PCB Defect Detection Using Deep Learning: A Comparative Performance Analysis

The rapid expansion of computer, communication and consumer sectors has propelled the production of electronic information devices into a new phase of development. Consequently, the market is seeing a rise in demand for Printed Circuit Boards (PCBs). PCB defect detection demands high precision and efficiency to identify critical flaws such as spurious copper, mousebites, pinholes, spurs, open circuits and short circuits and play an important role in manufacturing. This paper evaluates You Only Look Once version 11’s (YOLOv11) performance in detecting defects on Printed Circuit Boards (PCBs) and compares it with YOLOv8 and YOLOv10. YOLOv11 achieved a precision of 98.9