Collaborative Refinement Guidance for High-Fidelity Defect Image Synthesis

Automated Visual Inspection is a cornerstone of modern manufacturing, yet the development of robust deep learning models is frequently impeded by the scarcity and imbalance of training data. This challenge is particularly acute for industrial defects, which often manifest as subtle anomalies intrinsically linked to complex, structured backgrounds. To address this challenge, CRG-DefectDiffuser is proposed as a collaborative generative framework for high-fidelity defect synthesis. At its core lies the Collaborative Refinement Guidance (CRG) mechanism, which orchestrates two specialized diffusion models: one trained on abundant defect-free images to master background context, and another trained on scarce defect patches to encode fine-grained defect semantics. The CRG mechanism steers the synthesis by dynamically generating a guidance map, which is refined through a four-stage process to ensure that morphologically accurate defects are seamlessly integrated into the appropriate background context. Augmenting training data with our method boosts the defect detection mAP@50-95 from a baseline of 0.496 to 0.557, corresponding to a 12.3 percentage point relative improvement. The framework also demonstrates superior scalability, with performance gains continuing up to the three-fold data augmentation evaluated in our experiments, a point where competing methods often falter. These results establish CRG-DefectDiffuser as an effective and practical solution to data scarcity in industrial visual inspection, with strong potential for generalization across diverse manufacturing scenarios. The source code is publicly available at https://github.com/weihang-luo/ CRG-DefectDiffuser.