Automated visual inspection of silicon detectors in CMS experiment
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 7 of its 25 references that are in the map. First posted on arXiv 3 Jun 2022.
Builds on
7 of 25 references.
References · 7
- 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
- 2018YOLOv3: An Incremental Improvementcited by 2,659
- 2020YOLOv4: Optimal Speed and Accuracy of Object Detectioncited by 1,630
- 2020Designing Network Design Spacescited by 960
- 2020Scaled-YOLOv4: Scaling Cross Stage Partial Networkcited by 211
- 2019SM-NAS: Structural-to-Modular Neural Architecture Search for Object Detectioncited by 25
- 2020Real-time object detection method based on improved YOLOv4-tinycited by 9
Built on by
Citing papers per year: 2023: 1.
1 paper.