FastICENet: A real-time and accurate semantic segmentation model for aerial remote sensing river ice image

River ice semantic segmentation is a crucial task, which can provide us with information for river monitoring, disaster forecasting, and transportation management. Previous works mainly focus on higher accuracy acquirement, while efficiency is also important for reality usage. In this paper, a real-time and accurate river ice semantic segmentation network is proposed, named FastICENet. The general architecture consists of two branches, i.e., a shallow high-resolution spatial branch and a deep context semantic branch, which are carefully designed for the scale diversity and irregular shape of river ice in remote sensing images. Then, a novel Downsampling module and a dense connection block based on a lightweight Ghost module are adopted in the context branch to reduce the computation cost. Furthermore, a learnable upsampling strategy DUpsampling is utilized to replace the commonly used bilinear interpolation to improve the segmentation accuracy. We deploy detailed experiments on three publicly available datasets, named NWPU_YRCC_EX, NWPU_YRCC2, and Alberta River Ice Segmentation Dataset. The experimental results demonstrate that our method achieves state-of-the-art performance with competing methods, on the NWPU_YRCC_EX dataset, we can achieve the segmentation speed as 90.84FPS and the segmentation accuracy as 90.770% mIoU, which also illustrates the good leverage between accuracy and speed. Our code is available at https://github.com/nwpulab113/FastICENet