Hierarchical Dense Aggregation Network for Few-shot Metal Surface Defect Segmentation

Few-shot defect segmentation (FSDS) has emerged as an effective approach to address the challenge of limited annotated samples in industrial quality inspection. However, existing methods often rely on features extracted from a single semantic level, without effectively suppressing noise interference, which leads to suboptimal performance when segmenting complex and diverse defect patterns. Motivated by these limitations, we propose a novel framework named Hierarchical Dense Aggregation Network (HDANet), which systematically integrates multilevel complementary information through three targeted components: (1) The Prior Disentanglement Module (PDM) explicitly mitigates semantic confusion between defective and defect-free regions by introducing dual training-free prior masks that provide robust structural guidance. (2) The Activation Enhancement Module (AEM) performs dense feature activation to capture fine-grained multiscale details while suppressing class-irrelevant noise. (3) The Progressive Aggregation Module (PAM) models hierarchical long-range dependencies among discrete defects and aggregates contextual relationships across multiple semantic levels. To alleviate the scarcity of annotated metal surface defect data, we further introduce MSD-Seg, a comprehensive benchmark dataset comprising 1,109 high-quality pixel-level annotated images covering 24 representative defect categories from five types of industrial materials. Extensive experiments on MSD-Seg, FSSD-12, and Defect-4i demonstrate that HDANet achieves superior defect segmentation performance. The dataset and code will be made publicly available at https://github.com/VDT-2048/HDANet.