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→Defects generation and acquisition
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*1 panel uses the normal amount of solder, but shifts along x and y axes are applied too.
After completing the assembly of all 5 panels, a visual inspection was performed with a traditional AOI machine(*). A total amount of '''832 anomalies''' were found and the corresponding unmarked images were saved for dataset building. (*) This machine makes use of an RGB color lighting technique to emphasize anomalies of solder joints.
===Class subdivision and labelling===
*'''Missing''': the component is not in place, hence only the pads with applied solder are visible.
*'''Tombstoning''': the component is lifted from a pad of the PCB; this class also comprehends all the cases for which the component is lifted and rotated by a certain amount.
*'''Under soldering''': the component is soldered on both pads, but the amount of solder is too low. By looking at inspecting the picture of the reported component, this is clearly visible when on a pad or both there is a higher amount of red with respect to the blue one.
To simplify the problem, all the classes into which the images are divided are mutually exclusive. ''manhattanManhattan'' and ''over soldering'' defect typologies are no longer included among the possible classes because their generation is too difficult hence the quantity of examples obtained is too low for training a model.
[[File:Samples of defects divided by classes.png|center|thumb|500x500px|Examples of four types of defects]]
===Soldering regions extraction===
By looking at analyzing the acquired images, it is interesting to note that most defect features are distributed in the solder region in the component and in . In particular , two defects belonging to two different classes can be easily distinguished by looking at the two soldering regions. This means that both can be potentially used for training a ML model for a classification problem, while all the other parts in the image can be discarded without losing information and accuracy.
To address this issue a methodology was developed for extracting the soldering regions from all the collected images. The algorithm is designed to find the correct position of a rectangular window i.e. a region of interest (ROI) by employing an adaptive approach that uses OpenCV image processing functions. Note that this approach can be used for all the different classes of defects.
