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Creators/Authors contains: "Kennedy, Ken"

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  1. ; ; ; ; (, 2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing)
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  2. ; ; ; ; ; ; ; (, 2018 IEEE international conference on Big Data (Big Data))
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  3. ; ; ; ; ; ; ; (, Software: Practice and Experience)
    Summary 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. 
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