To promote the development of underwater robot picking in sea farms, we propose an underwater open-sea farm object detection dataset called UDD. Concretely, UDD consists of 3 categories (seacucumber, seaurchin, and scallop) with 2,227 images. To the best of our knowledge, it's the first dataset collected in a real open-sea farm for underwater robot picking and we also propose a novel Poisson-blending-embedded Generative Adversarial Network (Poisson GAN) to overcome the class-imbalance and massive small objects issues in UDD. By utilizing Poisson GAN to change the number, position, even size of objects in UDD, we construct a large scale augmented dataset (AUDD) containing 18,000 images. Besides, in order to make the detector better adapted to the underwater picking environment, a dataset (Pre-training dataset) for pre-training containing 590,000 images is also proposed.
@misc{liu2021new, title={A New Dataset, Poisson GAN and AquaNet for Underwater Object Grabbing}, author={Chongwei Liu and Zhihui Wang and Shijie Wang and Tao Tang and Yulong Tao and Caifei Yang and Haojie Li and Xing Liu and Xin Fan}, year={2021}, eprint={2003.01446}, archivePrefix={arXiv}, primaryClass={cs.CV} }