With the continuous development of the electronics industry in recent years, electronic products, as a ubiquitous consumer product, have been updated and replaced more frequently. They are becoming lighter, thinner, and more convenient electronic products, and the requirements for production capacity and product quality are gradually increasing. As the core of electronic products, printed circuit boards (PCBs) possess high integration and density, while also requiring high efficiency and quality in production. Any small error in the process of PCB welding and assembly can greatly affect the normal use of electronic products. In this environment, traditional manual inspection cannot meet the increasing demand due to its slow detection speed and easy omission. Therefore, this article will study the defect detection process in PCB assembly and welding based on computer-aided technology. Enhance the PCB public dataset through BigGAN network, use computer data models to detect defect locations and identify types in the bare board dataset, and verify the effectiveness and accuracy of this detection through experiments.