| dc.description.abstract | Printed circuit board is a basic component of various electronic products, a structural component formed by insulating materials supplemented by conductor wiring. Mainly used to carry electronic components, using the electronic circuit formed by the circuit board to connect various electronic components together, as a bridge for communication between circuits; widely used in aerospace military, precision instruments, computers, communications, various industrial products , and different consumer electronics products.
The quality of printed circuit boards deeply affects the performance of various electronic products. Only good quality printed circuit boards can maintain excellent electronic products. There will always be a small amount of abnormalities in the manufacture of any product; these abnormal flaws must be detected in order to deliver good-quality printed circuit boards to downstream manufacturers to continue to manufacture excellent electronic products. The defect detection of traditional automated optical inspection is easily affected by the complexity of the light source and the printed circuit board itself, and cannot effectively improve the detection accuracy. In recent years, deep learning technology has risen, and it has outstanding performance in all walks of life. Naturally, automatic optical inspection does not fall behind, and actively introduces deep learning technology in order to simultaneously improve the detection rate and screening rate of inspection.
In this paper, we propose a defect detection system for printed circuit boards based on a capsule network. The first part is the original pure capsule network. We discuss the design of the capsule network and analyze the key components; for example, dynamic routing algorithm, squash function, primary capsule. In this direction to optimize the original network architecture, the second part is the modification of the convolution module. We reduced the convolution layer of the original capsule network to compare the original network architecture. The third part is the expansion model, we discuss the impact of the expansion of the convolution layer and capsule layer on the performance of the model, the fourth part is the combination of depth convolution and capsule, we discuss the different types of depth convolution as extraction features applicability; for example, Inception, DenseNet, ResNet, VGGNet, and MobileNet, based on this as an architecture combined with the capsule layer, the final improved version is proposed.
In terms of experiments, in the stage of the original pure capsule network, we adjusted the squeeze function, dynamic routing algorithm, and the dimension of the primary capsule to verify the real changes of the modification to the original capsule network. The test results were consistent with the original capsule. Compared with the network, the accuracy, precision, and recall rate have increased by 1.86%, 1.87%, and 1.86% respectively. In the stage of modifying the convolution module, we combined the capsule layer with a simple convolution layer. The test results are consistent with the original capsule. Compared with the network, the accuracy, precision, and recall rate increased by 8.15%, 8.08%, and 7.99% respectively. In the expansion model stage, we deepened the convolution layer and capsule layer. The test results were compared with the original capsule network. The accuracy is better than 10.49%, the precision is better than 9.58%, and the recall rate is better than 10.58%. The depth convolution is combined with the capsule stage. After comparing the performance of different depth convolutions, we finally choose DenseNet as the depth convolution and capsule layer. In combination, the routing number of the last capsule layer was changed from 3 to 7, and the epoch was increased from 100 to 350, the optimizer Adam was changed to AdamW, and ReduceLROnPlateau was used as the learning rate strategy, and the final accuracy, precision, recall rate, and F-score all reached 99.22%. | en_US |