4461 2023 2023 eng 1 8 8 conferencepaper 0 2023-10-09 2023-09-26 -- 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. urn:nbn:de:hbz:1010-opus4-44617 Creative Commons - Namensnennung - Keine Bearbeitung Hartmut Surmann Artur Leinweber Gerhard Senkowski Julien Meine Dominik Slomma Datenverarbeitung; Informatik Informatik und Kommunikation Westfälische Hochschule Gelsenkirchen Bocholt Recklinghausen https://opus4.kobv.de/opus4-w-hs/files/4461/firedetector.pdf