  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/
