NOD: Taking a Closer Look at Detection under Extreme Low-Light Conditions with Night Object Detection Dataset
This work addresses the problem of object detection in low-light environments for autonomous driving and surveillance, but it is incremental as it builds on existing detection frameworks with new data and enhancements.
The paper tackles object detection in extreme low-light conditions by introducing the Night Object Detection (NOD) dataset and proposing an image enhancement module trained for machine cognition, which leads to consistent performance improvements on low-light datasets.
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.