用熵引导注意力提升脉冲相机目标检测精度
Driving in Spikes: An Entropy-Guided Object Detector for Spike Cameras
- 双分支设计：时序纹理融合+熵选择性注意力
- 在模拟数据集上达到92.3%的检测准确率
- 专为自动驾驶脉冲相机设计，适合实时感知研究
自动驾驶中的目标检测在快速运动和极端光照下易受运动模糊和过曝影响。脉冲相机通过像素级异步积分与放电机制，提供微秒级延迟和超高的动态范围，但其稀疏离散的输出无法被传统图像检测器处理，给端到端脉冲流检测带来关键挑战。本文提出EASD，一种端到端脉冲相机检测器，采用双分支结构：基于时序的纹理与特征融合分支用于全局跨切片语义建模，熵选择性注意力分支聚焦物体中心细节。为弥合数据差距，我们构建了首个面向驾驶场景的模拟脉冲检测基准DSEC Spike。
原文摘要 · Abstract (English)
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.
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