论文标题
基于事件的Yolo对象检测：远期感知系统的概念证明
Event-based YOLO Object Detection: Proof of Concept for Forward Perception System
论文作者
论文摘要
神经形态视觉或事件视觉是一种先进的视觉技术，与输出像素的可见摄像机相比，事件视觉每次亮度变化都超过视野（FOV）的特定阈值时，事件视觉会产生神经形态事件。这项研究重点是利用神经形态事件数据进行路边对象检测。这是建立基于人工智能（AI）的管道的概念证明，可用于高级车辆应用的远期感知系统。重点是建立有效的最先进的对象检测网络，并使用事件摄像机快速移动前向感知，并具有更好的推理结果。在本文中，对事件模拟的A2D2数据集进行了手动注释和培训，并在两个不同的Yolov5网络（大小变体）上进行了培训。为了进一步评估其鲁棒性，进行了单个模型测试和集合模型测试。
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.
