Application of Mask R-CNN and YOLOv8 algorithms for defect detection in printed circuit board manufacturing

In the last decades, machine vision and Machine Learning (ML) techniques have seen significant improvements in developing new algorithms thanks to the increment of hardware performance. Exploiting machine vision for specific technological applications became an essential opportunity to introduce significant improvements in the manufacturing context. This paper proposes a study to analyze the ML capabilities to perform Automated Optical Inspection (AOI) for quality control in the manufacturing of Printed Circuit Boards (PCBs). The study has been performed by testing Mask R-CNN and YOLOv8 algorithms and an open-source PCB dataset designed to evaluate other ML techniques. The chosen open-source dataset (i.e. PCB defect dataset released by Open Lab on Human–Robot Interaction of Peking University, HRIPCB) individuates appropriate classes of products and related defects for the context of interest, resulting in a suitable dataset for the performance evaluation of tested algorithms. The challenge of this specific application is the recognition of the component boundaries that have submillimetric dimensions and are not clearly identifiable. The comparison between Mask R-CNN and YOLOv8 highlights that the Mask R-CNN performs better in defect detection (i.e., Missing Holes and Shorts). In particular, for the missing hole defects, for example, the mAP50-95 is 0.798 for Mask R-CNN and 0.261 for YOLOv8. Instead, for the short defects, mAP50-95 is 0.519 for Mask R-CNN and 0.399 for YOLOv8. This work has been carried out to gather know-how for further activity related to AOI for quality control in the PCB assembly employed in the aerospace field.