted static objects with objects on an electronic map pre-stored to updating the electronic map data, when the objects on the electronic map data pre-stored are different from the extracted static objects.

Abstract: There is provided a method for detecting a vehicle including receiving continuously captured front images, setting a search area of the vehicle in a target image based on a location of the vehicle or a vehicle area detected from a previous image among the front images, detecting the vehicle in the search area according to a machine learning model, and tracking the vehicle in the target image by using feature points of the vehicle extracted from the previous image according to a vehicle detection result based on the machine learning model. Since the entire image is not used as a vehicle detection area, a processing speed may be increased, and a forward vehicle tracked in an augmented reality navigation may be continuously displayed without interruption, thereby providing a stable service to the user.

Abstract: A 3D space data generation method for flight of an aerial vehicle may include receiving map data for a 3D space in which the aerial vehicle flies, and dividing a space into a flight area in which the aerial vehicle flies and a restricted area in which the aerial vehicle does not fly based on the map data and dividing the flight area and the restricted area into each unit area to generate 3D space data.

Abstract: An augmented reality (AR)-based route guidance method includes acquiring a driving image captured by an image capturing device of a vehicle which is running, acquiring route data to a destination of the vehicle, recognizing both side lane markings of a lane in which the vehicle is running from the acquired driving image, generating first route guidance linear data based on the recognized both side lane markings for a region in which both side lane markings are recognized in the driving image, generating second route guidance linear data using link linear data of the route data for a region in which both side lane markings are not recognized in the driving image, combining the first route guidance linear data and the second route guidance linear data to generate combined route guidance linear data, and displaying a route guidance object in AR using the generated combined route guidance linear data.

Abstract: A method for measuring an inter-vehicle distance using a processor is provided. The method includes acquiring a driving image photographed by a photographing device of a first vehicle; detecting a second vehicle from the driving image and calculating a ratio between an image width of the detected second vehicle and an image width of a lane in which the second vehicle is located; determining a size class of the second vehicle among a plurality of size classes based on the calculated ratio; determining a width of the second vehicle based on the determined size class of the second vehicle; and calculating a distance from the photographing device to the second vehicle based on the determined width of the second vehicle, a focal length of the photographing device, and the image width of the second vehicle.

Abstract: A method for measuring an inter-vehicle distance using a processor is provided. The method includes acquiring a driving image photographed by a photographing device of a first vehicle; detecting a second vehicle from the driving image and calculating a ratio between an image width of the detected second vehicle and an image width of a lane in which the second vehicle is located; determining a size class of the second vehicle among a plurality of size classes based on the calculated ratio; determining a width of the second vehicle based on the determined size class of the second vehicle; and calculating a distance from the photographing device to the second vehicle based on the determined width of the second vehicle, a focal length of the photographing device, and the image width of the second vehicle.

Abstract: Disclosed is a method of detecting a dangerous object for an aerial vehicle. The method includes setting an object detection area in air in which the aerial vehicle is in flight using a first sensor, a second sensor, and a third sensor; detecting an object in the set object detection area; generating detailed object information on the detected object; and determining whether the detected object is the dangerous object based on the generated detailed object information.

Abstract: There is provided a method for detecting a lane marking using a processor including acquiring a drive image captured by an image capturing device of a vehicle which is running, detecting an edge corresponding to a lane marking from the acquired drive image and generating an edge image based on the detected edge, detecting a linear component based on the detected edge and generating a linearly processed edge image based on the detected linear component, detecting a lane marking point corresponding to the lane marking using the generated edge image and the linearly processed edge image, and detecting the lane marking based on the detected lane marking point.

Abstract: Disclosed is a method of providing augmented reality guidance for an aerial vehicle. A method of providing augmented reality guidance for an aerial vehicle includes acquiring an aerial vehicle flight image captured through a camera installed in the aerial vehicle, acquiring a flight route for a flight to a destination of the aerial vehicle, generating an augmented reality (AR) route guidance object corresponding to the flight route, generating an AR route guidance image by mapping the generated AR route guidance object to the aerial vehicle flight image, and displaying the generated AR route guidance image.

Abstract: Disclosed are an aerial vehicle using a hybrid distributed propulsion system and a control method thereof. According to an embodiment of the present specification, an aerial vehicle includes a power generation unit generating electricity by consuming fuel; a battery unit that is charged with power supplied from the power generation unit or supplied with power from the outside; a plurality of fan modules receiving power from the battery unit or the power generation unit to provide lift to the aerial vehicle; a power supply path control unit selecting at least one of the power generation unit and the battery unit to supply power to at least one of the plurality of fan modules; and a route generation unit generating a flight route from a departure point to a destination of the aerial vehicle.

Abstract: Disclosed is a method of displaying an omnidirectional hazard of aerial vehicle. The method includes generating an omnidirectional image based on the aerial vehicle by using a plurality of flight images acquired from each of a plurality of cameras equipped in the aerial vehicle, determining whether a hazard exists within a predetermined range based on a location of the aerial vehicle, classifying whether the hazard is a mobile object or an immobile object when the hazard exists within the predetermined range based on the location of the aerial vehicle, and displaying a risk guidance object in the omnidirectional image based on the classified hazard.

Abstract: Disclosed is a method of assisting landing of an aerial vehicle based on an image. The method includes acquiring an aerial vehicle landing image captured by a camera installed on the aerial vehicle; generating a landing guide object for guiding the landing of the aerial vehicle; generating a landing assistance image obtained by combining the acquired aerial vehicle landing image and the landing guide object; and displaying the generated landing assistance image.

Abstract: A method for measuring an inter-vehicle distance includes acquiring a driving image photographed by a photographing device of a first vehicle which is being driven; detecting a second vehicle from the acquired driving image; detecting first feature points of a second vehicle region in a first frame corresponding to a frame in which the second frame is detected before a frame in which the second vehicle is not detected among a plurality of frames constituting the driving image, when the second vehicle is not detected from the driving image; detecting second feature points in a second frame corresponding to a current frame by tracking the detected first feature points; calculating a feature point change value between the first feature points and the second feature points; and calculating an inter-vehicle distance from the photographing device of the first vehicle to the second vehicle based on the calculated feature point change value.

Abstract: There is provided a method for detecting a lane marking using a processor including acquiring a drive image captured by an image capturing device of a vehicle which is running, detecting an edge corresponding to a lane marking from the acquired drive image and generating an edge image based on the detected edge, detecting a linear component based on the detected edge and generating a linearly processed edge image based on the detected linear component, detecting a lane marking point corresponding to the lane marking using the generated edge image and the linearly processed edge image, and detecting the lane marking based on the detected lane marking point.

Abstract: A method for measuring an inter-vehicle distance includes acquiring a driving image photographed by a photographing device of a first vehicle which is being driven; detecting a second vehicle from the acquired driving image; detecting first feature points of a second vehicle region in a first frame corresponding to a frame in which the second frame is detected before a frame in which the second vehicle is not detected among a plurality of frames constituting the driving image, when the second vehicle is not detected from the driving image; detecting second feature points in a second frame corresponding to a current frame by tracking the detected first feature points; calculating a feature point change value between the first feature points and the second feature points; and calculating an inter-vehicle distance from the photographing device of the first vehicle to the second vehicle based on the calculated feature point change value.

Abstract: Provided is an image processing method. The image processing method includes receiving images acquired from a plurality of vehicles positioned on a road; storing the received images according to acquisition information of the received images; determining a reference image and a target image based on images having the same acquisition information among the stored images; performing an image registration using a plurality of feature points extracted from each of the determined reference image and target image; performing a transparency process for each of the reference image and the target image which are image-registered; extracting static objects from the transparency-processed image; and comparing the extracted static objects with objects on map data which is previously stored and updating the map data when the objects on the map data which is previously stored and the extracted static objects are different from each other.