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Airborne Sensor and Perception Management - Context-Based Selection of Specialized CNNs to Ensure Reliable and Trustworthy Object Detection.

, and . MESAS, volume 13866 of Lecture Notes in Computer Science, page 182-206. Springer, (2022)

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Simulation: The Great Enabler? - Synthetic Data for Supercharging AI Military Systems., , , , , , , and . MESAS, volume 13866 of Lecture Notes in Computer Science, page 312-325. Springer, (2022)Airborne Sensor and Perception Management - Context-Based Selection of Specialized CNNs to Ensure Reliable and Trustworthy Object Detection., and . MESAS, volume 13866 of Lecture Notes in Computer Science, page 182-206. Springer, (2022)Surveillance and Activity Recognition with Depth Information., , , , and . ICME, page 1103-1106. IEEE Computer Society, (2007)Deep Learning Algorithms for Vehicle Detection on UAV Platforms: First Investigations on the Effects of Synthetic Training., , and . MESAS, volume 11995 of Lecture Notes in Computer Science, page 50-70. Springer, (2019)Design and Capabilities of the Munich Cognitive Automobile, , , , , , , , , and 5 other author(s). Proc. IEEE Intelligent Vehicles Symposium, (2008)Automatic artificial ventilation therapy using the ARDSNet protocol enforcing dynamical constraints., , , , , , , , , and . MED, page 235-240. IEEE, (2017)Airborne sensor and perception management: A conceptual approach for surveillance UAS., and . FUSION, page 2444-2451. IEEE, (2012)LIDAR-based Object Detection on Small UAV: Integration, Experimentation and Results., , , and . Infotech@Aerospace, (2012)Application of a probabilistic market-based approach in UAV sensor & perception management., and . FUSION, page 676-683. IEEE, (2013)