Dependable visual drone detection is crucial for the secure integration of drones into the airspace. However, drone detection accuracy is significantly affected by domain shifts due to environmental changes, varied points of view, and background shifts. To address these challenges, we present the DrIFT dataset, specifically developed for visual drone detection under domain shifts. DrIFT includes fourteen distinct domains, each characterized by shifts in point of view, synthetic-to-real data, season, and adverse weather. DrIFT uniquely emphasizes background shift by providing background segmentation maps to enable background-wise metrics and evaluation. Our new uncertainty estimation metric, MCDO-map, features lower postprocessing complexity, surpassing traditional methods. We use the MCDO-map in our uncertainty-aware unsupervised domain adaptation method, demonstrating superior performance to SOTA unsupervised domain adaptation techniques.
@article{dadboud2025DrIFT,title={DrIFT: Autonomous Drone Dataset with Integrated Real and Synthetic Data, Flexible Views, and Transformed Domains},author={Dadboud, Fardad and Azad, Hamid and Mehta, Varun and Bolic, Miodrag and Mantegh, Iraj},booktitle={2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) },pages={1--8},year={2025},organization={IEEE/CVF},doi={https://doi.org/10.48550/arXiv.2412.04789},url={https://arxiv.org/abs/2412.04789},}
2023
IEEE
Object Semantics Give Us the Depth We Need: Multi-Task Approach to Aerial Depth Completion
Sara Hatami Gazani, Fardad Dadboud, Miodrag Bolic, Iraj Mantegh, and Homayoun Najjaran
@article{gazani2023object,title={Object Semantics Give Us the Depth We Need: Multi-Task Approach to Aerial Depth Completion},author={Gazani, Sara Hatami and Dadboud, Fardad and Bolic, Miodrag and Mantegh, Iraj and Najjaran, Homayoun},booktitle={2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC)},pages={2565--2570},year={2023},organization={IEEE},}
A Deep Learning Approach for Drone Detection and Classification Using Radar and Camera Sensor Fusion
Varun Mehta, Fardad Dadboud, Miodrag Bolic, and Iraj Mantegh
@article{mehta2023deep,title={A Deep Learning Approach for Drone Detection and Classification Using Radar and Camera Sensor Fusion},author={Mehta, Varun and Dadboud, Fardad and Bolic, Miodrag and Mantegh, Iraj},booktitle={2023 IEEE Sensors Applications Symposium (SAS)},pages={01--06},year={2023},organization={IEEE}}
Enhancing Counter Drone Operations Through Human-AI Collaboration: A Hierarchical Decision-Making Framework
@article{mehta2023enhancing,title={Enhancing Counter Drone Operations Through Human-AI Collaboration: A Hierarchical Decision-Making Framework},author={Mehta, Varun and Kaza, Kesav and Dadboud, Fardad and Bolic, Miodrag and Mantegh, Iraj},booktitle={2023 IEEE/AIAA 42nd Digital Avionics Systems Conference (DASC)},pages={1--7},year={2023},organization={IEEE}}
Real-Time UAV and Payload Detection and Classification System Using Radar and Camera Sensor Fusion
@article{mehta2023real,title={Real-Time UAV and Payload Detection and Classification System Using Radar and Camera Sensor Fusion},author={Mehta, Varun and Azad, Hamid and Dadboud, Fardad and Bolic, Miodrag and Mantegh, Iraj},booktitle={2023 IEEE/AIAA 42nd Digital Avionics Systems Conference (DASC)},pages={1--6},year={2023},organization={IEEE}}
Air-to-Air Simulated Drone Dataset for AI-powered problems
@article{azad2023air,title={Air-to-Air Simulated Drone Dataset for AI-powered problems},author={Azad, Hamid and Mehta, Varun and Dadboud, Fardad and Bolic, Miodrag and Mantegh, Iraj},booktitle={2023 IEEE/AIAA 42nd Digital Avionics Systems Conference (DASC)},pages={1--7},year={2023},organization={IEEE}}
2022
A machine learning model for predicting favorable outcome in severe traumatic brain injury patients after 6 months
Mehdi Nourelahi, Fardad Dadboud, Hosseinali Khalili, Amin Niakan, and Hossein Parsaei
@article{nourelahi2022machine,title={A machine learning model for predicting favorable outcome in severe traumatic brain injury patients after 6 months},author={Nourelahi, Mehdi and Dadboud, Fardad and Khalili, Hosseinali and Niakan, Amin and Parsaei, Hossein},journal={Acute and critical care},volume={37},number={1},pages={45--52},year={2022},publisher={Korean Society of Critical Care Medicine},}
2021
Single-stage uav detection and classification with yolov5: Mosaic data augmentation and panet
@article{dadboud2021single,title={Single-stage uav detection and classification with yolov5: Mosaic data augmentation and panet},author={Dadboud, Fardad and Patel, Vaibhav and Mehta, Varun and Bolic, Miodrag and Mantegh, Iraj},booktitle={2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)},pages={1--8},year={2021},organization={IEEE},}
Drone-vs-bird detection challenge at IEEE AVSS2021
Angelo Coluccia, Alessio Fascista, Arne Schumann, Lars Sommer, Anastasios Dimou, Dimitrios Zarpalas, Fatih Cagatay Akyon, Ogulcan Eryuksel, Kamil Anil Ozfuttu, Sinan Onur Altinuc, and others
@article{coluccia2021drone,title={Drone-vs-bird detection challenge at IEEE AVSS2021},author={Coluccia, Angelo and Fascista, Alessio and Schumann, Arne and Sommer, Lars and Dimou, Anastasios and Zarpalas, Dimitrios and Akyon, Fatih Cagatay and Eryuksel, Ogulcan and Ozfuttu, Kamil Anil and Altinuc, Sinan Onur and others},booktitle={2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)},pages={1--8},year={2021},organization={IEEE}}