YOLO-WWBi: An Optimized YOLO11 Algorithm for PCB Defect Detection

The manufacturing quality of printed circuit boards (PCBs) significantly influences the functionality and life expectancy of electronic devices. This paper introduces a YOLO-WWBi based on improved YOLO11 framework method for the detection of surface defects. First, an improved weighted and re-parameterized ghost multi-scale feature aggregation module (WRGMSFA) is designed. This module focuses more on defect information channels, enhancing multi-scale feature extraction capabilities while suppressing redundant information. Then, BiFPN is integrated into the neck to enhance the quality of fused features and deepen the interaction of feature information. Finally, the WIoU loss function was employed to optimize the localization performance of defect positions, thereby enhancing robustness in highly similar PCB background interference. The experimental results indicate that YOLO-WWBi has an mAP of 96.6%, surpassing YOLO11 by 5.4 points. Its performance metrics indicate that the requirements for the high-precision, real-time detection of PCB defects are satisfactorily met.