论文标题
CMS实验中硅探测器的自动视觉检查
Automated visual inspection of silicon detectors in CMS experiment
论文作者
论文摘要
在CERN的CMS实验中，日内瓦，在世界各地的高级实验室中制造了大量的HGCAL传感器模块。每个传感器模块都包含约700个检查站进行视觉检查，因此几乎不可能手动进行此类检查。由于人工智能在制造中越来越广泛地使用，因此传统的检测技术逐渐聪明。为了更准确地评估检查点，我们建议使用基于深度学习的对象检测技术来自动测试大量模块时检测制造缺陷。
In the CMS experiment at CERN, Geneva, a large number of HGCAL sensor modules are fabricated in advanced laboratories around the world. Each sensor module contains about 700 checkpoints for visual inspection thus making it almost impossible to carry out such inspection manually. As artificial intelligence is more and more widely used in manufacturing, traditional detection technologies are gradually being intelligent. In order to more accurately evaluate the checkpoints, we propose to use deep learning-based object detection techniques to detect manufacturing defects in testing large numbers of modules automatically.
