Changes

This Technical Note (TN for short) belongs to [[ML-TN-003 — AI at the edge: visual inspection of assembled PCBs for defect detection — Part 1|this series of articles]]dealing with the automatic visual inspection of Printed Circuit Boards (PCB-AVI). Specifically, it this article illustrates the attempt of employing ML and image processing techniques in the field of automatic visual inspection of Printed Circuit Boards (PCB-AVI) for building a proprietary dataset of SMD-populated PCBs exhibiting mounting anomalies. This  Detecting anomalies on an assembled PCB is a real-world use case is affected by the typical problem ML engineers and data scientists need to face when it comes to the detection of spotting defects/anomalies related to an industrial process: these defects/anomalies they are extremely rare! As a result, in general, no large datasets of samples are available to train the models and several tools have to be employed to augment available data. This is pretty challenging as described in the following chapters.