SDD-Net: Soldering defect detection network for printed circuit boards

Abstract

The rapid detection of soldering defects in printed circuit boards (PCBs) is crucial and a challenge for quality control. Thus, a novel soldering defect detection network (SDD-Net) is proposed based on improvements in YOLOv7-tiny. A fast spatial pyramid pooling block integrating a cross-stage partial network is designed to expand the receptive field and feature extraction ability of the model. A hybrid combination attention mechanism is proposed to boost feature representation. A residual feature pyramid network is subsequently presented to reinforce the capability of multilevel feature fusion to overcome the scale variance issue in PCB soldering defects. Finally, efficient intersection over union loss is applied for bounding box regression to accelerate model convergence while improving localisation precision. SDD-Net achieves a stunning mean average precision of 99.1% on the dataset, producing a 1.8% increase compared with the baseline. The detection speed is boosted to 102 frames/s for input images of 640 × 640 pixels using a mediocre processor. In addition, SDD-Net exhibits outstanding generalisation ability in two public surface defect datasets.