公开(公告)号：US12183485B2

公开(公告)日：2024-12-31

申请号：US17987203

申请日：2022-11-15

Applicant: North China Electric Power University

Inventor： Xin Wu ,  Tanxin Pi ,  Yawen Yu

IPC: H01B7/32 ,  B64C39/02 ,  G06V10/70 ,  B64U101/30

Abstract: A method and system for detecting a typical object of a transmission line based on UAV federated learning. The method includes: determining a detection model for a typical object of a transmission line by YOLOv3 object detection algorithm according to a prior database for the typical object; dividing a UAV network into multiple federated learning units; acquiring pictures, taken by the UAV network, of the typical object and tags corresponding to each picture to determine a training database; training, based on Horovod framework and FATE federated learning framework, each federated learning unit according to the training database and the detection model for the typical object, and determining the trained UAV network according to the trained federated learning unit; and determining, by the trained UAV network, the typical object in each picture. A congestion of communication links is avoided, thereby improving detection efficiency.

公开(公告)号：US20230238156A1

公开(公告)日：2023-07-27

申请号：US17987203

申请日：2022-11-15

Applicant: North China Electric Power University

Inventor： Xin Wu ,  Tanxin Pi ,  Yawen Yu

IPC: H01B7/32 ,  G06V10/70 ,  B64C39/02

CPC classification number: H01B7/32 ,  G06V10/70 ,  B64C39/024 ,  B64C2201/123 ,  G06V2201/07

Abstract: A method and system for detecting a typical object of a transmission line based on UAV federated learning. The method includes: determining a detection model for a typical object of a transmission line by YOLOv3 object detection algorithm according to a prior database for the typical object; dividing a UAV network into multiple federated learning units; acquiring pictures, taken by the UAV network, of the typical object and tags corresponding to each picture to determine a training database; training, based on Horovod framework and FATE federated learning framework, each federated learning unit according to the training database and the detection model for the typical object, and determining the trained UAV network according to the trained federated learning unit; and determining, by the trained UAV network, the typical object in each picture. A congestion of communication links is avoided, thereby improving detection efficiency.