# Polarized skylight orientation determination artificial neural network

arXiv: https://arxiv.org/abs/2107.02328 · HTML: https://mlsanity.com/paper/2107.02328/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 26 references that are in the map.
- First posted on arXiv 6 Jul 2021.

## Builds on (most cited first, 2 of 2)

- [Beyond One-hot Encoding: lower dimensional target embedding](https://mlsanity.com/paper/1806.10805/) (2018)
- [An end-to-end CNN framework for polarimetric vision tasks based on polarization-parameter-constructing network](https://mlsanity.com/paper/2004.08740/) (2020)

## Abstract (arXiv)

This paper proposes an artificial neural network to determine orientation using polarized skylight. This neural network has specific dilated convolution, which can extract light intensity information of different polarization directions. Then, the degree of polarization (DOP) and angle of polarization (AOP) are directly extracted in the network. In addition, the exponential function encoding of orientation is designed as the network output, which can better reflect the insect's encoding of polarization information, and improve the accuracy of orientation determination. Finally, training and testing were conducted on a public polarized skylight navigation dataset, and the experimental results proved the stability and effectiveness of the network.