Analysis of modern computer vision methods for monitoring and identifying flying objects: challenges and prospects
DOI:
https://doi.org/10.18372/2073-4751.86.21283Keywords:
Computer vision, airspace monitoring, neural networks, deep learning, flying object identificationAbstract
This review article presents a comprehensive analysis of modern software and algorithmic computer vision methods used for monitoring and identifying flying objects in airspace. The relevance of the study is driven by the rapid development of unmanned aerial vehicles and the growing need for efficient, affordable, and highly accurate surveillance systems capable of operating under uncertainty, limited resources, and complex backgrounds. The classification of the main computer vision tasks is considered, including object detection, classification and identification, localization and tracking, as well as estimation of the spatial position and behavioral characteristics of targets. The evolution of detection methods from traditional approaches to modern deep learning architectures is analyzed, including convolutional neural networks, one-stage and two-stage detectors, visual transformers, and specialized approaches for detecting small-sized objects. Particular attention is paid to the problem of distinguishing birds from drones, multi-object tracking methods, data association algorithms, and the robustness of systems against adversarial attacks. A comparative analysis of the effectiveness of computer vision methods is also carried out.
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