Mitsubishi Electric Corporation

Tomoya Sawada

Yoshimi Moriya

Akira Minezawa

Kazuyuki Miyazawa

Hideaki Maehara

Ken Fukuchi

Shunichi Sekiguchi

Ryoji Hattori

Hidetoshi Mishima

Mengxiong Wang

Momoyo Hino

Naohiro Shibuya

Momoyo Nagase

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.

Learning device including a machine learning mathematical model

An image signal indicating an inference target image in which a detection target appears is acquired when a domain of the inference target image is different from a domain of a training image or a recognition task of the inference target image is different from a pre-learned task. The image signal is provided to a trained learning model, and from the learning model, an inference time feature amount obtained by combining feature amounts of the detection target in the inference target image after the feature amounts is blurred is acquired. The detection target in the inference target image is recognized on the basis of a representative feature amount that is a registered feature amount of the detection target in an image for conversion in which a domain and a recognition task of the image are the same as those of the inference target image, and the inference time feature amount.

Inference device, inference method, and a non-transitory computer-readable medium

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.

Object detection device, monitoring device, training device, and model generation method

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.

Labeling device and learning device

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

Object detection device and object detection method

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.

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Tokyo, Japan

Tomoya Sawada