Class balanced underwater object detection dataset generated by class-wise style augmentation
This addresses the class imbalance issue in underwater object detection, which is crucial for applications like marine exploration, but the approach is incremental as it builds on existing data augmentation techniques.
The paper tackles the class imbalance problem in underwater object detection by proposing a class-wise style augmentation (CWSA) algorithm, which generates a class-balanced dataset (Balance18) from URPC2018, resulting in improved detection precisions for minority classes.
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.