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Underwater object detection technique is of great significance for variousapplications in underwater the scenes. However, class imbalance issue is stillan unsolved bottleneck for current underwater object detection algorithms. Itleads to large precision discrepancies among different classes that thedominant classes with more training data achieve higher detection precisionswhile the minority classes with fewer training data achieves much lowerdetection precisions. In this paper, we propose a novel class-wise styleaugmentation (CWSA) algorithm to generate a class-balanced underwater datasetBalance18 from the public contest underwater dataset URPC2018. CWSA is a newkind of data augmentation technique which augments the training data for theminority classes by generating various colors, textures and contrasts for theminority classes. Compare with previous data augmentation algorithms suchflipping, cropping and rotations, CWSA is able to generate a class balancedunderwater dataset with diverse color distortions and haze-effects.

Class balanced underwater object detection dataset generated by class-wise style augmentation