真实场景水下语义分割方法及数据集

随着水下生物抓取技术的不断发展，高精度的水下物体识别与分割成为了挑战。已有的水下目标检测技术仅能给出物体的大体位置，无法提供物体轮廓等更加细致的信息，严重影响了抓取效率。为了解决这一问题，标注并建立了真实场景水下语义分割数据集DUT-USEG，该数据集包含6 617张图像，其中1 487张具有语义分割和实例分割标注，剩余5 130张图像具有目标检测框标注。基于该数据集，提出了一个关注边界的半监督水下语义分割网络(US-Net)，该网络通过设计伪标签生成器和边界检测子网络，实现了对水下物体与背景之间边界的精细学习，提升了边界区域的分割效果。实验表明:所提方法在DUT-USEG数据集的海参、海胆和海星3个类别上相较于对比方法提升了6.7%，达到了目前最好的分割精度。 

A real scene underwater semantic segmentation method and related dataset

Underwater object recognition and segmentation with high accuracy have become a challenge with the development of underwater object grabbing technology. 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 underwater semantic segmentation dataset of a real scene (DUT-USEG). The DUT-USEG dataset includes 6 617 images, 1 487 of which have semantic segmentation and instance segmentation annotations, and the remaining 5 130 images have object detection box annotations. Based on this dataset, we propose a semi-supervised underwater semantic segmentation network (US-Net) focusing on the boundaries. 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, and starfish in DUT-USEG dataset, and achieves state-of-the-art results.