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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 utomatically labeled images. Finally, we evaluate the performance of different neural networks.
The dataset is used for 3D environment modeling, i.e. for the generation of dense 3D point clouds and 3D models with PatchMatch algorithm and neural networks. Difficult for the modeling algorithm are the reflections of rain, water and snow, as well as windows and vehicle surface. In addition, lighting conditions are constantly changing.