| Autor: |
Bittner, D, Ferreira, JF, Andrada, ME, Bird, JJ, Portugal, D |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Zdroj: |
Innovation in Forestry Robotics: Research and Industry Adoption Workshop-IEEE Conference on Robotics and Automation (ICRA 2022) |
| Popis: |
In this paper, we introduce a GAN-based solution for generating synthetic multispectral images from fully-annotated RGB images for data augmentation purposes in forestry robotics applications at ground-level. Fully-annotated multispectral datasets are difficult to obtain with sufficient training samples when compared to RGB-based datasets, since annotation in this case is often very time-consuming and expensive due to the need for expert knowledge. In this text, a study comparing different GAN-based image translation models designed for data augmentation is presented. Synthetic images generated by the proposed solution are shown to be realistic enough to yield performance ratings comparable to what is obtained using real images. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
|
