David Bull, Fan Zhang and Paul Hill
Deep Learning systems offer state-of-the-art performance in image analysis, outperforming conventional methods. Such systems offer huge potential across military and commercial domains including: human/target detection and recognition and spatial localization/mapping. However, heavy computational requirements limit their exploitation in surveillance applications, particularly airborne, where low-power embedded processing and limited bandwidth are common constraints.
Our aim is to explore deep learning performance whilst reducing processing and communication overheads, by developing learning-optimal compression schemes trained in conjunction with detection networks.
This work has been funded by DASA Advanced Vision 2020 Programme.