Abstract
A method of automating the visual inspection of ATLAS upgrade strip modules is shown. The visual inspection of the hybrids is a time consuming part of the quality control during module production. A method of detecting and classifying the SMD components on the hybrids using an object detection neural network was investigated. The results show that the amount of hybrids that needed to be check by a human operator was reduced to around 10% of the batch. This hugely reduced the amount of time needed for human inspection and did find real mistakes done during the production of the hybrids.
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