Generation-Augmented Intelligent Industrial Defect Detection Model Under Extremely Low-Sample Conditions

Deep-learning-based defect detection models require large and diverse datasets, yet obtaining sufficient defect samples in industrial environments is challenging. Existing sample-generation-based methods alleviate data scarcity, but still depend on a certain amount of training data and struggle to enforce the strict structural specifications of industrial products, limiting their applicability. To address these issues, we propose a sample-generation-based defect detection model. The core of our approach is a defect generation network that employs multiscale progressive learning to extract multiresolution features and enables augmentation from a single sample. To satisfy structural constraints without suppressing diversity, we introduce a structural attention mechanism that guides the generative process rather than imposing direct mask-based restrictions. Additionally, we design a simple yet effective foreground—background reconstruction loss that better preserves structural details compared with conventional reconstruction losses. Our method requires only a single sample to initiate data augmentation. As a result, collecting a small number of representative defect samples is sufficient to significantly enhance detection performance, and the low data requirement allows broad applicability across diverse industrial defect detection tasks. Experimental results demonstrate that our method outperforms existing models, with improved sample quality and detection performance, achieving up to a 28% and 20% increase in mAP@0.5 on the DeepPCB and NEU-DET datasets, respectively. Our method proves even more effective when the sample size is limited.