Double Helix Diffusion for Cross-Domain Anomaly Image Generation
This addresses the data scarcity issue in visual anomaly inspection for manufacturing, though it appears incremental as it builds on existing synthetic data generation methods.
The paper tackles the problem of generating synthetic anomaly images for training robust detectors in manufacturing, where real anomaly samples are scarce, by introducing Double Helix Diffusion (DH-Diff), which simultaneously synthesizes high-fidelity anomaly images and pixel-level masks, resulting in significant improvements in downstream anomaly detection performance.
Visual anomaly inspection is critical in manufacturing, yet hampered by the scarcity of real anomaly samples for training robust detectors. Synthetic data generation presents a viable strategy for data augmentation; however, current methods remain constrained by two principal limitations: 1) the generation of anomalies that are structurally inconsistent with the normal background, and 2) the presence of undesirable feature entanglement between synthesized images and their corresponding annotation masks, which undermines the perceptual realism of the output. This paper introduces Double Helix Diffusion (DH-Diff), a novel cross-domain generative framework designed to simultaneously synthesize high-fidelity anomaly images and their pixel-level annotation masks, explicitly addressing these challenges. DH-Diff employs a unique architecture inspired by a double helix, cycling through distinct modules for feature separation, connection, and merging. Specifically, a domain-decoupled attention mechanism mitigates feature entanglement by enhancing image and annotation features independently, and meanwhile a semantic score map alignment module ensures structural authenticity by coherently integrating anomaly foregrounds. DH-Diff offers flexible control via text prompts and optional graphical guidance. Extensive experiments demonstrate that DH-Diff significantly outperforms state-of-the-art methods in diversity and authenticity, leading to significant improvements in downstream anomaly detection performance.