Danger ranking using end to end deep neural network
US11281941B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 7, 2018 |
| Grant date | Mar 22, 2022 |
| Priority date | — |
| Expiry date | Jan 14, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG01C21/3667
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A danger ranking training method comprising training a first deep neural network for generic object recognition within generic images, training a second deep neural network for specific object recognition within images of a specific application, training a third deep neural network for specific scene flow prediction within image sequences of the application, training a fourth deep neural network for potential danger areas localization within images or image sequences of the application using at least one human trained danger tagging method, training a fifth deep neural network for non-visible specific object anticipation and/or visible specific object prediction within image or image sequences of the application, and determining at least one danger pixel within an image or an image sequence of the application using an end-to-end deep neural network as a sequence of transfer learning of the five deep neural networks followed by one or several end-to-end top layers.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.