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

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

Research output: Contribution to conferencePaperpeer-review

4 Citations (Scopus)
34 Downloads (Pure)


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
Number of pages13
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


Conference28th International Conference on Artificial Neural Networks 2019
Abbreviated titleICANN 2019
Internet address


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

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