Meta-analysis of Pareto-optimal object detection models for unmanned aerial vehicles on the VisDrone2019-DET dataset
DOI:
https://doi.org/10.17721/1812-5409.2026/1.33Keywords:
VisDrone2019, object detection, deep learning models, Pareto analysis, unmanned aerial vehiclesAbstract
The subject of this research is modern deep learning models for object detection from unmanned aerial vehicles (UAVs), trained and evaluated on the benchmark VisDrone2019-DET dataset. The problem addressed was the lack of a systematic analysis of existing models based on the criterion of optimal balance between detection accuracy, computational speed, and model size, all of which are critical for real-time operation on UAV onboard computers with limited resources. The core result lies in a comprehensive meta-analysis of over 60 scientific publications and the identification of two groups of Pareto-optimal models: one focused on minimizing model parameters and the other on minimizing computational complexity. Through multi-criteria Pareto analysis and the creation of the most complete performance summary table to date, the study identified architectures that offer the best trade-offs between accuracy, speed, and size. These turned out to be optimized versions of YOLO and transformer-based detectors like DETR, a result explained by the efficiency of their core architectures and further tuning for the specifics of aerial imagery. Using the Hailo 8L accelerator as a case study, the research also demonstrated that a model’s computational complexity is directly proportional (with a coefficient of determination up to 0.98) to its execution speed on the typical neural accelerator that is used in a UAV companion computer. This allows engineers to estimate relative runtime based on the number of operations, avoiding time-consuming benchmarking. Overall, the findings can be used by engineers to make informed choices about detector architectures depending on specific UAV hardware constraints, and by researchers as reference points for developing new models for autonomous UAV operation in aerial monitoring, infrastructure inspection, and search-and-rescue missions.
Pages of the article in the issue: 255 - 264
Language of the article: English
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