HCFNet: A SAM2-Based Hierarchical Cross-Branch Frequency-Aware Network for Industrial Surface Defect Segmentation

Foundation models such as the Segment Anything Model 2 (SAM2) have demonstrated strong performance in image segmentation; however, their application to industrial defect detection faces significant challenges due to the substantial domain gap between natural and industrial images, insufficient sensitivity to fine-grained high-frequency structures, and reliance on manual prompts. To address these issues, this study proposes a Hierarchical Cross-Branch Frequency-Aware Network (HCFNet) to adapt SAM2 for prompt-free industrial defect segmentation. First, a Gated Adapter is introduced into the frozen SAM2 encoder, enabling efficient cross-domain transfer without massive parameter retraining, thereby effectively preserving the pre-trained visual priors. Secondly, a Laplacian-enhanced Auxiliary Branch is designed to explicitly amplify high-frequency components, compensating for the inherent perception limitations of the Transformer backbone and significantly awakening the model's sensitivity to subtle defects like micro-cracks. Finally, a Cross-branch Multi-scale Fusion Module is proposed to seamlessly align and integrate global semantic information with local structural details in a unified manner, resolving heterogeneous feature distribution conflicts. Extensive experiments on the MVTec AD and VisA datasets demonstrate that the proposed method consistently outperforms SAM2-based baselines in terms of mIoU and mDice. This study establishes an effective approach for leveraging foundation models in automated industrial inspection and is expected to drive advancements in precise defect perception technologies.