Computed Tomography(CT)is a commonly used technology in Printed Circuit Boards(PCB)non-destructive testing,and element segmentation of CT images is a key subsequent step.With the development of deep learning,researche...Computed Tomography(CT)is a commonly used technology in Printed Circuit Boards(PCB)non-destructive testing,and element segmentation of CT images is a key subsequent step.With the development of deep learning,researchers began to exploit the“pre-training and fine-tuning”training process for multi-element segmentation,reducing the time spent on manual annotation.However,the existing element segmentation model only focuses on the overall accuracy at the pixel level,ignoring whether the element connectivity relationship can be correctly identified.To this end,this paper proposes a PCB CT image element segmentation model optimizing the semantic perception of connectivity relationship(OSPC-seg).The overall training process adopts a“pre-training and fine-tuning”training process.A loss function that optimizes the semantic perception of circuit connectivity relationship(OSPC Loss)is designed from the aspect of alleviating the class imbalance problem and improving the correct connectivity rate.Also,the correct connectivity rate index(CCR)is proposed to evaluate the model’s connectivity relationship recognition capabilities.Experiments show that mIoU and CCR of OSPC-seg on our datasets are 90.1%and 97.0%,improved by 1.5%and 1.6%respectively compared with the baseline model.From visualization results,it can be seen that the segmentation performance of connection positions is significantly improved,which also demonstrates the effectiveness of OSPC-seg.展开更多
A systematic, accurate and robust evaluating method for fine pitch printed circuit board （PCB） positioning assessment in testing fixture is developed. Targeting reliability of bed-of-nails tester is successfully eva...A systematic, accurate and robust evaluating method for fine pitch printed circuit board （PCB） positioning assessment in testing fixture is developed. Targeting reliability of bed-of-nails tester is successfully evaluated by the 2D pattern transform. Probe offset vector with its Weibull and Gaussian distribution estimates are obtained for further investigation about the causes of misalignment on the basis of a batch tests for same kind of PCBs.展开更多
文摘Computed Tomography(CT)is a commonly used technology in Printed Circuit Boards(PCB)non-destructive testing,and element segmentation of CT images is a key subsequent step.With the development of deep learning,researchers began to exploit the“pre-training and fine-tuning”training process for multi-element segmentation,reducing the time spent on manual annotation.However,the existing element segmentation model only focuses on the overall accuracy at the pixel level,ignoring whether the element connectivity relationship can be correctly identified.To this end,this paper proposes a PCB CT image element segmentation model optimizing the semantic perception of connectivity relationship(OSPC-seg).The overall training process adopts a“pre-training and fine-tuning”training process.A loss function that optimizes the semantic perception of circuit connectivity relationship(OSPC Loss)is designed from the aspect of alleviating the class imbalance problem and improving the correct connectivity rate.Also,the correct connectivity rate index(CCR)is proposed to evaluate the model’s connectivity relationship recognition capabilities.Experiments show that mIoU and CCR of OSPC-seg on our datasets are 90.1%and 97.0%,improved by 1.5%and 1.6%respectively compared with the baseline model.From visualization results,it can be seen that the segmentation performance of connection positions is significantly improved,which also demonstrates the effectiveness of OSPC-seg.
基金This project is supported by US Pennsylvania Dept. of Community & Economic Development(No.20-906-0015)National Natural Science Foundation of China(No.50390064, No.50575230)National Basic Research Program of China(973 Program, No.2003CB716202).
文摘A systematic, accurate and robust evaluating method for fine pitch printed circuit board （PCB） positioning assessment in testing fixture is developed. Targeting reliability of bed-of-nails tester is successfully evaluated by the 2D pattern transform. Probe offset vector with its Weibull and Gaussian distribution estimates are obtained for further investigation about the causes of misalignment on the basis of a batch tests for same kind of PCBs.
