At present, PCB welding defect images screened with traditional machine vision analysis methods still need manual reinspection, which is easy to make mistakes after visual fatigue due to heavy workload. In view of this, the study designs and applies the YOLOv3-spp object detection algorithm to build a welding defect detection model. For a higher detection speed, model pruning, model distillation, model quantization and other technologies are used to compress and optimize the detection model. OpenVINO, a deep learning acceleration component, is employed to load the compressed and optimized detection model for the reinspection of PCB welding defect images. With the help of this optimization algorithm, this study designs a PCB welding defect detection and identification system based on deep learning technology. It can quickly and accurately identify welding defects and locate the defects, addressing the low efficiency and high rates of missed detection and false detection caused by manual visual inspection.