UAV detection: a STDP trained deep convolutional spiking neural network retina-neuromorphic approach

Paul Kirkland, Gaetano Di Caterina, John Soraghan, Yiannis Andreopoulos, George Matich

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Abstract

The Dynamic Vision Sensor (DVS) has many attributes, such as sub-millisecond response time along with a good low light dy- namic range, that allows it to be well suited to the task for UAV De- tection. This paper proposes a system that exploits the features of an event camera solely for UAV detection while combining it with a Spik- ing Neural Network (SNN) trained using the unsupervised approach of Spike Time-Dependent Plasticity (STDP), to create an asynchronous, low power system with low computational overhead. Utilising the unique features of both the sensor and the network, this result in a system that is robust to a wide variety in lighting conditions, has a high temporal resolution, propagates only the minimal amount of information through the network, while training using the equivalent of 43,000 images. The network returns a 91% detection rate when shown other objects and can detect a UAV with less than 1% of pixels on the sensor being used for processing.
Original languageEnglish
Pages724-736
Number of pages13
DOIs
Publication statusPublished - 18 Sep 2019
Event28th International Conference on Artificial Neural Networks 2019 - Klinikum rechts der Isar, Technische Universität München, Munich, Germany
Duration: 17 Sep 201919 Sep 2019
Conference number: 28
https://e-nns.org/icann2019/

Conference

Conference28th International Conference on Artificial Neural Networks 2019
Abbreviated titleICANN 2019
CountryGermany
CityMunich
Period17/09/1919/09/19
Internet address

Keywords

  • dynamic vision sensor (DVS)
  • UAV detection
  • spiking neural network (SNN)
  • spike time-dependent plasticity (STDP)

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    Kirkland, P., Di Caterina, G., Soraghan, J., Andreopoulos, Y., & Matich, G. (2019). UAV detection: a STDP trained deep convolutional spiking neural network retina-neuromorphic approach. 724-736. Paper presented at 28th International Conference on Artificial Neural Networks 2019, Munich, Germany. https://doi.org/10.1007/978-3-030-30487-4_56