The growing availability of UAVs has created a demand for drone detection systems. Several studies have used neuromorphic cameras to detect UAVs; however, a fully neuromorphic system remains to be explored. We present a fully neuromorphic system consisting of an event camera and a spiking neural network running on neuromorphic hardware. Two spiking neural network architectures have been evaluated and compared to a non-spiking artificial neural network. The spiking networks show promise and perform on par with the non-spiking network in a few scenarios. Spiking networks were deployed on the Synsense Speck, a neuromorphic system on a chip, and demonstrated increased performance compared to simulations. The deployed network is capable of detecting drones up to a distance of 20 meters with high probability while consuming less than 7.13 milliwatts. The system can operate for over a year powered by a small power bank. In contrast, the equivalent non-spiking network running on the NVIDIA Jetson would operate for a few hours. The use of neuromorphic hardware enables sustained UAV detection in remote and challenging environments previously deemed inaccessible due to power constraints.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-204179 |
| Date | January 2024 |
| Creators | Eldeborg Lundin, Anton, Winzell, Rasmus |
| Publisher | Linköpings universitet, Datorseende |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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