A Faster R-CNN trained on synthetic drone images reached 97.0% AP50 on the real MAV-Vid set, close to 97.8% for a real-data model, but only 49.8% and 67.8% on two other real datasets.
Defending Airports from UAS: A Survey on Cyber-Attacks and Counter-Drone Sensing Tech- nologies,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2024 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data
A Faster R-CNN trained on synthetic drone images reached 97.0% AP50 on the real MAV-Vid set, close to 97.8% for a real-data model, but only 49.8% and 67.8% on two other real datasets.