Abstract
Ocean exploration has always been an important strategic direction for the joint efforts of all mankind. Many countries in the world today are developing their own underwater autonomous explorers to better explore the seabed. Vision, as the core technology of autonomous underwater explorers, has a great impact on the efficiency of exploration. Different from traditional tasks, the lack of ambient light on the seabed makes the visual system more demanding. In addition, the complex terrain on the seabed and various creatures with different shapes and colors also make exploration tasks more difficult. In order to effectively solve the above problems, we combined the traditional models to modify the structure and proposed an algorithm for the super-resolution fusion of enhanced extraction features to perform semantic segmentation of seabed scenes. By using a structurally reparameterized backbone network to better extract target features in complex environments, and using subpixel super-resolution to combine multiscale feature semantic information, we can achieve superior ocean scene segmentation performance. In this study, multiclass segmentation and two-class segmentation tests were performed on the public datasets SUIM and DeepFish, respectively. The test results show that the mIoU and mPA indicators of our proposed method on SUIM reach 84.52% and 92.33%mPA, respectively. The mIoU and mPA on DeepFish reach 95.26% and 97.38%, respectively, and the proposed model achieves SOTA compared with state-of-the-art methods. The proposed model and code are exposed via Github 1 .