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

TL;DR

This paper presents a computer vision-based AI system for real-time monitoring of social distancing and face mask compliance in manufacturing plants to enhance workforce safety post COVID-19.

Contribution

It introduces a robust social distancing measurement algorithm and a high-accuracy face mask detection method deployed in real manufacturing environments.

Findings

Effective social distancing measurement using deep learning and geometry techniques

High accuracy face mask detection across various mask types

Successful deployment in multiple manufacturing plants

Abstract

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.