A Robust Object Segmentation Network for UnderWater Scenes
Ruizhe Chen, Zhenqi Fu, Yue Huang, En Cheng, Xinghao Ding
Abstract
Underwater object segmentation is one of the key technologies in the fields of marine biology research and autonomous underwater vehicles. The challenges of underwater object segmentation originate from two aspects, 1) the complex underwater environment and 2) the camouflage characteristics of marine animals. In this paper, we propose WaterSNet, an underwater object segmentation network to address these challenges. Specified, we propose a random style adaption (RSA) module as well as a siamese structure to reduce the impact of water degradation diversity. We also extract multi-scale features via the receptive field block (RFB) module, and then fuses multi-level features to better utilize global context information via the attention fusion block (AFB) module. Experimental results on marine animal dataset MAS3K demonstrate that the proposed method outperforms other state-of-the-art methods significantly. The code will be available at: https://github.com/ruizhechen/WaterSNet/