公开(公告)号：US20240257541A1

公开(公告)日：2024-08-01

申请号：US18160841

申请日：2023-01-27

Applicant: X Development LLC

Inventor： Xinyue Li ,  Kshitij Naresh Nikhal ,  Kari Anne Klein ,  Ananya Gupta

IPC: G06V20/64 ,  G06V10/75 ,  G06V10/80 ,  G06V10/82 ,  G06V20/00

CPC classification number: G06V20/647 ,  G06V10/757 ,  G06V10/809 ,  G06V10/82 ,  G06V20/38

Abstract: Methods, systems, and apparatus, including computer programs encoded on a storage device, for identifying characteristics of electric grid assets are disclosed. A method includes obtaining a plurality of images, each image depicting at least one utility pole of an electric grid; detecting, in each of the plurality of images, keypoints of the at least one utility pole depicted in the image; determining, using the keypoints from at least two images of a particular utility pole, at least one measurement of the particular utility pole; determining, using the measurement of the particular utility pole, an electrical characteristic of an asset supported by the particular utility pole; and providing the electrical characteristic as an output. The two or more images include images of the particular utility pole captured from multiple different camera perspectives. The asset can include a capacitor, a transformer, a switch, a power line.

公开(公告)号：US20230237644A1

公开(公告)日：2023-07-27

申请号：US18160276

申请日：2023-01-26

Applicant: X Development LLC

Inventor： Ananya Gupta ,  Phillip Ellsworth Stahlfeld ,  Kshitij Naresh Nikhal ,  Om Prakash Ravi ,  Aviva Cheryl Shwaid ,  Arthur Robert Pope ,  Xinyue Li

IPC: G06T7/00 ,  G06V10/82 ,  G06T7/11

CPC classification number: G06T7/001 ,  G06V10/82 ,  G06T7/11 ,  G06T2207/20084 ,  G06T2207/20081 ,  G06T2207/20132

Abstract: Methods, computer systems, and apparatus, including computer programs encoded on computer storage media, for training a classification neural network. The system generates, from a set of object-specific data, one or more meta-learning datasets for one or more respective initial training tasks. The system determines values for a set of meta parameters by performing meta-learning with a classification neural network on the one or more meta-learning datasets. The system obtains a set of labeled training examples for a characteristic-detection task. The system determines based at least on one of the values for the set of meta parameters and using the set of labeled training examples, target values for the network parameters for the classification neural network to perform the characteristic-detection task.