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.

中文翻译：

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通过逐级样式增强生成的逐级平衡水下物体检测数据集

水下物体检测技术对于水下场景中的各种应用具有重要意义。但是，类不平衡问题仍然是当前水下物体检测算法尚未解决的瓶颈。这会导致不同类别之间的较大精度差异，训练数据较多的显性类别实现较高的检测精度，而训练数据较少的少数类别则具有较低的检测精度。在本文中，我们提出了一种新颖的逐级样式增强（CWSA）算法，以从公开竞赛水下数据集URPC2018生成类平衡水下数据集Balance18。CWSA是一种新型的数据增强技术，它通过为少数群体生成各种颜色，纹理和对比度来增强少数群体训练数据。