  Generic object detection algorithms have proven their excellent performance
in recent years. However, object detection on underwater datasets is still less
explored. In contrast to generic datasets, underwater images usually have color
shift and low contrast; sediment would cause blurring in underwater images. In
addition, underwater creatures often appear closely to each other on images due
to their living habits. To address these issues, our work investigates
augmentation policies to simulate overlapping, occluded and blurred objects,
and we construct a model capable of achieving better generalization. We propose
an augmentation method called RoIMix, which characterizes interactions among
images. Proposals extracted from different images are mixed together. Previous
data augmentation methods operate on a single image while we apply RoIMix to
multiple images to create enhanced samples as training data. Experiments show
that our proposed method improves the performance of region-based object
detectors on both Pascal VOC and URPC datasets.
