UAVs and Neural Networks for search and rescue missions
Hartmut Surmann Artur Leinweber Gerhard Senkowski Julien Meine Dominik Slomma
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.