Abstract: A wiring substrate includes a resin insulating layer, a via conductor formed in the resin insulating layer such that the via conductor is penetrating through the resin insulating layer, a conductor pad formed on the resin insulating layer and connected to the via conductor, a coating insulating layer formed on the resin insulating layer such that the coating insulating layer is covering the conductor pad, and a metal post formed on the conductor pad and protruding from the coating insulating layer. The conductor pad is formed such that a central axis of the conductor pad is shifted in a predetermined direction with respect to a central axis of the via conductor, and the metal post is formed such that a central axis of the metal post is shifted in the predetermined direction with respect to the central axis of the conductor pad.

Abstract: An image signal indicating a target image is acquired when a domain of the target image is different from that of a training image or a recognition task of the target image is different from a pre-learned task. The image signal is provided to a trained learning model. An inference time feature amount obtained by combining feature amounts of the detection target after the feature amounts are blurred is acquired from the learning model. A three-dimensional position of the detection target is estimated on the basis of a representative feature amount being a registered feature amount of the detection target appearing in an image for conversion whose domain and recognition task of the image are the same as those of the target image, and the inference time feature amount. A temporal positional change of the detection target in the target image is analyzed on the basis of the estimated position.

Abstract: An object detection device includes an image data acquiring unit that acquires image data indicating an image captured by a camera, a first feature amount extracting unit that generates a first feature map using the image data, a second feature amount extracting unit that generates a second feature map using the image data, and generates a third feature map by performing addition or multiplication of the second feature map using the first feature map and weighting the second feature map, and the object detection unit that detects an object in the captured image using the third feature map. A first feature amount in the first feature map uses a mid-level feature corresponding to objectness, and a second feature amount in the second feature map uses a high-level feature.

Abstract: A learning device according to the present disclosed technology is a learning device including a coupled mathematical model capable of machine learning and learning a data set of a target domain from a data set of an original domain for a teacher, in which a pre-stage part of the coupled mathematical model generates a plurality of low-level feature maps from input image data, compares the low-level feature maps of data sets belonging to the same type of learning target for the original domain and the target domain in the image data, and calculates domain-shared features, and calculates domain relaxation learning information for each space of {1} color, {2} illumination, {3} low frequency component, and {4} high frequency component among the domain-shared features.

Abstract: A labeling device includes: an image-signal acquisition unit that acquires an image signal indicating an image captured by a camera; an image recognition unit that has learned by machine learning and performs image recognition on the captured image; and a learning-data-set generation unit that generates, by performing labeling on each object included in the captured image on the basis of a result of image recognition, a learning data set including image data corresponding to each object and label data corresponding to each object.

Abstract: An image feature map generating unit (3) generates, on the basis of feature amounts extracted from a plurality of images successively captured by a camera (109), an image feature map which is an estimated distribution of the object likelihood on each of the images. An object detecting unit (4) detects an object on the basis of the image feature map generated by the image feature map generating unit (3).

Abstract: An object detection device includes: an optical flow calculator to calculate an optical flow between images captured by the image capturer at different times; an evaluation value calculator to divide the image captured by the image capturer into areas, and calculate, for each divided area, an evaluation value by using the optical flows of pixels belonging to the divided area, the evaluation value indicating a measure of a possibility that the divided area is an object area representing part or whole of the object to be detected; and an area determinator to determine an area in an image, in which the object to be detected exists, by comparing the evaluation value of each divided area calculated by the evaluation value calculator with a threshold value.

Abstract: An information processing unit (32) collects accident information from a vehicle-mounted device (2) existing in a periphery of a site of an accident indicated by accident information stored in a storage unit (31), by controlling a communication unit (30).

Abstract: An image feature map generating unit (3) generates, on the basis of feature amounts extracted from a plurality of images successively captured by a camera (109), an image feature map which is an estimated distribution of the object likelihood on each of the images. An object detecting unit (4) detects an object on the basis of the image feature map generated by the image feature map generating unit (3).

Abstract: An object detection device includes: an optical flow calculator to calculate an optical flow between images captured by the image capturer at different times; an evaluation value calculator to divide the image captured by the image capturer into areas, and calculate, for each divided area, an evaluation value by using the optical flows of pixels belonging to the divided area, the evaluation value indicating a measure of a possibility that the divided area is an object area representing part or whole of the object to be detected; and an area determinator to determine an area in an image, in which the object to be detected exists, by comparing the evaluation value of each divided area calculated by the evaluation value calculator with a threshold value.