SDFSeg: multiscale perception and deformable feature fusion for coastal ecosystem

Abstract Monitoring coastal ecosystems is essential for mitigating pollution, preserving biodiversity, and understanding the impacts of climate change. However, existing approaches, such as fully convolutional network (FCN) and Transformer-based models, often struggle with challenges such as low-class variance, difficulty in detecting small targets, and loss of boundary information. To handle large variations in target scales, we propose a semantic segmentation framework, SDFSeg, which integrates three key modules: the scale aware conv, dynamic deformable sample, and fusion perceiver. The scale aware conv is designed to improve multiscale feature extraction by incorporating convolutional layers with varying dilation rates; the dynamic deformable sample precisely aligns target boundaries, focuses on small features, and enables adaptive dynamic sampling for improved small target detection and boundary segmentation; and the fusion perceiver effectively fuses local and global information. Extensive experiments on benchmark datasets demonstrate that our method achieves a superior performance while reducing the computational overhead, confirming its practical applicability.