在后COVID世界中使用计算机视觉提高制造业劳动力的安全性
Using Computer Vision to enhance Safety of Workforce in Manufacturing in a Post COVID World
摘要：
COVID-19疫情迫使世界各国政府实施封锁，以防止病毒传播。这导致所有经济活动停止，因此大多数部门的制造厂停止生产。虽然恢复生产迫在眉睫，但更需要确保工厂现场工人的安全。报告显示，在工作时保持社交距离和戴口罩明显降低了传播风险。我们决定在闭路电视上使用计算机视觉来监控工人的活动，并检测违规行为，从而在车间触发实时的语音警报。本文描述了一种有效且经济的方法，使用AI在制造环境中创建安全的环境。我们展示了我们的方法来建立一个强大的社会距离测量算法，使用现代的深度学习和经典的投影几何技术的混合。我们已经在Aditya Birla Group（ABG）的制造工厂部署了我们的解决方案。我们还描述了我们的面罩检测方法，该方法在一系列定制面罩中提供了高精度。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
