System and method for improved general object detection using neural networks
US10628701B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 10, 2018 |
| Grant date | Apr 21, 2020 |
| Priority date | — |
| Expiry date | Oct 18, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/20084
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
In general, certain embodiments of the present disclosure provide methods and systems for object detection by a neural network comprising a convolution-nonlinearity step and a recurrent step. In a training mode, a dataset is passed into the neural network, and the neural network is trained to accurately output a box size and a center location of an object of interest. The box size corresponds to the smallest possible bounding box around the object of interest and the center location corresponds to the location of the center of the bounding box. In an inference mode, an image that is not part of the dataset is passed into the neural network. The neural network automatically identifies an object of interest and draws a box around the identified object of interest. The box drawn around the identified object of interest corresponds to the smallest possible bounding box around the object of interest.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.