# Using Computer Vision to enhance Safety of Workforce in Manufacturing in a Post COVID World

arXiv: https://arxiv.org/abs/2005.05287 · HTML: https://mlsanity.com/paper/2005.05287/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 May 2020.

## Built on by (most cited first, 3 of 3)

- [Detection of a facemask in real-time using deep learning methods: Prevention of Covid 19](https://mlsanity.com/paper/2401.15675/) (2024)
- [Deep Learning Techniques for Visual Counting](https://mlsanity.com/paper/2206.03033/) (2022)
- [Computer Vision-based Social Distancing Surveillance Solution with Optional Automated Camera Calibration for Large Scale Deployment](https://mlsanity.com/paper/2104.10891/) (2021)

## Abstract (arXiv)

The COVID-19 pandemic forced governments across the world to impose lockdowns to prevent virus transmissions. This resulted in the shutdown of all economic activity and accordingly the production at manufacturing plants across most sectors was halted. While there is an urgency to resume production, there is an even greater need to ensure the safety of the workforce at the plant site. Reports indicate that maintaining social distancing and wearing face masks while at work clearly reduces the risk of transmission. We decided to use computer vision on CCTV feeds to monitor worker activity and detect violations which trigger real time voice alerts on the shop floor. This paper describes an efficient and economic approach of using AI to create a safe environment in a manufacturing setup. We demonstrate our approach to build a robust social distancing measurement algorithm using a mix of modern-day deep learning and classic projective geometry techniques. We have deployed our solution at manufacturing plants across the Aditya Birla Group (ABG). We have also described our face mask detection approach which provides a high accuracy across a range of customized masks.