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

• URL: http://arxiv.org/abs/2106.15045v1
• Date: Tue, 29 Jun 2021 01:16:01 GMT
• Title: EVPropNet: Detecting Drones By Finding Propellers For Mid-Air Landing And Following
• Authors: Nitin J. Sanket, Chahat Deep Singh, Chethan M. Parameshwara, Cornelia Ferm\"uller, Guido C.H.E. de Croon, Yiannis Aloimonos
• Abstract summary: Drone propellers are the fastest moving parts of an image and cannot be directly "seen" by a classical camera without severe motion blur. We train a deep neural network called EVPropNet to detect propellers from the data of an event camera. We present two applications of our network: (a) tracking and following an unmarked drone and (b) landing on a near-hover drone.
• Score: 11.79762223888294
• License: http://creativecommons.org/licenses/by/4.0/
• 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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