Conventional defect detection systems in Automated Fibre Placement (AFP) typically rely on end-to-end supervised learning, necessitating a substantial number of labelled defective samples for effective training. However, the scarcity of such labelled data poses a challenge. To overcome this limitation, we present a comprehensive framework for defect detection and localization in Automated Fibre Placement. Our approach combines unsupervised deep learning and classical computer vision algorithms, eliminating the need for labelled data or manufacturing defect samples. It efficiently detects various surface issues while requiring fewer images of composite parts for training. Our framework employs an innovative sample extraction method leveraging AFP's inherent symmetry to expand the dataset. By inputting a depth map of the fibre layup surface, we extract local samples aligned with each composite strip (tow). These samples are processed through an autoencoder, trained on normal samples for precise reconstructions, highlighting anomalies through reconstruction errors. Aggregated values form an anomaly map for insightful visualization. The framework employs blob detection on this map to locate manufacturing defects. The experimental findings reveal that despite training the autoencoder with a limited number of images, our proposed method exhibits satisfactory detection accuracy and accurately identifies defect locations. Our framework demonstrates comparable performance to existing methods, while also offering the advantage of detecting all types of anomalies without relying on an extensive labelled dataset of defects.

翻译：在自动纤维铺设（AFP）的传统缺陷检测系统中，通常依赖端到端监督学习模式，需要大量标注缺陷样本以实现有效训练。然而，此类标注数据的稀缺性构成了显著挑战。为突破这一限制，我们提出了一套针对自动纤维铺设缺陷检测与定位的综合框架。该方法融合无监督深度学习与经典计算机视觉算法，无需依赖标注数据或制造缺陷样本即可实现高效检测，同时大幅降低复合材料部件训练所需图像数量。框架创新性地利用AFP固有的对称性设计样本提取方法，有效扩展数据集规模。通过输入纤维铺层表面的深度图，我们提取与每条复合材料丝束对齐的局部样本。这些样本经由基于正常样本训练的自编码器处理，实现精确重建，并通过重建误差凸显异常特征。聚合误差值形成直观的可视化异常图，在此基础上采用斑点检测算法定位制造缺陷。实验结果表明，尽管仅使用有限数量的图像训练自编码器，所提出方法仍展现出令人满意的检测精度与缺陷定位能力。与现有方法相比，本框架在保持同等性能的同时，还具备无需依赖大规模标注缺陷数据集即可检测所有类型异常的优势。