To prevent failures and quality issues caused by welding defects,PCB solder joint inspection has become a critical step in the manufacturing of electronic products.The application of deep learning-based digital X-ray non-destructive testing to examine internal solder joint defects within PCB circuit boards can increase production efficiency while reducing the labor pressure on workers.Three common digital X-ray datasets of PCB solder joint defects were established and an automated detection network model based on RetinaNet was constructed.After training and testing,the model achieved an average detec-tion accuracy of 92.7％for defect images.Experimental results demonstrate that the proposed model effec-tively enhanced the performance and efficiency of PCB solder joint defect detection under X-ray inspection.
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