<p>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.</p>