Addressing Class Scarcity and Imbalance for Few-Shot Detection of Wind Turbine Blade Surface Defects

Wind turbine blade (WTB) surface defect detection often suffers from severe class imbalance and limited annotated data, making conventional deep learning approaches impractical. Few-shot object detection (FSOD) addresses this challenge by enabling models to detect novel defect types from only a few labelled examples. In this study, FSOD is applied to the WTB surface defect detection task using the DTU dataset. We adopt the Two-Stage Fine-Tuning Approach and utilised Contrastive Proposal Encoding (CPE) loss to improve proposal discrimination and feature representation under low-data conditions. A progressive experimental setup is designed by partitioning defect categories into base and novel classes based on sample scarcity.
Our results show that integrating CPE loss leads to up to 22\% improvement in novel class detection and overall gains of around 2–3\% in mAP under 10-shot scenarios, while highlighting performance trade-offs under extreme class imbalance.These findings validate the effectiveness of contrastive objectives in FSOD and underscore the importance of strategic dataset construction for robust generalisation.