Nec Corporation

Asuka Ishii

Gaku Nakano

Katsuhiko Takahashi

Tetsuo Inoshita

Yuichi Nakatani

Masahiko Ohta

Kazuhito Murata

In an object detection device, the plurality of object detection units output a score indicating a probability that a predetermined object exists for each partial region set with respect to inputted image data. The weight computation unit uses weight computation parameters to compute a weight for each of the plurality of object detection units on a basis of the image data and outputs of the plurality of object detection units, the weight being used when the scores outputted by the plurality of object detection units are merged. The merging unit merges the scores outputted by the plurality of object detection units for each partial region according to the weights computed by the weight computation unit. The first loss computation unit computes a difference between a ground truth label of the image data and the score merged by the merging unit as a first loss. Then, the first parameter correction unit corrects the weight computation parameters so as to reduce the first loss.

Object detection device, learned model generation method, and recording medium

The information processing device performs distillation learning of a student model using unknown data which a teacher model has not learned. The label distribution determination unit outputs an arbitrary label for the unknown data. The data generation unit outputs new generated data using an arbitrary label and unknown data as inputs. The distillation learning part performs distillation learning of the student model using the teacher model and using the generated data as an input.

Information processing device, learning method, and recording medium

The learning device includes: a first estimation unit configured to perform estimation using a temperature parameter, based on a student model; a second estimation unit configured to perform estimation using the temperature parameter, based on a teacher model; and a temperature calculation unit configured to calculate the temperature parameter, based on estimation information generated by the first estimation unit and the second estimation unit.

Learning device, learning method and program

Teacher and student models output inference results for training data. A loss calculation unit calculates a total loss using at least one of (1) a loss obtained by multiplying a difference between a true value and a student model output by a weight increasing as a confidence of the teacher model output is lower, (2) a loss obtained by multiplying a difference between the true value and the student model output by a weight increasing as a difference between the true value and the teacher model output is greater, and (3) a loss obtained by multiplying a difference between the teacher and student model outputs by weights increasing as the difference between the teacher and student model outputs is greater and increasing as the difference between the true value and the teacher model output is smaller. An update part updates parameters of the student model based on the total loss.

Learning apparatus, learning method, and recording medium

In an object detection device, a plurality of object detection units output a score indicating probability that a predetermined object exists, for each partial region set to image data inputted. The weight computation unit computes weights for merging the scores outputted by the plurality of object detection units, using weight calculation parameters, based on the image data. The merging unit merges the scores outputted by the plurality of object detection units, for each partial region, with the weights computed by the weight computation unit. The target model object detection unit configured to output a score indicating probability that the predetermined object exists, for each partial region set to the image data. The first loss computation unit computes a first loss indicating a difference of the score of the target model object detection unit from a ground truth label of the image data and the score merged by the merging unit. The first parameter correction unit corrects parameters of the target model object detection unit to reduce the first loss.

Object detection device, learning method, and recording medium

In the object detection device, the plurality of object detection units output a score indicating a probability that a predetermined object exists, for each partial region set with respect to image data inputted. The weight computation unit computes a weight for each of the plurality of object detection units by using weight computation parameters and based on the image data. The weights are used when the scores outputted by the plurality of object detection units are merged. The weight redistribution unit changes the weight for a predetermined object detection unit, among the weights computed by the weight computation unit, to 0 and output the weights. The merging unit merges the scores outputted by the plurality of object detection units for each of the partial regions, by using the weights computed by the weight computation unit and including the weight changed by the weight redistribution unit. The loss computation unit computes a difference between a ground truth label of the image data and the merged score merged by the merging unit as a loss. Then, the parameter correction unit corrects the weight computation parameters so as to reduce the loss.

An apparatus includes: a deflection acquiring unit that acquires a deflection amount caused in a measurement target region on a structure by a vehicle traveling on the structure; a vehicle position acquiring unit that acquires a position of the vehicle on the structure at time when the deflection amount is acquired; and a rigidity coefficient calculating unit that calculates a rigidity coefficient specifying a relation between a magnitude of a force applied to the structure due to a weight of the vehicle and flexural rigidity of the structure, from a relation equation, the acquired deflection amount, and the detected position of the vehicle. The relation equation is established among a position of the vehicle on the structure, the magnitude of the force, a position of the measurement target region on the structure, a length of the structure, the flexural rigidity, and a deflection amount caused in the measurement target region.

Structure deflection measurement apparatus

A vibration measurement apparatusincludes a detection unitthat acquires, as a pattern image from an image capturing apparatusthat shoots a measurement target surface of a structure, an image of the measurement target surface onto which pattern light is projected by an optical apparatus, and detects the pattern light from the pattern image, an estimation unitthat estimates an angle between the normal of the image capturing surface and the normal of the measurement target surface, based on the pattern light, an image conversion unitthat converts the shot image into an image that would be obtained were the normal of the measurement target surface coincident with the normal of the image capturing surface of the image capturing apparatus, using the estimated angle, and a vibration measurement unitthat measures the vibration of the structureusing the converted image.

Vibration measurement system, vibration measurement apparatus, vibration measurement method, and computer-readable recording medium

A vibration measurement system comprises an image capturing apparatus, a distance measuring apparatus, a sensor that outputs a signal according to an inclination of the image capturing apparatus relative to the vertical direction, and a vibration measurement apparatus. The vibration measurement apparatus includes calculating an angle formed by the normal of an image capturing surface of the image capturing apparatus and the normal of the measurement target surface that the image capturing apparatus shoots, based on the signal output by the sensor, converting the image obtained that the image capturing apparatus shoots into an image that would be obtained were the normal of the measurement target surface coincident with the normal of the image capturing surface of the image capturing apparatus, using the calculated angle, and measuring vibration of the structure, using the converted image and the measured distance from the image capturing apparatus to the measurement target surface.

Growing community of inventors

Tokyo, Japan

Asuka Ishii