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

• URL: http://arxiv.org/abs/2005.05287v2
• Date: Mon, 25 May 2020 12:16:12 GMT
• Title: Using Computer Vision to enhance Safety of Workforce in Manufacturing in a Post COVID World
• Authors: Prateek Khandelwal, Anuj Khandelwal, Snigdha Agarwal, Deep Thomas, Naveen Xavier, Arun Raghuraman (for Group Data and Analytics, Aditya Birla Group)
• Abstract summary: The COVID-19 pandemic forced governments across the world to impose lockdowns to prevent virus transmissions. 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.
• Score: 29.52028845462248
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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