Object detection in dark conditions at night is challenging due to poor visibility, leading to reduced accuracy and performance. We propose ODDNet, a framework for object detection in the dark that combines enhanced RGB images with event-based data, leveraging their complementary strengths. Using Zero-DCE, RGB images are enhanced for low-light, while event data is processed through a Temporal Multi-scale Aggregation to extract its temporal features. Attention mechanisms and specialized loss functions improve low-light imaging, preserve spatial details, and enhance detection accuracy. Ablation studies highlight the contributions of Zero-DCE and attention-based fusion. The proposed ODDNet model achieves a mean average precision (mAP) of 45.8% during the day and 28.3% at night at an intersection over Union (IoU) threshold of 0.5. These results demonstrate ODDNet’s capability to effectively address low-light challenges, indicating its potential for autonomous systems and night-time surveillance.