  To boost the object grabbing capability of underwater robots for open-sea
farming, we propose a new dataset (UDD) consisting of three categories
(seacucumber, seaurchin, and scallop) with 2,227 images. To the best of our
knowledge, it is the first 4K HD dataset collected in a real open-sea farm. We
also propose a novel Poisson-blending Generative Adversarial Network (Poisson
GAN) and an efficient object detection network (AquaNet) to address two common
issues within related datasets: the class-imbalance problem and the problem of
mass small object, respectively. Specifically, Poisson GAN combines Poisson
blending into its generator and employs a new loss called Dual Restriction loss
(DR loss), which supervises both implicit space features and image-level
features during training to generate more realistic images. By utilizing
Poisson GAN, objects of minority class like seacucumber or scallop could be
added into an image naturally and annotated automatically, which could increase
the loss of minority classes during training detectors to eliminate the
class-imbalance problem; AquaNet is a high-efficiency detector to address the
problem of detecting mass small objects from cloudy underwater pictures. Within
it, we design two efficient components: a depth-wise-convolution-based
Multi-scale Contextual Features Fusion (MFF) block and a Multi-scale
Blursampling (MBP) module to reduce the parameters of the network to 1.3
million. Both two components could provide multi-scale features of small
objects under a short backbone configuration without any loss of accuracy. In
addition, we construct a large-scale augmented dataset (AUDD) and a
pre-training dataset via Poisson GAN from UDD. Extensive experiments show the
effectiveness of the proposed Poisson GAN, AquaNet, UDD, AUDD, and pre-training
dataset.
