Event-based YOLO object detection: proof of concept for forward perception system

Abstract

Neuromorphic vision or event vision is an advanced vision technology, where in contrast to visible camera sensors that output pixels, the event vision generates neuromorphic events every time there’s 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 imaging pipelines which can be used for forward perception systems for advanced vehicular applications. The focus is on building efficient stateof- 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.

Neuromorphic Driver Monitoring Systems: A Computationally Efficient Proof-of-Concept for Driver Distraction Detection

“…Event-based vision for driver monitoring has many advantages including high temporal resolution, reduced motion blur, low latency, reduced power consumption for the onboard sensor suite, and most importantly privacy [22], [23], [9], [6], [2]. The following are some of the recent studies on driver monitoring using event cameras.…”