用深度学习自动识别陶瓷件OCT图像中的缺陷，提升无损检测效率。
Defect Segmentation in OCT scans of ceramic parts for non-destructive inspection using deep learning
- 基于U-Net的深度学习模型，结合多配置实验优化性能。
- Dice分数达0.979，优于已有研究，可精准分割孔隙、分层等缺陷。
- 单体积推理仅需18.98秒，适合工业级自动化质检应用。
无损检测（NDT）在陶瓷制造中至关重要，可确保部件质量而不破坏其完整性。光学相干断层扫描（OCT）能实现高分辨率内部成像，揭示孔隙、分层或夹杂物等缺陷。本文提出一种基于深度学习的自动缺陷检测系统，使用人工标注的OCT图像进行训练。开发了基于U-Net架构的神经网络，通过多种实验配置提升性能。后处理技术支持预测结果的定性和定量评估。系统在缺陷分割上表现优异，Dice分数达到0.979，优于同类研究。单体积推理时间仅为18.98秒，具备实际应用潜力，可实现更高效、可靠和自动化的质量控制。
原文摘要 · Abstract (English)
Non-destructive testing (NDT) is essential in ceramic manufacturing to ensure the quality of components without compromising their integrity. In this context, Optical Coherence Tomography (OCT) enables high-resolution internal imaging, revealing defects such as pores, delaminations, or inclusions. This paper presents an automatic defect detection system based on Deep Learning (DL), trained on OCT images with manually segmented annotations. A neural network based on the U-Net architecture is developed, evaluating multiple experimental configurations to enhance its performance. Post-processing techniques enable both quantitative and qualitative evaluation of the predictions. The system shows an accurate behavior of 0.979 Dice Score, outperforming comparable studies. The inference time of 18.98 seconds per volume supports its viability for detecting inclusions, enabling more efficient, reliable, and automated quality control.
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