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Neuromorphic vision or event vision is an advanced vision technology, wherein contrast to the visible camera that outputs pixels, the event visiongenerates neuromorphic events every time there is a brightness change whichexceeds a specific threshold in the field of view (FOV). This study focuses onleveraging neuromorphic event data for roadside object detection. This is aproof of concept towards building artificial intelligence (AI) based pipelineswhich can be used for forward perception systems for advanced vehicularapplications. The focus is on building efficient state-of-the-art objectdetection networks with better inference results for fast-moving forwardperception using an event camera. In this article, the event-simulated A2D2dataset is manually annotated and trained on two different YOLOv5 networks(small and large variants). To further assess its robustness, single modeltesting and ensemble model testing are carried out.

Event-based YOLO Object Detection: Proof of Concept for Forward Perception System