Printed Circuit Board Sample Expansion and Automatic Defect Detection Based on Diffusion Models and ConvNeXt

Soldering of printed circuit board (PCB)-based surface-mounted assemblies is a critical process, and to enhance the accuracy of detecting their multi-targeted soldering defects, we propose an automated sample generation method that combines ControlNet and a Stable Diffusion Model. This method can expand the dataset by quickly obtaining sample images with high quality containing both defects and normal detection targets. Meanwhile, we propose the Cascade Mask R-CNN model with ConvNeXt as the backbone, which performs well in dealing with multi-target defect detection tasks. Unlike previous detection methods that can only detect a single component, it can detect all components in the region. The results of the experiment demonstrate that the detection accuracy of our proposed approach is significantly enhanced over the previous convolutional neural network model, with an increase of more than 10.5% in the mean accuracy precision (mAP) and 9.5% in the average recall (AR).