OBJECT DETECTION DEVICE, LEARNING METHOD, AND RECORDING MEDIUM

    公开(公告)号:US20220277553A1

    公开(公告)日:2022-09-01

    申请号:US17624913

    申请日:2019-07-11

    Abstract: 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, OBJECT DETECTION SYSTEM, OBJECT DETECTION METHOD, AND RECORDING MEDIUM HAVING PROGRAM RECORDED THEREON

    公开(公告)号:US20210201007A1

    公开(公告)日:2021-07-01

    申请号:US17058796

    申请日:2018-05-29

    Abstract: The purpose of the present invention is to detect an object in images accurately by means of image recognition without using a special device for removing the influence of the parallax between a plurality of images. An image transformation unit (401) transforms a plurality of images acquired by an image acquisition unit (407). A reliability level calculation unit (402) calculates a level of reliability representing how small the misalignment between images is. A score calculation unit (405) calculates a total score taking into account both an object detection score based on a feature quantity calculated by a feature extraction unit (404), and the level of reliability calculated by the reliability level calculation unit (402). An object detection unit (406) detects an object in the images on the basis of the total score.

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