# Driving in Spikes: An Entropy-Guided Object Detector for Spike Cameras

arXiv: https://arxiv.org/abs/2511.15459 · HTML: https://mlsanity.com/paper/2511.15459/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 30 references that are in the map.
- First posted on arXiv 19 Nov 2025.

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

- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [Events-to-Video: Bringing Modern Computer Vision to Event Cameras](https://mlsanity.com/paper/1904.08298/) (2019)
- [A Retina-inspired Sampling Method for Visual Texture Reconstruction](https://mlsanity.com/paper/1907.08769/) (2019)
- [CAMixerSR: Only Details Need More "Attention"](https://mlsanity.com/paper/2402.19289/) (2024)
- [Optical Flow Estimation for Spiking Camera](https://mlsanity.com/paper/2110.03916/) (2021)
- [Spike Camera and Its Coding Methods](https://mlsanity.com/paper/2104.04669/) (2021)

## Abstract (arXiv)

Object detection in autonomous driving suffers from motion blur and saturation under fast motion and extreme lighting. Spike cameras, offer microsecond latency and ultra high dynamic range for object detection by using per pixel asynchronous integrate and fire. However, their sparse, discrete output cannot be processed by standard image-based detectors, posing a critical challenge for end to end spike stream detection. We propose EASD, an end to end spike camera detector with a dual branch design: a Temporal Based Texture plus Feature Fusion branch for global cross slice semantics, and an Entropy Selective Attention branch for object centric details. To close the data gap, we introduce DSEC Spike, the first driving oriented simulated spike detection benchmark.