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.
In:
Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation.
(pp. pp. 724-736).
Springer Nature
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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 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.
| Type: | Proceedings paper |
|---|---|
| Title: | UAV Detection: A STDP Trained Deep Convolutional Spiking Neural Network Retina-Neuromorphic Approach |
| Event: | 28th International Conference on Artificial Neural Networks |
| Location: | Munich, Germany |
| Dates: | 17th-19th September 2019 |
| ISBN-13: | 978-3-030-30486-7 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/978-3-030-30487-4_56 |
| Publisher version: | https://doi.org/10.1007/978-3-030-30487-4_56 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | CNN, SNN, STDP, UAV |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10092569 |
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