<p>Ablation study results of probability parameters.</p>

Although meta-learning based few-shot segmentation paradigms have made initial progress, existing methods still suffer from two prominent limitations. First, the limited number of support set samples leads to insufficient feature diversity, rendering the model prone to overfitting during training.

Second, feature extraction restricted to a single hierarchical level and lacking contextual guidance constrains the representational capacity for complex and multi-scale defects. To address these issues, a novel few-shot surface defect segmentation method termed FH-Net is proposed, which aims to achieve high-precision segmentation of previously unseen categories using only a limited number of annotated samples. Specifically, a Semantic-Preserving Frequency Perturbation (SPFP) module is designed, which enriches support set feature diversity through foreground- and global-level amplitude perturbations in the Fourier domain, thereby mitigating the risk of overfitting under limited supervision.