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With new regulations around unmanned aerial vehicles (UAV) use in the European Union, the drone industry is set to bloom. This led to the BEAST project, which aims at the automatic fail-safe landing for UAVs. This technical paper tackles a specific part within the module, which revolves around anomaly detection to avoid obstacles for fail-safe landing, where the CutPaste anomaly detection framework is used. For this framework new alterations are introduced, CutPaste-multiple and CutPaste-normalscar. Various experiments try to maximize the model’s performance, including choosing the tile size, CutPaste alteration method, hyperparameter tuning, and different data augmentation techniques. The best-performing model achieved an F1-score of 0.8036. Although anomaly detection looks like a suitable approach to resolve this problem, some limitations must be overcome before being used in a real-life application

Post Author: Koen Molenaar

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