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

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.