公开(公告)号：US20250037453A1

公开(公告)日：2025-01-30

申请号：US18226716

申请日：2023-07-26

Applicant: X Development LLC

Inventor： Xin-Jing Wang ,  Anthony Ha ,  Sze Mei Cat Wong ,  Reuben Nahouraii ,  Arthur Robert Pope ,  Om Prakash Ravi

IPC: G06V20/10 ,  G06T7/70 ,  G06V10/774

Abstract: Methods, systems, and apparatus, including computer programs encoded on a storage device, for mapping an electrical grid are disclosed. A method includes sampling multiple locations within a geographic region, executing a detection process for each location, the detection process including applying the set of images for the location as input to a machine learning (ML) model that is trained to identify electrical grid assets depicted within images taken from a combination of different perspectives and obtaining an output from the machine learning model that indicates whether a same electrical grid asset is identified in each of the images of the location. In response to an ML output that indicates a positive identification of the same electrical grid asset being depicted in a particular set of images of a particular location, the method further includes: selecting a number of sublocations within the region, and executing the detection process for each sublocation.

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.