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.
Supported by Leonardo, Data Collection in collaboration with University College London.
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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. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation. ICANN 2019. Lecture Notes in Computer Science(), vol 11727. Springer, Cham. https://doi.org/10.1007/978-3-030-30487-4_56
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