[论文解读] Using Computer Vision to enhance Safety of Workforce in Manufacturing in a Post COVID World
本文提出一个计算机视觉系统，利用 CCTV 摄像头实时监控来强制执行制造厂的社交距离和戴口罩规定，提供实时语音警报，并在 Aditya Birla Group 设施中部署该解决方案。
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.
研究动机与目标
- 通过在车间降低 COVID-19 传播风险，推动安全的制造业复产。
- 利用现有 CCTV 基础设施探索经济实惠、可扩展的 AI 驱动监控。
- 开发适用于各种口罩和环境的社交距离测量与口罩检测的鲁棒方法。
提出的方法
- 开发结合深度学习与经典投影几何的社交距离测量算法。
- 实现在多样定制口罩下具有高精度的口罩检测模块。
- 在车间集成实时语音警报以强制执行合规。
- 利用现有 CCTV 摄像头实现制造厂的成本效益部署。
实验结果
研究问题
- RQ1计算机视觉是否能够在制造环境中使用 CCTV 数据可靠地测量和监控社交距离？
- RQ2在现实条件下，口罩检测系统对多种定制口罩的识别有多高的准确性？
- RQ3车间的实时语音警报是否提升对安全规程的遵守？
- RQ4所提议的方法是否可扩展且在多家工厂部署经济？
- RQ5在后疫情工业环境中部署基于 CV 的安全系统会带来哪些实际考量？
主要发现
- 展示了一种将前沿深度学习与投影几何技术融合的鲁棒社交距离测量算法。
- 在多种定制口罩条件下实现了高精度的口罩检测。
- 将该解决方案部署在 Aditya Birla Group 的制造工厂中，展示了其实用性和可扩展性。
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