公开(公告)号：US20250166374A1

公开(公告)日：2025-05-22

申请号：US18764552

申请日：2024-07-05

Applicant: ELECTRONICS AND TELECOMMUNICATIONS RESEARCH INSTITUTE

Inventor： Junyong Park ,  Yong Hyuk Moon

IPC: G06V20/10 ,  G06T7/50 ,  G06T17/00 ,  G06V10/764

Abstract: A method of extracting characteristics of smart farm corps includes a step of extracting depth information about a crop object by using an extraction module, based on a depth image and an RGB image, and extracting object information about the crop object, based on the RGB image, a step of extracting space characteristic information representing a shape, a size, and a direction of the crop object in a 3D space by using a space characteristic extraction module, based on the depth information and the object information, a step of reconstructing a 3D model of the crop object in the 3D space by using a 3D model reconstruction module, based on the space characteristic information, and a step of inferring volume information and pose information about the crop object by using an inference module, based on the reconstructed 3D model.

公开(公告)号：US20240119282A1

公开(公告)日：2024-04-11

申请号：US18356415

申请日：2023-07-21

Applicant: Electronics and Telecommunications Research Institute

Inventor： Jongryul LEE ,  Yong Hyuk Moon ,  Junyong Park

IPC: G06N3/08

CPC classification number: G06N3/08

Abstract: The present disclosure relates to a method and apparatus for searching for a light-weight model through the replacement of a subnetwork of a trained neural network model. The method of searching for a light-weight model includes a preprocessing step of extracting a subnetwork from an original neural network model, constructing a mapping relation between the subnetwork and an alternative block corresponding to the subnetwork by extracting the alternative block from a pre-trained neural network model, and generating profiling information including performance information relating to the subnetwork and the alternative block, and a query processing step of receiving a query, extracting a constraint that is included in the query through query parsing, and generating the final model based on the constraint, the original neural network model, the alternative block, the mapping relation, and the profiling information.