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Deployment of a cloud pipeline for real‐time visual inspection using fast streaming high‐definition images
Author(s) -
Srivastava Aishwarya,
Aggarwal Siddhant,
Apon Amy,
Duffy Edward,
Kennedy Ken,
Luckow Andre,
Posey Brandon,
Ziolkowski Marcin
Publication year - 2020
Publication title -
software: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.437
H-Index - 70
eISSN - 1097-024X
pISSN - 0038-0644
DOI - 10.1002/spe.2816
Subject(s) - computer science , cloud computing , pipeline (software) , automotive industry , real time computing , frame (networking) , scalability , software deployment , visual inspection , visualization , process (computing) , set (abstract data type) , fault detection and isolation , embedded system , artificial intelligence , engineering , operating system , computer network , actuator , programming language , aerospace engineering
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.