Defect Detection in Printed Circuit Boards: A Comparative Analysis of Object Detection Models with Depthwise Convolution Adaptation

Abstract

Printed circuit boards (PCBs) are key components in the electronics industry, and ensuring their integrity is essential for reliable manufacturing. Automated inspection systems based on computer vision, although efficient, face challenges. In this scenario, deep learning techniques have become effective solutions for detecting defects in more modern and complex PCBs. This article presents a comparative study between the YOLOv8n, YOLOv11n and RT-DETRv2 models for identifying defects in PCBs. The experiments were conducted using the PKU-Market-PCB dataset, which includes Missing Hole, Mouse Bite, Open Circuit, Short Circuit, Spur and Spurious Copper defects. To reduce the computational cost, modified versions of YOLOv8n and YOLOv11n with Depthwise convolution blocks (YOLOv8-DWConv and YOLOv11-DWConv). The analysis includes quantitative and qualitative comparisons. In addition, the robustness of the models is evaluated under challenging conditions with blur and illumination gradient noise. The results indicate that YOLOv11n achieves the best overall performance, while YOLOv11n-DWConv offers a competitive balance between precision and computational efficiency.