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arXiv · 2106.15045

EVPropNet: Detecting Drones By Finding Propellers For Mid-Air Landing And Following

Nitin J. Sanket·Chahat Deep Singh·Chethan M. Parameshwara·Cornelia Fermüller·Guido C. H. E. de Croon·Yiannis Aloimonos
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Abstract

The rapid rise of accessibility of unmanned aerial vehicles or drones pose a threat to general security and confidentiality. Most of the commercially available or custom-built drones are multi-rotors and are comprised of multiple propellers. Since these propellers rotate at a high-speed, they are generally the fastest moving parts of an image and cannot be directly "seen" by a classical camera without severe motion blur. We utilize a class of sensors that are particularly suitable for such scenarios called event cameras, which have a high temporal resolution, low-latency, and high dynamic range. In this paper, we model the geometry of a propeller and use it to generate simulated events which are used to train a deep neural network called EVPropNet to detect propellers from the data of an event camera. EVPropNet directly transfers to the real world without any fine-tuning or retraining. We present two applications of our network: (a) tracking and following an unmarked drone and (b) landing on a near-hover drone. We successfully evaluate and demonstrate the proposed approach in many real-world experiments with different propeller shapes and sizes. Our network can detect propellers at a rate of 85.1% even when 60% of the propeller is occluded and can run at upto 35Hz on a 2W power budget. To our knowledge, this is the first deep learning-based solution for detecting propellers (to detect drones). Finally, our applications also show an impressive success rate of 92% and 90% for the tracking and landing tasks respectively.

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Nitin J. Sanket, Chahat Deep Singh, Chethan M. Parameshwara, Cornelia Fermüller, Guido C. H. E. de Croon, Yiannis Aloimonos. 2021-06-29. EVPropNet: Detecting Drones By Finding Propellers For Mid-Air Landing And Following. https://arxiv.org/abs/2106.15045

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