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

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Kirkland, Paul and Di Caterina, Gaetano and Soraghan, John and Andreopoulos, Yiannis and Matich, George (2019) UAV detection : a STDP trained deep convolutional spiking neural network retina-neuromorphic approach. In: 28th International Conference on Artificial Neural Networks 2019, 2019-09-17 - 2019-09-19, Klinikum rechts der Isar, Technische Universität München. (https://doi.org/10.1007/978-3-030-30487-4_56)

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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.

ORCID iDs

Kirkland, Paul ORCID logoORCID: https://orcid.org/0000-0001-5905-6816, Di Caterina, Gaetano ORCID logoORCID: https://orcid.org/0000-0002-7256-0897, Soraghan, John ORCID logoORCID: https://orcid.org/0000-0003-4418-7391, Andreopoulos, Yiannis and Matich, George;
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