  Recent trends in AIGC 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.
