公开(公告)号：US20210291980A1

公开(公告)日：2021-09-23

申请号：US17203735

申请日：2021-03-16

Applicant: Coretronic Corporation

Inventor： Yi-Fan Liou ,  Su-Yun Yu ,  Kui-Ting Chen

IPC: B64C39/02 ,  G06K9/00 ,  G06K9/62

Abstract: The invention relates to an unmanned aerial vehicle (UAV) and an image recognition method applied to a UAV. The image recognition method includes: obtaining an image data stream, wherein the image data frame includes a current frame; performing image recognition on an object in the current frame to generate a first box corresponding to the current frame; detecting movement of the object to generate a second box corresponding to the current frame; and determining the object as a tracking target according to the first box and the second box. A moving object can be accurately identified, detected, and tracked according to the UAV and the image recognition method provided in embodiments of the invention.

公开(公告)号：US20220171994A1

公开(公告)日：2022-06-02

申请号：US17513896

申请日：2021-10-29

Applicant: Coretronic Corporation

Inventor： Ching-Wen Cheng ,  Yen-Chun Huang ,  Yi-Fan Liou ,  Kui-Ting Chen

IPC: G06K9/62 ,  G06K9/34 ,  G06K9/20 ,  G06K9/46

Abstract: The invention provides a method of generating an image recognition model and an electronic device using the method. The method includes the following. A source image is obtained; a first image is cut out of a first region of the source image to generate a cut source image; a preliminary image recognition model is pre-trained according to feature data and label data, in which the feature data is associated with the cut source image, and the label data is associated with the first image; and the pre-trained preliminary image recognition model is fine-tuned to generate the image recognition model. The method of generating the image recognition model and the electronic device provided by the invention may correctly restore an input image.

公开(公告)号：US11893083B2

公开(公告)日：2024-02-06

申请号：US17467453

申请日：2021-09-07

Applicant: Coretronic Corporation

Inventor： Yi-Fan Liou ,  Yen-Chun Huang

IPC: G06F18/214 ,  G06N3/04

CPC classification number: G06F18/214 ,  G06N3/04

Abstract: An electronic device and a method for training or applying a neural network model are provided. The method includes the following steps. An input data is received. Convolution is performed on the input data to generate a high-frequency feature map and a low-frequency feature map. One of upsampling and downsampling is performed to match a first size of the high-frequency feature map and a second size of the low-frequency feature map. The high-frequency feature map and the low-frequency feature map are concatenated to generate a concatenated data. The concatenated data is inputted to an output layer of the neural network model.

公开(公告)号：US20220129694A1

公开(公告)日：2022-04-28

申请号：US17499884

申请日：2021-10-13

Applicant: Coretronic Corporation

Inventor： Yi-Fan Liou ,  Hsin-Ya Liang ,  Kai-Cheng Hu

IPC: G06K9/62 ,  G06F17/16 ,  G06N3/02

Abstract: An electronic device and a method for screening a sample are provided. The method includes the following steps. N samples corresponding to a first object are received, in which the N samples include a first sample. N similarity vectors respectively corresponding to the N samples are calculated, in which the N similarity vectors include a first similarity vector corresponding to the first sample. The first similarity vector includes multiple first similarities between the first sample and each of the N samples except the first sample. The first sample is determined to be a representative sample of the first object in response to an average value of the first similarities of the first similarity vector being the maximum value among average values of N similarities respectively corresponding to the N similarity vectors.

公开(公告)号：US11875560B2

公开(公告)日：2024-01-16

申请号：US17203735

申请日：2021-03-16

Applicant: Coretronic Corporation

Inventor： Yi-Fan Liou ,  Su-Yun Yu ,  Kui-Ting Chen

IPC: B64C39/02 ,  G06V20/64 ,  G06F18/213 ,  G06V10/25 ,  G06V20/17 ,  B64U101/30

CPC classification number: G06V20/17 ,  B64C39/024 ,  G06F18/213 ,  G06V10/25 ,  G06V20/64 ,  B64U2101/30

Abstract: The image recognition method includes: obtaining an image data stream, wherein the image data frame includes a current frame; performing image recognition on an object in the current frame to generate a first box corresponding to the current frame; detecting movement of the object to generate a second box corresponding to the current frame; and determining the object as a tracking target according to the first box and the second box.

公开(公告)号：US20220092350A1

公开(公告)日：2022-03-24

申请号：US17467453

申请日：2021-09-07

Applicant: Coretronic Corporation

Inventor： Yi-Fan Liou ,  Yen-Chun Huang

IPC: G06K9/62 ,  G06N3/04

Abstract: An electronic device and a method for training or applying a neural network model are provided. The method includes the following steps. An input data is received. Convolution is performed on the input data to generate a high-frequency feature map and a low-frequency feature map. One of upsampling and downsampling is performed to match a first size of the high-frequency feature map and a second size of the low-frequency feature map. The high-frequency feature map and the low-frequency feature map are concatenated to generate a concatenated data. The concatenated data is inputted to an output layer of the neural network model.

公开(公告)号：US20210192286A1

公开(公告)日：2021-06-24

申请号：US17126054

申请日：2020-12-18

Applicant: Coretronic Corporation

Inventor： Yi-Fan Liou ,  Po-Yen Tseng

IPC: G06K9/62 ,  G06N3/04 ,  G06T5/20

Abstract: A model training method and an electronic device are provided. The method includes: obtaining a first image; masking at least one region in the first image to obtain a masked image; inputting the masked image to a first model to obtain a first generated image; training the first model according to the first generated image and the first image; training a second model according to the first generated image and the first image; and when the first model is trained to a first condition and the second model is trained to a second condition, completing the training for the first model. By means of the model training method and the electronic device, the problem brought by a manually marked image can be resolved and the problem of causing mode collapse can be effectively avoided.