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公开(公告)号：US11074450B2
公开(公告)日：2021-07-27
申请号：US16726833
申请日：2019-12-25
Applicant: UBTECH ROBOTICS CORP LTD
Abstract: The present disclosure provides a picture hook identification method as well as an apparatus and a terminal device using the same. The method includes; determining geometric parameter(s) of an identification object based on image(s) collected b a camera and internal parameter(s) of the camera; comparing the geometric parameters of the identification object with geometric parameter(s) of a target picture book; and determining the identification object as the target picture book, if a difference between the geometric parameters of the identification object and the geometric parameters of the target picture book is within a preset range. In this manner, the target picture book is further filtered by matching the geometric parameters, which can reduce misidentification to improve the accuracy of identifying the picture book.
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公开(公告)号：US11373443B2
公开(公告)日：2022-06-28
申请号：US17105667
申请日：2020-11-27
Applicant: UBTECH ROBOTICS CORP LTD
Inventor： Yue Wang , Jun Cheng , Yepeng Liu , Yusheng Zeng , Jianxin Pang , Youjun Xiong
Abstract: The present disclosure provides a method and an apparatus for face recognition and a computer readable storage medium. The method includes: inputting a to-be-recognized blurry face image into a generator of a trained generative adversarial network to obtain a to-be-recognized clear face image; inputting the to-be-recognized clear face image to the feature extraction network to obtain a facial feature of the to-be-recognized clear face image; matching the facial feature of the to-be-recognized clear face image with each user facial feature in a preset facial feature database to determine the user facial feature best matching the to-be-recognized clear face image as a target user facial feature; and determining a user associated with the target user facial feature as a recognition result. Through this solution, the accuracy of the recognition of blurry faces can be improved.
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公开(公告)号：US20210124925A1
公开(公告)日：2021-04-29
申请号：US16726833
申请日：2019-12-25
Applicant: UBTECH ROBOTICS CORP LTD
Abstract: The present disclosure provides a picture hook identification method as well as an apparatus and a terminal device using the same. The method includes; determining geometric parameter(s) of an identification object based on image(s) collected b a camera and internal parameter(s) of the camera; comparing the geometric parameters of the identification object with geometric parameter(s) of a target picture book; and determining the identification object as the target picture book, if a difference between the geometric parameters of the identification object and the geometric parameters of the target picture book is within a preset range. In this manner, the target picture book is further filtered by matching the geometric parameters, which can reduce misidentification to improve the accuracy of identifying the picture book.
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公开(公告)号：US11941844B2
公开(公告)日：2024-03-26
申请号：US17403902
申请日：2021-08-17
Applicant: UBTECH ROBOTICS CORP LTD
Inventor： Yepeng Liu , Yusheng Zeng , Jun Cheng , Jing Gu , Yue Wang , Jianxin Pang
CPC classification number: G06T7/74 , G06N3/04 , G06T7/75 , G06V40/164 , G06T2207/20081 , G06T2207/20084 , G06T2207/30201
Abstract: An object detection model generation method as well as an electronic device and a computer readable storage medium using the same are provided. The method includes: during the iterative training of the to-be-trained object detection model, the detection accuracy of the iteration nodes of the object detection model is sequentially determined according to the node order, and the mis-detected negative samples of the object detection model at the iteration nodes with the detection accuracy less than or equal to a preset threshold are enhanced. Then the object detection model is trained at the iteration node based on the enhanced negative samples and a first amount of preset training samples. After the training at the iteration nodes are completed, it returns to the step of sequentially determining the detection accuracy of the iteration nodes of the object detection model until the training of the object detection model is completed.
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