Abstract: A parking assistance device performs a process for parking a vehicle in a parking area based on a captured image obtained by an imaging device mounted on the vehicle, and includes a storage part that stores the captured image; a first processing part that detects parking areas present in an imaging area of the imaging device, based on a low-resolution image obtained by reducing the resolution of the captured image; a selecting part that selects a target parking area from the detected parking areas when the parking areas have been detected by the first processing part; an extracting part that extracts an extraction image including the target parking area from the captured image stored in the storage part; and a second processing part that generates, based on the extraction image, target information indicating a target stop location used upon parking the vehicle in the target parking area.

Abstract: A periphery monitoring device includes: an acquisition unit that acquires capture images which are captured by an imaging unit in time series when a vehicle is moving, the imaging unit being provided in the vehicle and capable of imaging surroundings of the vehicle; and a restoration processing unit that, in a case where dirt is present in a latest capture image among the capture images, inputs the capture images into a first learned model and generates a first restoration image as a restoration image obtained by restoring a region concealed with the dirt in the latest capture image, the first learned model being a result obtained by machine learning a relationship between a first learning image in which learning dirt is not present and first learning dirt images each of which is made by causing the learning dirt to be present in the first learning image.

Abstract: A specific area detection device that detects a specific area in an imaging area based on a captured image includes: an estimation unit configured to estimate a plurality of points and a direction of a straight line connecting two predetermined points among the plurality of points from a captured image using a learning model created by learning about a specific area defined by a predetermined number of the points in an imaging area using a captured image for learning; and a detection unit configured to detect the specific area by classifying the plurality of points for each specific area based on the direction.

Abstract: An image restoration device includes an acquisition processing unit configured to acquire a captured image obtained by an imaging unit capturing surroundings of a vehicle, and a restoration processing unit. In a case where the captured image includes a stained area caused by a stain of an optical system of the imaging unit, the restoration processing unit is configured to output a restored image corresponding to the captured image acquired by the acquisition processing unit based on a restoration model pre-trained by a machine learning method to output the restored image serving as the captured image in response to an input of the captured image, the restored image in which the stained area is reduced in a different degree in accordance with a position of the stained area in the captured image.

Abstract: A periphery monitoring device includes: an image obtaining unit configured to obtain a captured image captured by an image capturing unit provided in a vehicle so as to image an area including a road surface on periphery of the vehicle; and a restoration control unit configured to control, when the captured image includes a spotted area caused by a spot on an optical system of the image capturing unit, whether to execute restoration processing of outputting a restored image restored from the captured image so as to simulatively reproduce a state where the optical system of the image capturing unit does not have the spot by removing the spotted area, according to a positional relationship between a road surface area where the road surface is captured and the spotted area in the captured image.

Abstract: A specific area detection device that detects a specific area in an imaging area based on a captured image includes: an estimation unit configured to estimate a plurality of points and a direction of a straight line connecting two predetermined points among the plurality of points from a captured image using a learning model created by learning about a specific area defined by a predetermined number of the points in an imaging area using a captured image for learning; and a detection unit configured to detect the specific area by classifying the plurality of points for each specific area based on the direction.

Abstract: A machine learning apparatus includes: an estimating unit estimating, for each of classes into which an element is classified, a likelihood indicating a probability of being classified into the class for an element contained in learning data based on a learning model; a loss value calculation unit calculating a loss value indicating a degree of error of the likelihood based on the likelihood for each class estimated by the estimating unit and a loss function; a weight calculation unit calculating a weight based on a comparison between a first likelihood for a first class to which the element is to be classified as true and a second likelihood for another class to which the element is not to be classified as true among the likelihoods calculated for the classes; and a machine learning unit causing the learning model to perform machine learning based on the loss value and the weight.

Abstract: An awakening support apparatus includes an acquisition portion acquiring surroundings information which indicates information of at least one of a temperature and a vibration around a driver of a movable object, a determination portion determining a drowsiness inducing level of the driver based on the surroundings information acquired by the acquisition portion, and an execution portion executing an awakening support for decreasing a possibility of induction of drowsiness of the driver based on a determination result of the drowsiness inducing level determined by the determination portion.

Abstract: A periphery monitoring device includes: an acquisition unit that acquires capture images which are captured by an imaging unit in time series when a vehicle is moving, the imaging unit being provided in the vehicle and capable of imaging surroundings of the vehicle; and a restoration processing unit that, in a case where dirt is present in a latest capture image among the capture images, inputs the capture images into a first learned model and generates a first restoration image as a restoration image obtained by restoring a region concealed with the dirt in the latest capture image, the first learned model being a result obtained by machine learning a relationship between a first learning image in which learning dirt is not present and first learning dirt images each of which is made by causing the learning dirt to be present in the first learning image.

Abstract: An awakening support apparatus includes an acquisition portion acquiring surroundings information which indicates information of at least one of a temperature and a vibration around a driver of a movable object, a determination portion determining a drowsiness inducing level of the driver based on the surroundings information acquired by the acquisition portion, and an execution portion executing an awakening support for decreasing a possibility of induction of drowsiness of the driver based on a determination result of the drowsiness inducing level determined by the determination portion.