The ability to detect objects in all lighting (i.e., normal-, over-, and under-exposed) conditions is crucial for real-world applications, such as self-driving.Traditional RGB-based detectors often fail under such varying lighting conditions.Therefore, recent works utilize novel event cameras to supplement or guide the RGB modality; however, these methods typically adopt asymmetric network structures that rely predominantly on the RGB modality, resulting in limited robustness for all-day detection. In this paper, we propose EOLO, a novel object detection framework that achieves robust and efficient all-day detection by fusing both RGB and event modalities. Our EOLO framework is built based on a lightweight spiking neural network (SNN) to efficiently leverage the asynchronous property of events. Buttressed by it, we first introduce an Event Temporal Attention (ETA) module to learn the high temporal information from events while preserving crucial edge information. Secondly, as different modalities exhibit varying levels of importance under diverse lighting conditions, we propose a novel Symmetric RGB-Event Fusion (SREF) module to effectively fuse RGB-Event features without relying on a specific modality, thus ensuring a balanced and adaptive fusion for all-day detection. In addition, to compensate for the lack of paired RGB-Event datasets for all-day training and evaluation, we propose an event synthesis approach based on the randomized optical flow that allows for directly generating the event frame from a single exposure image. We further build two new datasets, E-MSCOCO and E-VOC based on the popular benchmarks MSCOCO and PASCAL VOC. Extensive experiments demonstrate that our EOLO outperforms the state-of-the-art detectors,e.g.,RENet,by a substantial margin (+3.74% mAP50) in all lighting conditions.Our code and datasets will be available at https://vlislab22.github.io/EOLO/

翻译：在所有光照条件（即正常曝光、过曝和欠曝）下实现目标检测能力，对自动驾驶等实际应用至关重要。传统基于RGB的检测器在此类多变光照条件下往往失效。为此，近年研究利用新型事件相机补充或引导RGB模态，但这些方法通常采用依赖RGB模态的非对称网络结构，导致全天候检测的鲁棒性有限。本文提出EOLO——一种融合RGB与事件模态的新型目标检测框架，可实现鲁棒高效的全天候检测。EOLO框架基于轻量级脉冲神经网络（SNN）构建，以高效利用事件的异步特性。在此基础上，我们首先引入事件时间注意力（ETA）模块，从事件中学习高时间信息的同时保留关键边缘信息。其次，针对不同光照条件下各模态重要性差异，提出新型对称RGB-事件融合（SREF）模块，该模块不依赖特定模态即可有效融合RGB-事件特征，从而为全天候检测提供均衡自适应的融合方案。此外，为弥补全天候训练与评估所需的配对RGB-事件数据集匮乏问题，我们提出基于随机光流的事件合成方法，可直接从单张曝光图像生成事件帧。并在主流基准MSCOCO和PASCAL VOC基础上构建两个新数据集E-MSCOCO与E-VOC。大量实验表明，我们的EOLO在所有光照条件下均显著超越现有最优检测器（如RENet），mAP50提升达3.74%。相关代码与数据集将在https://vlislab22.github.io/EOLO/公开。