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- W4379653348 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." @default.
- W4379653348 created "2023-06-08" @default.
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- W4379653348 date "2023-06-07" @default.
- W4379653348 modified "2023-10-03" @default.
- W4379653348 title "Event-based YOLO object detection: proof of concept for forward perception system" @default.
- W4379653348 doi "https://doi.org/10.1117/12.2679341" @default.
- W4379653348 hasPublicationYear "2023" @default.
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