UAVs and Neural Networks for search and rescue missions
This work addresses search and rescue missions in fire scenarios, but it is incremental as it applies existing methods to a specific domain.
The paper tackles the problem of detecting objects like cars, humans, and fire in aerial images from UAVs during vegetation fires by using neural networks, achieving performance evaluation of different models.
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.