Speaker
suyun huang
Description
This talk presents a machine learning–based approach for automated defect detection in CMS HGCAL assembly. A hybrid framework combining supervised object detection (YOLO) and unsupervised anomaly detection (PatchCore) is developed to identify defects such as glue leakage and abnormal wire bonding. The method achieves strong performance in controlled conditions and demonstrates the advantage of combining known-pattern recognition with anomaly detection. Challenges such as rare defects and domain shift across production batches are also discussed.
本报告介绍了一种基于机器学习的CMS高粒度量能器（HGCAL）组装缺陷自动检测方法。该方法构建了一个融合监督学习目标检测（YOLO）与无监督异常检测（PatchCore）的混合框架，用于识别如胶水泄漏和异常bond等缺陷。在受控条件下，该方法表现出良好的性能，并展示了将已知模式识别与异常检测相结合的优势。同时，还讨论了实际应用中面临的挑战，例如异常样本稀缺以及不同生产批次之间的偏移问题。
Primary authors
suyun huang
Shuo Han
(IHEP (高能所))
