| Autor: |
Léo Poughon, Vincent Aubry, Jocelyn Monnoyer, Stéphane Viollet, Julien R Serres |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
BMC Research Notes, Vol 17, Iss 1, Pp 1-8 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1756-0500 |
| DOI: |
10.1186/s13104-024-06959-6 |
| Popis: |
Abstract Objectives Recent advances in bio-inspired navigation have sparked interest in the phenomenon of skylight polarization. This interest stems from the potential of skylight-based orientation sensors, which performance can be simulated using physical models. However, the effectiveness of machine learning algorithms in this domain relies heavily on access to large datasets for training. Although there are several databases of simulated images in literature, there remains a lack of publicly available annotated real-world color polarimetric images of the sky across various weather conditions. Data description We present here a dataset obtained from a long-term experimental setup designed to collect polarimetric images from a stand-alone camera. The setup utilizes a Division-of-Focal-Plane polarization camera equipped with a fisheye lens mounted on a rotative telescope mount. Furthermore, we obtained the sensor’s orientation within the East-North-Up (ENU) frame from a geometrical calibration and an algorithm provided with the database. To facilitate further research in this area, the present sample dataset spanning two months has been made available on a public archive with manual annotations as required by deep learning algorithms. The images were acquired at 10 min intervals and were taken with various exposure times ranging from 33µs to 300ms. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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