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.

翻译：本文提出了一种方法，用于从无人机在植被火灾期间通常拍摄的航拍图像中检测感兴趣的目标，包括车辆、人类和火源。为实现这一目标，我们采用人工神经网络并构建了一个用于监督学习的数据集。通过实施一个结合经典图像处理技术与预训练神经网络的目标检测流程，我们完成了数据集的辅助标注。此外，我们还开发了一个数据增强流程，利用自动标注的图像扩充数据集。最后，我们评估了不同神经网络的性能。