We investigate the challenges of building an end‐to‐end cloud pipeline for real‐time intelligent visual inspection system for use in automotive manufacturing. Current methods of visual detection in automotive assembly are highly labor intensive, and thus prone to errors. An automated process is sought that can operate within the real‐time constraints of the assembly line and can reduce errors. Components of the cloud pipeline include capture of a large set of high‐definition images from a camera setup at the assembly location, transfer and storage of the images as needed, execution of object detection, and notification to a human operator when a fault is detected. The end‐to‐end execution must complete within a fixed time frame before the next car arrives in the assembly line. In this article, we report the design, development, and experimental evaluation of the tradeoffs of performance, accuracy, and scalability for a cloud system.

中文翻译：

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使用快速流高清图像部署用于实时视觉检查的云管道

我们调查了为用于汽车制造的实时智能视觉检测系统构建端到端云管道的挑战。当前汽车装配中的视觉检测方法是高度劳动密集型的，因此容易出错。寻求一种能够在装配线的实时约束内运行并能够减少错误的自动化流程。云管道的组件包括从装配位置的摄像头设置捕获大量高清图像、根据需要传输和存储图像、执行对象检测以及在检测到故障时通知操作员. 端到端的执行必须在下一辆车到达装配线之前的固定时间范围内完成。在本文中，我们报告了设计、开发、