# Automated visual inspection of silicon detectors in CMS experiment

arXiv: https://arxiv.org/abs/2206.02572 · HTML: https://mlsanity.com/paper/2206.02572/

## Key facts

- Cited by 1 papers 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 (most cited first, 7 of 7)

- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [Designing Network Design Spaces](https://mlsanity.com/paper/2003.13678/) (2020)
- [Scaled-YOLOv4: Scaling Cross Stage Partial Network](https://mlsanity.com/paper/2011.08036/) (2020)
- [SM-NAS: Structural-to-Modular Neural Architecture Search for Object Detection](https://mlsanity.com/paper/1911.09929/) (2019)
- [Real-time object detection method based on improved YOLOv4-tiny](https://mlsanity.com/paper/2011.04244/) (2020)

## Built on by (most cited first, 1 of 1)

- [Automated visual inspection of CMS HGCAL silicon sensor surface using an ensemble of a deep convolutional autoencoder and classifier](https://mlsanity.com/paper/2303.15319/) (2023)

## 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.