Solder Joint Defect Inspection Method Based on ConvNeXt-YOLOX

Abstract

ConvNeXt-YOLOX model, an improved deep learning algorithm based on the YOLOX neural network, is proposed for surface mount technology (SMT) production line printed circuit board assembly (PCBA) solder joint defect detection with high accuracy and speed. The improved model employs the classification neural network ConvNeXt to enhance the feature extraction capability of the YOLOX backbone feature extraction network, thus enhancing the target detection accuracy and speed as well as the small target detection capability while maintaining the same number of parameters. A solder joint defect detection dataset containing 759 PCBA solder joint defect images is constructed, on which the improved model ConvNeXt-YOLOX, the original model YOLOX, and the lightweight model YOLOX-s are trained. Subsequently, the training results of the three models are compared. The comparison shows that the mean average precision (mAP) of the improved model ConvNeXt-YOLOX is 97.21%, which is 0.82% and 3.02% higher than that of YOLOX and YOLOX-s, respectively, while the mAP at (0.5:0.95) is increased from 76.3 to 77.5. Moreover, the detection speed is increased from the original 27.06 to 27.88 frames/s. In summary, the improved model ConvNeXt-YOLOX has strong small target feature extraction and detection capabilities, which are consistent with the actual requirements of solder joint defect detection.