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

ABSTRACT: 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.
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.
NOD: Taking a Closer Look at Detection under Extreme Low-Light Conditions with Night Object Detection Dataset
http://arxiv.org/abs/2110.10364v1