Changes

By analyzing the acquired images, it is interesting to note that most defect features are distributed in the solder region in the component. In particular, two defects belonging to any two different classes can be easily distinguished by looking at the two soldering regionsonly. This means that both regions 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.