Hydra is a system which utilizes computer vision to monitor data quality in near real time. Currently, it is deployed in all of Jefferson Lab’s experimental halls and lightens the load on shift takers by autonomously monitoring diagnostic plots in near real time. Hydra is constructed from “off-the-shelf” technologies and is backed up by a full MySQL database. To aid with both labeling and monitoring of Hydra’s inference, web apps have been developed, lowering the barrier to entry. Hydra connects with the EPICS alarm system and includes full history recording. This has enabled it to spot issues that were missed by shift takers. When issues are spotted a natural first question is “Why does Hydra think there is a problem?”. To answer this question Hydra uses gradCAM to highlight regions of an image which are most important in inference classification. This provides a level of interpretability/trustworthiness; essential for any operational system in science. This talk will describe the Hydra system, the technologies employed as well as in situ results. The sociological hurdles in deploying such a system will also be discussed.
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