Learning method and learning device for adjusting parameters of CNN by using multi-scale feature maps and testing method and testing device using the same
US10007865B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Oct 16, 2017 |
| Grant date | Jun 26, 2018 |
| Priority date | — |
| Expiry date | Oct 16, 2037 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V20/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A learning method for acquiring a bounding box corresponding to an object in a training image from multi-scaled feature maps by using a CNN is provided. The learning method includes steps of: (a) allowing an N-way RPN to acquire at least two specific feature maps and allowing the N-way RPN to apply certain operations to the at least two specific feature maps; (b) allowing an N-way pooling layer to generate multiple pooled feature maps by applying pooling operations to respective areas on the at least two specific feature maps; and (c) (i) allowing a FC layer to acquire information on pixel data of the bounding box, and (ii) allowing a loss layer to acquire first comparative data, thereby adjusting at least one of parameters of the CNN by using the first comparative data during a backpropagation process.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.