Object detection is critical in autonomous driving, and it is more practical yet challenging to localize objects of unknown categories: an endeavour known as Class-Agnostic Object Detection (CAOD). Existing studies on CAOD predominantly rely on ordinary cameras, but these frame-based sensors usually have high latency and limited dynamic range, leading to safety risks in real-world scenarios. In this study, we turn to a new modality enabled by the so-called event camera, featured by its sub-millisecond latency and high dynamic range, for robust CAOD. We propose Detecting Every Object in Events (DEOE), an approach tailored for achieving high-speed, class-agnostic open-world object detection in event-based vision. Built upon the fast event-based backbone: recurrent vision transformer, we jointly consider the spatial and temporal consistencies to identify potential objects. The discovered potential objects are assimilated as soft positive samples to avoid being suppressed as background. Moreover, we introduce a disentangled objectness head to separate the foreground-background classification and novel object discovery tasks, enhancing the model's generalization in localizing novel objects while maintaining a strong ability to filter out the background. Extensive experiments confirm the superiority of our proposed DEOE in comparison with three strong baseline methods that integrate the state-of-the-art event-based object detector with advancements in RGB-based CAOD. Our code is available at https://github.com/Hatins/DEOE.

翻译：目标检测在自动驾驶中至关重要，而定位未知类别物体更具实用性与挑战性，这一任务被称为类别无关目标检测（CAOD）。现有CAOD研究主要依赖普通相机，然而这些基于帧的传感器通常具有高延迟和有限动态范围，在真实场景中会带来安全风险。本研究转向采用事件相机这一新型传感器——其具备亚毫秒级延迟和高动态范围特性，以实现鲁棒的CAOD。我们提出"从事件中检测每一个物体"（DEOE）方法，专门针对基于事件视觉的高速、类别无关开放世界目标检测任务。该算法以快速事件骨干网络——递归视觉Transformer为基础，联合考虑空间与时间一致性来识别潜在物体。发现的潜在物体被整合为软正样本，以避免被抑制为背景。此外，我们引入解耦目标性头部分离前景-背景分类与新颖物体发现任务，在保持强大背景过滤能力的同时，增强模型定位新物体的泛化性能。大量实验表明，我们的DEOE方法优于三种强基线方法——这些方法将最先进的事件目标检测器与基于RGB图像的CAOD最新进展相结合。代码已开源：https://github.com/Hatins/DEOE。