NEFELI: A computer vision software for aircraft detection and tracking implemented on an edge Graphic Processing Unit
Year: 2024
Type: article
Abstract: Efficient detection and accurate collision estimation for non-cooperative aerial vehicles are crucial for the realization of fully autonomous aircraft. This paper introduces NEFELI, a machine learning software utilizing optical sensors to detect, and track non-cooperative aerial vehicles. NEFELI’s detector employs an enhanced YOLOv5 large model, strengthened with a sliding window postprocessing step to enhance detection capabilities for distant, diminutive objects. Furthermore, NEFELI introduces several innovations in its tracking component. The tracking system adopts the Strong-SORT model, enhanced with a global motion compensation technique to accommodate the movement of the host aircraft’s camera. A key advancement lies in the creation and utilization of the first large-scale re-identification dataset of aerial objects. This dataset is used to train the deep learning component of the tracking module.
Source: Figshare
Authors: Anastasios Arsenos (17925563), Evangelos Petrongonas (17659833), Orfeas Filippopoulos (19933588), Christos Skliros (19933595), Dimitrios Kollias (17216917), Stefanos Kollias (17161576)
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7067929700
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