DMnet: A New Few-Shot Framework for Wind Turbine Surface Defect Detection

Abstract

In the field of wind turbine surface defect detection, most existing defect detection algorithms have a single solution with poor generalization to the dilemma of insufficient defect samples and have unsatisfactory precision for small and concealed defects. Inspired by meta-learning ideology, we devised a cross-task training strategy. By exploring the common properties between tasks, the hypothesis space shrinks so that the needed sample size that satisfies a reliable empirical risk minimizer is reduced. To improve the training efficiency, a depth metric-based classification method is specially designed to find a sample-matching feature space with a good similarity measure by cosine distance. Additionally, a real-time feedback session is innovatively added to the model training loop, which performs information enhancement and filtering according to the task relevance. With dynamic activation mapping, it alleviates the information loss during traditional pooling operations, thus helping to avoid the missed detection of small-scale targets. Experimental results show that the proposed method has significantly improved the defect recognition ability under few-shot training conditions.