Method and system for transfer learning based object detection
US12288387B2 · kind B2 · utility
Assignee
Inventor
Key dates
| Filing date | Mar 8, 2021 |
| Grant date | Apr 29, 2025 |
| Priority date | — |
| Expiry date | Oct 11, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V10/774
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Image analysis is a vital field since images can provide contextual, environmental, and emotional factors. Conventional methods are facing challenges in analyzing an image accurately when the image is having lesser data or if the image is having less resolution. Conventional machine learning architectures are computationally intensive when run on high power computing devices for training and inference. The present disclosure provides a robust deep learning model to inference in any given environmental condition. Initially, image data is generated using a pre-trained Generative Adversarial Network (GAN). The GAN receives a plurality of images of varying domain and generates image data. The image data is annotated and segmented to obtain a contextual label map. The contextual label map is given as input to a pre-trained transfer learning model to obtain a plurality of image attributes including number of objects and activity performed by each object.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.