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

arXiv: https://arxiv.org/abs/2212.07181 · HTML: https://mlsanity.com/paper/2212.07181/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 19 references that are in the map.
- First posted on arXiv 14 Dec 2022.

## Builds on (most cited first, 8 of 8)

- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [HATS: Histograms of Averaged Time Surfaces for Robust Event-based Object Classification](https://mlsanity.com/paper/1803.07913/) (2018)
- [A2D2: Audi Autonomous Driving Dataset](https://mlsanity.com/paper/2004.06320/) (2020)
- [Learning to Detect Objects with a 1 Megapixel Event Camera](https://mlsanity.com/paper/2009.13436/) (2020)
- [AEGNN: Asynchronous Event-based Graph Neural Networks](https://mlsanity.com/paper/2203.17149/) (2022)
- [Asynchronous Convolutional Networks for Object Detection in Neuromorphic Cameras](https://mlsanity.com/paper/1805.07931/) (2018)
- [Object Detection in Thermal Spectrum for Advanced Driver-Assistance Systems (ADAS)](https://mlsanity.com/paper/2109.09854/) (2021)

## Built on by (most cited first, 2 of 2)

- [EHGCN: Hierarchical Euclidean-Hyperbolic Fusion via Motion-Aware GCN for Hybrid Event Stream Perception](https://mlsanity.com/paper/2504.16616/) (2025)
- [Decisive Data using Multi-Modality Optical Sensors for Advanced Vehicular Systems](https://mlsanity.com/paper/2307.13600/) (2023)

## Abstract (arXiv)

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.