Abstract
In critical fields such as automotive, medicine, and defense, ensuring the reliability of microelectronics has been paramount given the extensive nature of their globalized supply chain. Automated visual inspection (AVI) of printed circuit boards (PCBs) offers a solution through computer vision and deep learning to automate defect detection, component verification, and quality assurance. In this paper, our research follows this precedent by introducing a novel dataset and annotations to train artificial intelligence (AI) models for extracting PCB connectivity components. Utilizing high-resolution images, and state-of-the-art instance segmentation models, this study aims to examine the difficulties in this implementation and lay the groundwork for more robust automated visual inspection.
