Image segmentation plays an important role in the sensing systems of autonomous underwater vehicles for fishing. Via accurately perceiving the marine organisms and surrounding environment, the automatic catch of marine products can be implemented. However, existing segmentation methods cannot precisely segment marine animals due to the low quality and complex shapes of collected marine images in the underwater situation. A novel multi-scale transformer network (MulTNet) is proposed for improving the segmentation accuracy of marine animals, and it simultaneously possesses the merits of a convolutional neural network (CNN) and a transformer. To alleviate the computational burden of the proposed network, a dimensionality reduction CNN module (DRCM) based on progressive downsampling is first designed to fully extract the low-level features, and then they are fed into a proposed multi-scale transformer module (MTM). For capturing the rich contextural information from different subregions and scales, four parallel small-scale encoder layers with different heads are constructed, and then they are combined with a large-scale transformer layer to form a multi-scale transformer module. The comparative results demonstrate MulTNet outperforms the existing advanced image segmentation networks, with MIOU improvements of 0.76% in the marine animal dataset and 0.29% in the ISIC 2018 dataset. Consequently, the proposed method has important application value for segmenting underwater images.
Keywords: contextural information; marine animal; multi-scale transformer; semantic segmentation; subregion.