Contacts Detection and Machine Learning for Classification of Defects in PCB Images

The presented work considers three approaches for detecting the coordinates of contacts in printed circuit boards (PCBs). The objects of the approaches are contacts with holes, contacts without holes, and mixed contacts. The central procedure of the method is the flood-filling algorithm, which detects the component edges simultaneously. Overlaying the found coordinates of contacts with the standard PCB creates an image, which allows the determination of features characterizing defects, such as connectivity, redundant or missing components. Machine learning based on these identified image features is applied to classify printed circuit boards. Artificial neural networks (ANNs), which are available on the Internet, were used in the work. The developed approach combines two components: software for image processing and Machine Learning.