Recent trends in cloud computing technology effectively boosted the application of visual inspection. However, most of the available systems work in a human-in-the-loop manner and can not provide long-term support to the online application. To make a step forward, this paper outlines an automatic annotation system called SsaA, working in a self-supervised learning manner, for continuously making the online visual inspection in the manufacturing automation scenarios. Benefit from the self-supervised learning, SsaA is effective to establish a visual inspection application for the whole life-cycle of manufacturing. In the early stage, with only the anomaly-free data, the unsupervised algorithms are adopted to process the pretext task and generate coarse labels for the following data. Then supervised algorithms are trained for the downstream task. With user-friendly web-based interfaces, SsaA is very convenient to integrate and deploy both of the unsupervised and supervised algorithms. So far, the SsaA system has been adopted for some real-life industrial applications.
翻译：云计算技术的近期趋势有效地促进了视觉检查的应用,然而,大多数可用的系统都是以人到流的方式工作,无法为在线应用提供长期支持。为了向前迈出一步,本文件概述了一个称为SsaA的自动批注系统,该系统以自我监督的学习方式工作,在制造自动化情景中持续进行在线视觉检查。从自监督的学习中受益,SsaA能够有效地为整个制造周期建立一个视觉检查应用程序。在早期,只有无异常数据,采用无监督的算法来处理托辞任务,并为随后的数据制作粗略标签。随后,监督的算法为下游任务提供培训。利用方便用户的网络界面,SsaA非常方便整合和部署不受监督和监督的算法。迄今为止,SsaA系统已被采用用于一些真实的工业应用。
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