工厂地面工业信号灯的检测与分类
Detection and Classification of Industrial Signal Lights for Factory Floors
摘要：
在过去的几十年里，工业制造业已经从劳动密集型的机器手动控制发展到完全连接的自动化过程。下一个大的飞跃被称为工业4.0，或智能制造。随着工业4.0的到来，IT系统和工厂车间之间从客户订单系统到最终产品交付的集成度不断提高。这种集成的一个好处是大规模生产个性化定制的产品。然而，考虑到现有工厂的使用寿命可长达30年，这在现有工厂中实施具有挑战性。在工厂中测量的最重要的参数是每台机器的运行时间。运行时间可能会受到机器维护以及不同产品重新配置的影响。对于没有连接的旧机器，运行状态通常由绿色，黄色和红色的信号灯指示。因此，目标是开发一种解决方案，其可以使用来自捕获工厂车间的视频摄像机的输入来测量操作状态。使用自动驾驶汽车中常用的交通灯识别方法，提出了一种在指定条件下准确率超过99%的系统。据信，如果更多样化的视频数据变得可用，则可以使用类似的方法来开发具有高可靠性的系统。
英文摘要：
Industrial manufacturing has developed during the last decades from a labor-intensive manual control of machines to a fully-connected automated process. The next big leap is known as industry 4.0, or smart manufacturing. With industry 4.0 comes increased integration between IT systems and the factory floor from the customer order system to final delivery of the product. One benefit of this integration is mass production of individually customized products. However, this has proven challenging to implement into existing factories, considering that their lifetime can be up to 30 years. The single most important parameter to measure in a factory is the operating hours of each machine. Operating hours can be affected by machine maintenance as well as re-configuration for different products. For older machines without connectivity, the operating state is typically indicated by signal lights of green, yellow and red colours. Accordingly, the goal is to develop a solution which can measure the operational state using the input from a video camera capturing a factory floor. Using methods commonly employed for traffic light recognition in autonomous cars, a system with an accuracy of over 99% in the specified conditions is presented. It is believed that if more diverse video data becomes available, a system with high reliability that generalizes well could be developed using a similar methodology.
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。
