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Publication Detail
UAV Detection: A STDP Trained Deep Convolutional Spiking Neural Network Retina-Neuromorphic Approach
  • Publication Type:
  • Authors:
    Kirkland P, Di Caterina G, Soraghan J, Andreopoulos Y, Matich G
  • Publication date:
  • Pagination:
    724, 736
  • Published proceedings:
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
  • Volume:
    11727 LNCS
  • ISBN-13:
  • Status:
  • Print ISSN:
© 2019, Springer Nature Switzerland AG. The Dynamic Vision Sensor (DVS) has many attributes, such as sub-millisecond response time along with a good low light dynamic range, that allows it to be well suited to the task for UAV Detection. This paper proposes a system that exploits the features of an event camera solely for UAV detection while combining it with a Spiking 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.
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