车间工业信号灯的检测与分类

Felix Nilsson, J. Jakobsen, F. Alonso-Fernandez
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引用次数: 4

摘要

在过去的几十年里,工业制造已经从劳动密集型的人工控制机器发展到完全连接的自动化过程。下一个重大飞跃被称为工业4.0,或智能制造。随着工业4.0的到来,从客户订单系统到产品的最终交付,IT系统和工厂车间之间的集成得到了加强。这种集成的一个好处是可以批量生产个性化定制的产品。然而,考虑到现有工厂的使用寿命长达30年,这已被证明是具有挑战性的。在工厂中测量的最重要的一个参数是每台机器的工作时间。操作时间可能会受到机器维护以及不同产品的重新配置的影响。对于没有连接的旧机器,运行状态通常由绿、黄、红三色信号灯指示。因此,目标是开发一种解决方案,该解决方案可以使用摄像机捕捉工厂车间的输入来测量操作状态。利用自动驾驶汽车中常用的红绿灯识别方法,提出了一个在特定条件下准确率超过99%的系统。人们相信,如果有更多不同的视频数据可用,可以使用类似的方法开发出具有高可靠性的系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Detection and Classification of Industrial Signal Lights for Factory Floors
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.
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