论文标题
利用计算机愿景来增强在邮政邮政世界中制造业中劳动力的安全性
Using Computer Vision to enhance Safety of Workforce in Manufacturing in a Post COVID World
论文作者
论文摘要
联盟19日大流行迫使世界各地的政府施加锁定,以防止病毒传播。这导致了所有经济活动的关闭,因此大多数行业的制造厂生产都停止了。尽管有恢复生产的紧迫性,但还需要更需要确保工厂现场的劳动力安全。报告表明,在工作时保持社交距离并戴口罩清楚地降低了传播的风险。我们决定在CCTV供稿上使用计算机视觉来监视工人活动并检测违规行为,从而触发商店地板上的实时语音警报。本文描述了一种在制造设置中使用AI创建安全环境的有效和经济方法。我们展示了我们使用现代深度学习和经典的投射几何技术的混合物来构建强大的社会距离测量算法的方法。我们已经在Aditya Birla集团(ABG)的制造工厂中部署了解决方案。我们还描述了我们的面罩检测方法,该方法在一系列定制的面罩上提供了很高的精度。
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.