Object detection and recognition apparatus based on CNN based integrated circuits
US10387740B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | May 19, 2018 |
| Grant date | Aug 20, 2019 |
| Priority date | — |
| Expiry date | May 19, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V40/168
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A deep learning object detection and recognition system contains a number of cellular neural networks (CNN) based integrated circuits (ICs) operatively coupling together via the network bus. The system is configured for detecting and then recognizing one or more objects out of a two-dimensional (2-D) imagery data. The 2-D imagery data is divided into N set of distinct sub-regions in accordance with respective N partition schemes. CNN based ICs are dynamically allocated for extracting features out of each sub-region for detecting and then recognizing an object potentially contained therein. Any two of the N sets of sub-regions overlap each other. N is a positive integer. Object detection is achieved with a two-category classification using a deep learning model based on approximated fully-connected layers, while object recognition is performed using a local database storing feature vectors of known objects.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.