  With the development of underwater object grabbing technology, underwater
object recognition and segmentation of high accuracy has become a challenge.
The existing underwater object detection technology can only give the general
position of an object, unable to give more detailed information such as the
outline of the object, which seriously affects the grabbing efficiency. To
address this problem, we label and establish the first underwater semantic
segmentation dataset of real scene(DUT-USEG:DUT Underwater Segmentation
Dataset). The DUT- USEG dataset includes 6617 images, 1487 of which have
semantic segmentation and instance segmentation annotations, and the remaining
5130 images have object detection box annotations. Based on this dataset, we
propose a semi-supervised underwater semantic segmentation network focusing on
the boundaries(US-Net: Underwater Segmentation Network). By designing a pseudo
label generator and a boundary detection subnetwork, this network realizes the
fine learning of boundaries between underwater objects and background, and
improves the segmentation effect of boundary areas. Experiments show that the
proposed method improves by 6.7% in three categories of holothurian, echinus,
starfish in DUT-USEG dataset, and achieves state-of-the-art results. The DUT-
USEG dataset will be released at https://github.com/baxiyi/DUT-USEG.
