UAeroNet: domain-specific dataset for automation of unmanned aerial vehicles
DOI:
https://doi.org/10.20535/2786-8729.7.2025.341779Keywords:
unmanned aerial vehicles, UAeroNet, object detection, autonomous navigation, computer visionAbstract
This paper addresses the challenges and key principles of designing domain-specific datasets that can be used especially for automation of unmanned aerial vehicles. Such datasets play a key role in building intelligent systems that enable autonomous operation and support data-driven decisions. The study presents approaches we used for data collection, analysis and annotation, highlighting their importance and practical impact on real-world application. The preparation of a domain-specific dataset for automating unmanned aerial vehicles operations (such as navigation and environmental monitoring) is a challenging task due to frequently low image resolution, complex weather conditions, a wide range of object scales, background noise and heterogeneous terrain landscapes. Existing open datasets typically cover only a limited variety of unmanned aerial vehicles use cases, which restricts the ability of deep learning models to perform adequately under non-standard or unpredictable conditions.
The object of the study is video data acquired by unmanned aerial vehicles for creating domain-specific datasets that enable machine learning models to perform autonomous object recognition, navigation, obstacle avoidance and interaction with an environment with minimal operator involvement. The subject focuses on the collection, preparation and annotation of video data acquired by unmanned aerial vehicles. The purpose of the study is to develop and systematize workflow for creating specialized datasets to train robust models capable of autonomously recognizing objects in real-time video captured by unmanned aerial vehicles. To achieve this goal, a workflow was designed for collecting and annotating video data, raw video data were acquired from unmanned aerial vehicles sensors and manually annotated using the Computer Vision Annotation Tool.
As a result of this work, we developed a domain-specific dataset (UAeroNet) using an open-source annotation tool for object tracking task in real scenarios. UAeroNet consists of 456 annotated tracks and a total of 131 525 labeled instances that belong to 13 distinct classes.
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