Enhanced prototypical contrastive learning for carbon fiber defect detection

Carbon fiber defect detection presents unique challenges due to the homogeneous nature of the material and the scarcity of labeled defect data. Traditional pre-training and fine-tuning approaches, while effective for natural scenes, often struggle with the limited semantic diversity and low recognizability characteristic of carbon fiber surfaces. In this paper, we introduce an enhanced prototypical contrastive learning method specifically tailored for carbon fiber defect detection in scenarios with limited labeled data. Our approach leverages the visual similarity of normal samples in carbon fiber materials to develop a more effective self-supervised pre-training strategy. By addressing the gap between natural image datasets and carbon fiber scenes, we enhance feature learning and improve the adaptability of models to the specific requirements. We propose a novel dense contrastive learning branch and a clustering-based prototype identification technique to better capture the subtle variations indicative of defects. Following a pretrain-finetune paradigm, we demonstrate that our approach significantly outperforms existing self-supervised learning techniques when applied to carbon fiber defect detection tasks.