# NOD: Taking a Closer Look at Detection under Extreme Low-Light Conditions with Night Object Detection Dataset

arXiv: https://arxiv.org/abs/2110.10364 · HTML: https://mlsanity.com/paper/2110.10364/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 28 references that are in the map.
- First posted on arXiv 20 Oct 2021.

## Builds on (most cited first, 18 of 18)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [FCOS: Fully Convolutional One-Stage Object Detection](https://mlsanity.com/paper/1904.01355/) (2019)
- [MMDetection: Open MMLab Detection Toolbox and Benchmark](https://mlsanity.com/paper/1906.07155/) (2019)
- [Deep Retinex Decomposition for Low-Light Enhancement](https://mlsanity.com/paper/1808.04560/) (2018)
- [Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement](https://mlsanity.com/paper/2001.06826/) (2020)
- [Learning to See in the Dark](https://mlsanity.com/paper/1805.01934/) (2018)
- [Kindling the Darkness: A Practical Low-light Image Enhancer](https://mlsanity.com/paper/1905.04161/) (2019)
- [LLNet: A Deep Autoencoder Approach to Natural Low-light Image Enhancement](https://mlsanity.com/paper/1511.03995/) (2015)
- [Getting to Know Low-light Images with The Exclusively Dark Dataset](https://mlsanity.com/paper/1805.11227/) (2018)
- [Probabilistic Anchor Assignment with IoU Prediction for Object Detection](https://mlsanity.com/paper/2007.08103/) (2020)
- [DeepISP: Towards Learning an End-to-End Image Processing Pipeline](https://mlsanity.com/paper/1801.06724/) (2018)
- [UG2+ Track 2: A Collective Benchmark Effort for Evaluating and Advancing Image Understanding in Poor Visibility Environments](https://mlsanity.com/paper/1904.04474/) (2019)
- [Recurrent Exposure Generation for Low-Light Face Detection](https://mlsanity.com/paper/2007.10963/) (2020)
- [Deep Camera: A Fully Convolutional Neural Network for Image Signal Processing](https://mlsanity.com/paper/1908.09191/) (2019)

## Built on by (most cited first, 12 of 12)

- [Instance Segmentation in the Dark](https://mlsanity.com/paper/2304.14298/) (2023)
- [2PCNet: Two-Phase Consistency Training for Day-to-Night Unsupervised Domain Adaptive Object Detection](https://mlsanity.com/paper/2303.13853/) (2023)
- [Human Pose Estimation in Extremely Low-Light Conditions](https://mlsanity.com/paper/2303.15410/) (2023)
- [GenISP: Neural ISP for Low-Light Machine Cognition](https://mlsanity.com/paper/2205.03688/) (2022)
- [FeatEnHancer: Enhancing Hierarchical Features for Object Detection and Beyond Under Low-Light Vision](https://mlsanity.com/paper/2308.03594/) (2023)
- [Boosting Object Detection with Zero-Shot Day-Night Domain Adaptation](https://mlsanity.com/paper/2312.01220/) (2023)
- [Unsupervised Image Prior via Prompt Learning and CLIP Semantic Guidance for Low-Light Image Enhancement](https://mlsanity.com/paper/2405.11478/) (2024)
- [Real-Time Environment Condition Classification for Autonomous Vehicles](https://mlsanity.com/paper/2405.19305/) (2024)
- [UltraMNIST Classification: A Benchmark to Train CNNs for Very Large Images](https://mlsanity.com/paper/2206.12681/) (2022)
- [Leveraging Content and Context Cues for Low-Light Image Enhancement](https://mlsanity.com/paper/2412.07693/) (2024)
- [ALEN: A Dual-Approach for Uniform and Non-Uniform Low-Light Image Enhancement](https://mlsanity.com/paper/2407.19708/) (2024)
- [The SET Perceptual Factors Framework: Towards Assured Perception for Autonomous Systems](https://mlsanity.com/paper/2508.10798/) (2025)

## Abstract (arXiv)

Recent work indicates that, besides being a challenge in producing perceptually pleasing images, low light proves more difficult for machine cognition than previously thought. In our work, we take a closer look at object detection in low light. First, to support the development and evaluation of new methods in this domain, we present a high-quality large-scale Night Object Detection (NOD) dataset showing dynamic scenes captured on the streets at night. Next, we directly link the lighting conditions to perceptual difficulty and identify what makes low light problematic for machine cognition. Accordingly, we provide instance-level annotation for a subset of the dataset for an in-depth evaluation of future methods. We also present an analysis of the baseline model performance to highlight opportunities for future research and show that low light is a non-trivial problem that requires special attention from the researchers. Further, to address the issues caused by low light, we propose to incorporate an image enhancement module into the object detection framework and two novel data augmentation techniques. Our image enhancement module is trained under the guidance of the object detector to learn image representation optimal for machine cognition rather than for the human visual system. Finally, experimental results confirm that the proposed method shows consistent improvement of the performance on low-light datasets.