  Underwater object detection technique is of great significance for various
applications in underwater the scenes. However, class imbalance issue is still
an unsolved bottleneck for current underwater object detection algorithms. It
leads to large precision discrepancies among different classes that the
dominant classes with more training data achieve higher detection precisions
while the minority classes with fewer training data achieves much lower
detection precisions. In this paper, we propose a novel class-wise style
augmentation (CWSA) algorithm to generate a class-balanced underwater dataset
Balance18 from the public contest underwater dataset URPC2018. CWSA is a new
kind of data augmentation technique which augments the training data for the
minority classes by generating various colors, textures and contrasts for the
minority classes. Compare with previous data augmentation algorithms such
flipping, cropping and rotations, CWSA is able to generate a class balanced
underwater dataset with diverse color distortions and haze-effects.
