# UAVs and Neural Networks for search and rescue missions

arXiv: https://arxiv.org/abs/2310.05512 · HTML: https://mlsanity.com/paper/2310.05512/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 17 references that are in the map.
- First posted on arXiv 9 Oct 2023.

## Builds on (most cited first, 3 of 3)

- [Deep Learning vs. Traditional Computer Vision](https://mlsanity.com/paper/1910.13796/) (2019)
- [Experimental Exploration of Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire Detection](https://mlsanity.com/paper/1911.09010/) (2019)
- [Deployment of Aerial Robots during the Flood Disaster in Erftstadt / Blessem in July 2021](https://mlsanity.com/paper/2209.03084/) (2022)

## Abstract (arXiv)

In this paper, we present a method for detecting objects of interest, including cars, humans, and fire, in aerial images captured by unmanned aerial vehicles (UAVs) usually during vegetation fires. To achieve this, we use artificial neural networks and create a dataset for supervised learning. We accomplish the assisted labeling of the dataset through the implementation of an object detection pipeline that combines classic image processing techniques with pretrained neural networks. In addition, we develop a data augmentation pipeline to augment the dataset with automatically labeled images. Finally, we evaluate the performance of different neural networks.