Evaluation of the performance of data classification models for aerial imagery under resource constraints - Prystavka, Pylyp; Cholyshkina, Olha; Zivakin, Valerii; Stetsenko, Borys (Technology audit and production reserves, 2025)
The object of the study is the process of aerial imagery data processing under limited computational resources, particularly onboard unmanned aerial vehicles (UAVs) using classification models. One of the most challenging issues is the adaptation of classification models to scale variations and perspective distortions that occur during UAV maneuvers. Additionally, the high computational complexity of traditional methods, such as sliding window approaches, significantly limits their applicability on resource-constrained devices. The study utilized state-of-the-art neural network classifiers, including ResNet50v2, DenseNet121, and MobileNetV2, which were fine-tuned on a specialized aerial imagery dataset. An experimental evaluation of the proposed neural network classifiers was conducted on Raspberry Pi 4 Model B and OrangePi 5 Pro platforms with limited computational power, simulating the constrained resources of UAV systems. To optimize performance, a stripe-based processing approach was proposed for streaming video, ensuring a balance between processing speed and the amount of analyzed data for surveillance applications. Specific execution time evaluations were obtained for different types of classifiers running on single-board computers suitable for UAV deployment. This approach enables real-time aerial imagery processing, significantly enhancing UAV system autonomy. Compared to traditional methods, the proposed solutions offer advantages such as reduced power consumption, accelerated computations, and improved classification accuracy. These results demonstrate high potential for implementation in various fields, including military operations, reconnaissance, search-and-rescue missions, and agricultural technology applications.