Description
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.
Details
N/A
| Is this abstract from experiment? | No |
|---|---|
| Name of experiment and experimental site | N/A |
| Is the speaker for that presentation defined? | No |
| Internet talk | Yes |
Authors
AMIT CHHABRIA
Dr
Nupur Giri
(Vivekanand Education Society's Institute Of Technology)
Ms
Priyanka Asrani
(Vivekanand Education Society's Institute Of Technology)
Ms
Rashmi Manwani
(Vivekanand Education Society's Institute Of Technology)
Dr
Shashi Dugad
(Tata Institute of Fundamental Research)
