MSMD-YOLO: Enhanced Printed Circuit Board Defect Detection With a Multi-Scale Merging and Attention Network

The growing demand for flawless printed circuit boards (PCBs) necessitates highly precise and reliable defect detection technologies. However, the tiny size of defects, complex textures, and irregular geometries present major challenges to existing vision-based approaches. To overcome these limitations, this study introduces MSMD-YOLO, an enhanced detection framework developed as a next-generation extension of YOLOv11 [1] for PCB inspection. The proposed Multi-Scale Feature Merging (MSFM) module enables comprehensive fusion of structural information across multiple scales, enriching fine-grained feature representation and detail extraction. In parallel, the MDA-Attention module integrates channel-wise and spatial attention mechanisms with dilated convolutions [2], significantly improving semantic discrimination and spatial sensitivity while maintaining low computational complexity. Additionally, the Weighted <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IoU</i>(<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">WIoU</i>) [3] loss introduces adaptive attention that prioritizes medium-quality anchors and enhances localization of small or low-quality defects. Experimental evaluation on a challenging PCB defect dataset demonstrates that MSMD-YOLO achieves a mean average precision (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mAP</i>) [4] of 92.9%, outperforming YOLOv11 by 5.0 percentage points. The model effectively minimizes false detections and missed defects, confirming its robustness and reliability as a practical solution for high-accuracy industrial inspection and automated quality control in advanced PCB manufacturing environments.