  Underwater image enhancement is such an important vision task due to its
significance in marine engineering and aquatic robot. It is usually work as a
pre-processing step to improve the performance of high level vision tasks such
as underwater object detection. Even though many previous works show the
underwater image enhancement algorithms can boost the detection accuracy of the
detectors, no work specially focus on investigating the relationship between
these two tasks. This is mainly because existing underwater datasets lack
either bounding box annotations or high quality reference images, based on
which detection accuracy or image quality assessment metrics are calculated. To
investigate how the underwater image enhancement methods influence the
following underwater object detection tasks, in this paper, we provide a
large-scale underwater object detection dataset with both bounding box
annotations and high quality reference images, namely OUC dataset. The OUC
dataset provides a platform for researchers to comprehensive study the
influence of underwater image enhancement algorithms on the underwater object
detection task.
