  Neuromorphic vision or event vision is an advanced vision technology, where
in contrast to the visible camera that outputs pixels, the event vision
generates neuromorphic events every time there is a brightness change which
exceeds a specific threshold in the field of view (FOV). This study focuses on
leveraging neuromorphic event data for roadside object detection. This is a
proof of concept towards building artificial intelligence (AI) based pipelines
which can be used for forward perception systems for advanced vehicular
applications. The focus is on building efficient state-of-the-art object
detection networks with better inference results for fast-moving forward
perception using an event camera. In this article, the event-simulated A2D2
dataset is manually annotated and trained on two different YOLOv5 networks
(small and large variants). To further assess its robustness, single model
testing and ensemble model testing are carried out.
