Unified deep convolutional neural net for free-space estimation, object detection and object pose estimation
US10474908B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jul 6, 2017 |
| Grant date | Nov 12, 2019 |
| Priority date | — |
| Expiry date | Jan 26, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/30261
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A method in a vehicle for performing multiple on-board sensing tasks concurrently in the same network using deep learning algorithms is provided. The method includes receiving vision sensor data from a sensor on the vehicle, determining a set of features from the vision sensor data using a plurality of feature layers in a convolutional neural network, and concurrently estimating, using the convolutional neural network, bounding boxes for detected objects, free-space boundaries, and object poses for detected objects from the set of features determined by the plurality of feature layers. The neural network may include a plurality of free-space estimation layers configured to determine the boundaries of free-space in the vision sensor data, a plurality of object detection layers configured to detect objects in the image and to estimate bounding boxes that surround the detected objects, and a plurality of object pose detection layers configured to estimate the direction of each object.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.